DETAILED ACTION
This Office action is in response to the Application filed on April 5, 2024, which is a national stage application under 35 U.S.C. §371 of International Application No. PCT/AU2022/051206, filed on October 7, 2022, and claims foreign priority to AU Application Nos. 2021903222, 2021903223, and 2021903224, filed October 7, 2021, respectively. Claims 8-12 and 14-15 have been amended via preliminary amendment. An action on the merits follows. Claims 1-17 are pending on the application.
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 .
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.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains.
The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claims 2… are objected to because of the following informalities:
Claim 2 recites the limitation “A method according to claim 1 wherein the captured visual data includes a plurality of video frames, and the constructed biomechanical model is based on at least one frame capturing the subject in a predefined stance” in lines 1-4 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 2 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example. Additionally, it is not clear if the claimed “at least one frame” recited in lines 3-4 of claim 2 corresponds to “at least one frame” of the claimed “plurality of video frames” previously recited in lines 1-2 of claim 2, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 1 wherein the captured visual data includes a plurality of video frames, and the constructed biomechanical model is based on at least one frame capturing the subject in a predefined stance” in lines 1-4 of claim 2 will be interpreted as “The method according to claim 1 wherein the captured visual data includes a plurality of video frames, and the constructed biomechanical model is based on at least one frame of the plurality of video frames capturing the subject in a predefined stance”.
Claim 3 recites the limitation “A method according to claim 2” in line 1 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 3 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 2” in line 1 of claim 3 will be interpreted as “The method according to claim 2”.
Claim 4 recites the limitation “A method according to claim 1” in line 1 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 4 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 1” in line 1 of claim 4 will be interpreted as “The method according to claim 1”.
Claim 5 recites the limitation “A method according to claim 1” in line 1 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 5 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 1” in line 1 of claim 5 will be interpreted as “The method according to claim 1”.
Claim 6 recites the limitation “A method according to claim 5” in line 1 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 6 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 5” in line 1 of claim 6 will be interpreted as “The method according to claim 5”.
Claim 7 recites the limitation “A method according to claim 5 wherein the camera is an IP camera” in line 1 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 6 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example. Additionally, the acronym “IP” recited in line 1 of claim 7 is undefined in the claims. To clarify that the acronym means internet protocol, examiner suggests amending the claimed “an IP camera” recited in line 1 of claim 7 to “an internet protocol (IP) camera”.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 5 wherein the camera is an IP camera” in line 1 of claim 7 will be interpreted as “The method according to claim 5 wherein the camera is an internet protocol (IP) camera”.
Claim 8 recites the limitation “A method according to claim 1” in line 1 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 8 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 1” in line 1 of claim 8 will be interpreted as “The method according to claim 1”.
Claim 9 recites the limitation “A method according to claim 1 wherein the visual data of a subject is captured” in lines 1-2 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 9 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example. Additionally, it is not clear if the claimed “a subject” recited in line 2 of claim 9 corresponds to the claimed “subject” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 1 wherein the visual data of a subject is captured” in lines 1-2 of claim 9 will be interpreted as “The method according to claim 1 wherein the visual data of the subject is capture”.
Claim 10 recites the limitation “A method according to claim 1” in line 1 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 10 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 1” in line 1 of claim 10 will be interpreted as “The method according to claim 1”.
Claim 11 recites the limitation “A method according to claim 1” in line 1 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 11 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 1” in line 1 of claim 11 will be interpreted as “The method according to claim 1”.
Claim 12 recites the limitation “A method according to claim 1 including the further step of outputting the motion performance metric” in lines 1-2 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 12 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example. Additionally, the claimed “motion performance metric” recited in line 2 of claim 12 was previously recited in line 3 of claim 11.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 1 including the further step of outputting the motion performance metric” in lines 1-2 of claim 12 will be interpreted as “The method according to claim 11 including the further step of outputting the motion performance metric”.
Claim 14 recites the limitation “A method according to claim 1” in line 1 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 14 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 1” in line 1 of claim 14 will be interpreted as “The method according to claim 1”.
Claim 15 recites the limitation “A method according to claim 1” in line 1 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 15 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method according to claim 1” in line 1 of claim 15 will be interpreted as “The method according to claim 1”.
Claim 16 recites the limitation “A method for providing a visual comparison of a first biomechanical model and a second biomechanical model, the method including the steps of: generating the first and second biomechanical models according to the method of claim 1” in lines 1-4 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 16 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method for providing a visual comparison of a first biomechanical model and a second biomechanical model, the method including the steps of: generating the first and second biomechanical models according to the method of claim 1” in lines 1-4 of claim 16 will be interpreted as “The method according to claim 1, the method including the steps of: providing a visual comparison of a first biomechanical model and a second biomechanical model; generating the first and second biomechanical models”.
Claim 17 recites the limitation “A method for identifying a current subject based on a predefined limb length ratio of a known subject, the method including the steps of: generating a biomechanical model of the current subject in motion according to the method of claim 1” in lines 1-4 of the claim. However, it is not clear if the claimed “[a] method” recited in line 1 of claim 17 corresponds to the claimed “[a] method” recited in line of claim 1, or not, for example.
Therefore, based on above, for examination purposes, the claimed “A method for identifying a current subject based on a predefined limb length ratio of a known subject, the method including the steps of: generating a biomechanical model of the current subject in motion according to the method of claim 1” in lines 1-4 of claim 16 will be interpreted as “The method according to claim 1, the method including the steps of: identifying a current subject based on a predefined limb length ratio of a known subject; generating a biomechanical model of the current subject in motion”.
Appropriate correction is required.
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. Such claim limitation(s) are “capturing, by a… device… visual data” and “a… device configured to capture… visual data”: in claims 1 and 13, respectively.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. For example, Fig. 1, No. 112, Fig. 22, Nos. 112 and 2210, and Par. [0020-21, 44-45, 68-69, 107, 117, 123, 136-139, 144, 147-152, 422] describe a camera configured to capture visual data and controlled by a programmed computing device or computer, including for example software, hardware, or a combination of hardware and software, capable of performing the described functionality.
If applicant does not intend to have 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 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 sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 15-17 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 15 recites the limitation “visual data of the at least two subjects… recognising human pose points on the subject… recognising a plurality of predefined anatomical components of the subject to construct the biomechanical model of the subject” in lines 4-14 of the claim. However, there is insufficient antecedent basis for the claimed “the at least two subjects” recited in line 4 of the claim. Therefore, the lack of antecedent basis makes the scope of the claim indeterminate. Additionally, it is not clear if the claimed “the subject” recited in lines 10-14 of claim 15 encompass embodiments corresponding to the claimed “each of the at last two subjects” recited in line 7 of claim 15, or if the claimed “the subject” recited in lines 10-14 of claim 15 encompass embodiments corresponding to the claimed “subject” recited in line 1 of claim 1, for example. Furthermore, it is not clear if the claimed “a plurality of predefined anatomical components of the subject” recited in lines 12-13 of claim 15 encompass embodiments corresponding to the claimed “plurality of predefined anatomical components of the subject” recited in lines 8-9 of claim 1, or if the claimed “a plurality of predefined anatomical components of the subject” recited in lines 12-13 of claim 15 encompass embodiments corresponding to another “plurality of predefined anatomical components of the subject” different from the claimed “plurality of predefined anatomical components of the subject” recited in lines 8-9 of claim 1, for example. Therefore, the metes and bounds of the claim are not clearly set forth and the examiner cannot clearly determine which elements are encompassed by the claim language, which renders the claim indefinite.
Claim 16 recites the limitation “a visual comparison of a first biomechanical model and a second biomechanical model” in lines 1-2 of the claim. However, it is not clear if any one of the claimed “first biomechanical model” or “second biomechanical model” in lines 1-2 of claim 16, respectively, encompass embodiments corresponding to the claimed “biomechanical model of a subject in motion” previously recited in line 1 of claim 1, or if any one of the claimed “first biomechanical model” or “second biomechanical model” in lines 1-2 of claim 16, respectively, encompass embodiments corresponding to a “first biomechanical model” and a “second biomechanical model” different from the claimed “biomechanical model of a subject in motion” previously recited in line 1 of claim 1, for example. Therefore, the metes and bounds of the claim are not clearly set forth and the examiner cannot clearly determine which elements are encompassed by the claim language, which renders the claim indefinite.
Claim 17 recites the limitation “identifying a current subject based on a predefined limb length ratio of a known subject… the plurality of predefined anatomical components of the current subject… a first subject limb and a second subject limb of the subject” in lines 1-5 of the claim. However, it is not clear if any one of the claimed “current subject” or “known subject” in lines 1-2 of claim 17, respectively, encompass embodiments corresponding to the claimed “subject” previously recited in line 1 of claim 1, or if any one of the claimed “current subject” or “known subject” in lines 1-2 of claim 17, respectively, encompass embodiments corresponding to a “current subject” and a “known subject” different from the claimed “biomechanical model of a subject in motion” previously recited in line 1 of claim 1, for example. Therefore, the metes and bounds of the claim are not clearly set forth and the examiner cannot clearly determine which elements are encompassed by the claim language, which renders the claim indefinite. Additionally, there is insufficient antecedent basis for the claimed “the plurality of predefined anatomical components of the current subject” recited in lines 4-5 of claim 17. Therefore, the lack of antecedent basis makes the scope of the claim indeterminate. Furthermore, it is not clear if the claimed “the subject” recited in line 5 of claim 17 encompass embodiments corresponding to the claimed “current subject” or “known subject” in lines 1-2 of claim 17, or if the claimed “the subject” recited in line 5 of claim 17 encompass embodiments corresponding to the claimed “subject” in line 1 of claim 1, for example. Therefore, the metes and bounds of the claim are not clearly set forth and the examiner cannot clearly determine which elements are encompassed by the claim language, which renders the claim indefinite.
Claim Rejections - 35 USC § 103
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.
Claims 1-2, 4-6, 9, and 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Martinetz et al. (US PG Publication No. 2014/0328519 A1), hereafter referred to as Martinetz, in view of Kord et al. (US PG Publication No. 2015/0097937 A1), hereafter referred to as Kord, Applicant cited prior art furnished via IDS, in further in view of McGibbon et al. (US PG Publication No. 2002/0009222 A1), hereafter referred to as McGibbon.
Regarding claim 1, Martinetz discloses a method for generating a model of a subject in motion (Par. [0001-5]: method and an apparatus for real time-capable analysis of a sequence of electronic images for estimating the positions and orientations of a movable object captured in the image sequence, i.e. estimating the pose of the movable object… apparatus mentioned for example provides the 3D coordinates of predetermined body parts that change over time--during the motion; Par. [0024-26]: pose estimation by means of SOM training is proposed that is able to work with a skeleton model that is modelled according to the anatomy of the object observed… the anatomy of the object whose pose is to be detected is modelled as a skeleton model… A skeleton model that is described only by a small number of points ("nodes" below) in the 3D space represents a good information compression of the image information if the coordinates of the nodes at all times describe the position of predetermined parts of the moving object… a method for pose estimation of a moving object (e.g. a person or a robot) by computer calculation of displacements of 3D position coordinates of the nodes of a skeleton model, that is continuously fitted into a sequence of 3D point clouds. The node coordinates are present in table form in an electronic memory and the 3D point clouds are determined from electronically recorded images from a depth sensor camera that represent the moving person; Par. [0116-120]: exemplary embodiment for the inventive pose estimation method with the skeleton model from FIG. 1 b) is presented in FIG. 4 using exemplary images from video sequences. The video images of a depth sensor camera constantly provide 3D point clouds that represent a moving person, using image segmentation that is known per se… A depth sensor camera that comprises an arithmetic unit for determining the distance coordinate can in particular also be engineered directly to carry out the inventive method. Such a camera as a constructional unit having at least one arithmetic unit designed according to the invention is correspondingly suited to directly translate the image of a moving person into 3D coordinates of his essential body parts; a method for generating a model of a subject in motion (e.g. method for pose estimation of a moving object (i.e. a subject), such as a person, for example, includes three dimensional (3D) point clouds that are determined (i.e. generated, calculated, computed, estimated, etc.) by computer calculation of displacements (i.e. movements) of 3D position coordinates of nodes of a skeleton model (i.e. a model) that is modelled according to an anatomy of an object observed, including 3D point clouds that represent a moving person (i.e. a method for generating a model of a subject in motion), as indicated above), for example) including the steps of:
capturing, by a single motion capture device, visual data of the subject as it moves in a field of vision of the motion capture device (Par. [0001-5]: method and an apparatus for real time-capable analysis of a sequence of electronic images for estimating the positions and orientations of a movable object captured in the image sequence, i.e. estimating the pose of the movable object… If the motion of the person is imaged by an image sequence, the apparatus mentioned for example provides the 3D coordinates of predetermined body parts that change over time--during the motion; Par. [0024-53]: the anatomy of the object whose pose is to be detected is modelled as a skeleton model… A skeleton model that is described only by a small number of points ("nodes" below) in the 3D space represents a good information compression of the image information if the coordinates of the nodes at all times describe the position of predetermined parts of the moving object… The skeleton model is to be fitted fast and precisely into the image information that corresponds to the object. The fitting is effected between two images of an image sequence by continuously displacing the nodes and updating the skeleton model in step with the image sequence… a method for pose estimation of a moving object (e.g. a person or a robot) by computer calculation of displacements of 3D position coordinates of the nodes of a skeleton model, that is continuously fitted into a sequence of 3D point clouds. The node coordinates are present in table form in an electronic memory and the 3D point clouds are determined from electronically recorded images from a depth sensor camera that represent the moving person… an apparatus for pose estimation of a moving object. This apparatus comprises a depth sensor camera, an electronic memory… the memory storing the electronic images of the depth sensor camera… determine from the electronic images 3D point clouds representing the object, temporally in step with the image recording by the camera. The memory further stores a list of 3D coordinates for the nodes of a skeleton model… A skeleton model having only a low node count serves to effectively compress the image information when the coordinates of the nodes at any time describe the position of predetermined parts of the moving object; Par. [0116-120]: exemplary embodiment for the inventive pose estimation method with the skeleton model from FIG. 1 b) is presented in FIG. 4 using exemplary images from video sequences. The video images of a depth sensor camera constantly provide 3D point clouds that represent a moving person, using image segmentation that is known per se… A depth sensor camera that comprises an arithmetic unit for determining the distance coordinate can in particular also be engineered directly to carry out the inventive method. Such a camera as a constructional unit having at least one arithmetic unit designed according to the invention is correspondingly suited to directly translate the image of a moving person into 3D coordinates of his essential body parts; capturing, by a single motion capture device, visual data of the subject as it moves in a field of vision of the motion capture device (e.g. method for pose estimation of a moving object (i.e. subject), such as a person, for example, includes three dimensional (3D) point clouds that are determined from electronically recorded (i.e. captured, acquired, etc.) images (i.e. capturing visual data of the subject) by a depth sensor camera (i.e. by a single motion capture device), that represent a person (i.e. visual data of the subject as it moves in a field of vision of the motion capture device), for example, to provide 3D coordinates of predetermined body parts of the person that change over time during motion (i.e. capturing, by a single motion capture device, visual data of the subject as it moves in a field of vision of the motion capture device), as indicated above), for example);
from the captured visual data, recognising [recognizing] human pose points on the subject to extract data of the subject (Par. [0001]: method and an apparatus for real time-capable analysis of a sequence of electronic images for estimating the positions and orientations of a movable object captured in the image sequence, i.e. estimating the pose of the movable object; Par. [0024-26]: pose estimation by means of SOM training is proposed that is able to work with a skeleton model that is modelled according to the anatomy of the object observed and exhibits a reduced node count, it being possible to reliably and consistently assign each model node to a predetermined body part. Here the anatomy of the object whose pose is to be detected is modelled as a skeleton model… embodiment of the invention refers to a method for pose estimation of a moving object (e.g. a person or a robot) by computer calculation of displacements of 3D position coordinates of the nodes of a skeleton model, that is continuously fitted into a sequence of 3D point clouds; Par. [0068-73]: the learning rules are formed according to the following concept. Starting from the existence of an image sequence for which continuous pose estimation is to be carried out, the skeleton model that is a list of node positions (described in a 3D coordinate system), node pairs, and optionally node triplets, is in each case updated when a new image of the sequence exists. As soon as the new image is recorded by the depth sensor camera and has been converted by means of image segmentation and projection into a list of 3D coordinates (3D point cloud) for the points of the surface of the object observed--e.g. the entire person, his torso, his hand etc.… determination of the closest topology element is explained in more detail below, and the specific displacement rules for the topology elements are named… At first the crossing points of X in terms of all topology elements are at first determined for a point X of the 3D point cloud--that is to say for a coordinate point in the 3D space that, after imaging of the object observed using a depth sensor camera and subsequent image segmenting and projection, represents a point of the body surface of the person; Par. [0116-120]: exemplary embodiment for the inventive pose estimation method with the skeleton model from FIG. 1 b) is presented in FIG. 4 using exemplary images from video sequences. The video images of a depth sensor camera constantly provide 3D point clouds that represent a moving person, using image segmentation that is known per se. The anatomic skeleton model is fitted in real time using the learning rules described above, and the association of the model nodes with the different body parts of the person remains correct throughout as can be clearly gathered from FIG. 4… A depth sensor camera that comprises an arithmetic unit for determining the distance coordinate can in particular also be engineered directly to carry out the inventive method. Such a camera as a constructional unit having at least one arithmetic unit designed according to the invention is correspondingly suited to directly translate the image of a moving person into 3D coordinates of his essential body parts; from the captured visual data, recognizing human pose points on the subject to extract data of the subject (e.g. method for pose estimation of a moving object (i.e. subject), such as a person (i.e. human), for example, includes three dimensional (3D) point clouds that are determined from electronically recorded (i.e. captured, acquired, etc.) images (i.e. from the captured visual data), including analysis of a sequence of electronic images for estimating positions and orientations of a movable object captured in the image sequence, for example, to estimate (i.e. recognize, identify, determine, etc.) a pose of the movable object (i.e. from the captured visual data, recognizing human pose points on the subject), for example, including images recorded by the depth sensor camera that have been converted by image segmentation (i.e. to extract data of the subject) and projected into a list of 3D coordinates (3D point cloud) for points of a surface of an object observed (i.e. from the captured visual data, recognizing human pose points on the subject to extract data of the subject), for example, including coordinate points in the 3D space that, after imaging of the object observed using the depth sensor camera and subsequent image segmenting and projection, represent points of the body surface of a person, as indicated above), for example); and
based on the extracted data, recognising [recognizing] a plurality of predefined anatomical components of the subject to construct the model of the subject (Par. [0056-57]: an anatomically motivated skeleton model is now used… The model from FIG. 1 b) is particularly suited that unambiguously associates each node with a distinctive point of human anatomy (e.g. head, shoulder, elbow, hand, hip, pelvis, knee, foot). FIG. 1 c) represents a variant of the model from FIG. 1 b), where the torso is represented by triangles (in each case defined by three nodes that form the corners); Par. [0068-73]: the learning rules are formed according to the following concept. Starting from the existence of an image sequence for which continuous pose estimation is to be carried out, the skeleton model that is a list of node positions (described in a 3D coordinate system), node pairs, and optionally node triplets, is in each case updated when a new image of the sequence exists. As soon as the new image is recorded by the depth sensor camera and has been converted by means of image segmentation and projection into a list of 3D coordinates (3D point cloud) for the points of the surface of the object observed--e.g. the entire person, his torso, his hand etc.… determination of the closest topology element is explained in more detail below, and the specific displacement rules for the topology elements are named… At first the crossing points of X in terms of all topology elements are at first determined for a point X of the 3D point cloud--that is to say for a coordinate point in the 3D space that, after imaging of the object observed using a depth sensor camera and subsequent image segmenting and projection, represents a point of the body surface of the person; Par. [0116-120]: exemplary embodiment for the inventive pose estimation method with the skeleton model from FIG. 1 b) is presented in FIG. 4 using exemplary images from video sequences. The video images of a depth sensor camera constantly provide 3D point clouds that represent a moving person, using image segmentation that is known per se. The anatomic skeleton model is fitted in real time using the learning rules described above, and the association of the model nodes with the different body parts of the person remains correct throughout as can be clearly gathered from FIG. 4… A depth sensor camera that comprises an arithmetic unit for determining the distance coordinate can in particular also be engineered directly to carry out the inventive method. Such a camera as a constructional unit having at least one arithmetic unit designed according to the invention is correspondingly suited to directly translate the image of a moving person into 3D coordinates of his essential body parts; and based on the extracted data, recognizing a plurality of predefined anatomical components of the subject to construct the model of the subject (e.g. method for pose estimation of a moving object (i.e. subject), such as a person (i.e. human), for example, includes three dimensional (3D) point clouds that are determined (i.e. generated, calculated, computed, estimated, etc.) from electronically recorded images (i.e. captured visual data), including images recorded by a depth sensor camera that have been converted by image segmentation (i.e. based on the extracted data) and projected into a list of 3D coordinates (3D point cloud) for points of a surface of an object observed (i.e. recognized, analyzed, etc.), for example, by computer calculation of displacements (i.e. movements) of 3D position coordinates of nodes of a skeleton model (i.e. the model of the subject) that is modelled according to an anatomy of the object observed, including 3D point clouds that represent a moving person (i.e. recognizing a plurality of components of the subject to construct the model of the subject), for example, including coordinate points in 3D space that, after imaging of the object observed using the depth sensor camera and subsequent image segmenting and projection, represent points of the body surface of a person, for example, by directly translating images of a moving person into 3D coordinates of essential body parts and by assigning each model node to a predetermined body part (i.e. based on the extracted data, recognizing a plurality of predefined anatomical components of the subject to construct the model of the subject), for example, in order to provide 3D coordinates of predetermined body parts of a person that change over time during motion, as indicated above), for example), but fails to teach the following as further recited in claim 1.
However, Kord teaches a biomechanical model of a subject (Par. [0002]: motion capture system records the movements of a motion capture subject in a sequence of digital images, converting the recorded images to a mathematical model… The mathematical model may represent a biomechanical skeleton having rigid links connected to one another by rotatable joints; Par. [0013-22]: FIG. 2 is a pictorial view toward the front of a person acting as a motion capture subject, with the subject standing in an example of a scale frame, the scale frame positioned relative to a camera as in the example of FIG. 1, the subject's right hand at the left side of the figure, and an example of a biomechanical skeleton model overlaid on the motion capture subject showing examples of correspondence between links and joints in the biomechanical skeleton with the person's joints, limbs, head, neck, and torso… A single digital camera records images of a motion capture subject. The images are analyzed to assign values to parameters for a biomechanical skeleton capable of accurately emulating the motion capture subject's movements and body positions; a biomechanical model of a subject (e.g. motion capture system records movements of a motion capture subject (i.e. a subject) in a sequence of digital images, converts the recorded images of the subject to a mathematical model representing a biomechanical skeleton (i.e. a biomechanical model of a subject) having rigid links connected to one another by rotatable joints, as indicated above), for example);
capturing, by a single supported motion capture device from a capture position, visual data of the subject (Par. [0009-27]: example of an apparatus embodiment includes a digital camera, a central processing unit in data communication with the digital camera, a memory in data communication with the central processing unit, and an image calibration tool… method embodiment includes the capturing a sequence of digital images of a motion capture subject… FIG. 1 shows a pictorial view of an example of an apparatus for creating accurately calibrated motion capture images from a single stationary digital camera… FIG. 2 is a pictorial view toward the front of a person acting as a motion capture subject, with the subject standing in an example of a scale frame, the scale frame positioned relative to a camera as in the example of FIG. 1, the subject's right hand at the left side of the figure, and an example of a biomechanical skeleton model overlaid on the motion capture subject showing examples of correspondence between links and joints in the biomechanical skeleton with the person's joints, limbs, head, neck, and torso… A single digital camera records images of a motion capture subject… the single camera in an embodiment may remain entirely stationary in a fixed position for capturing images used for determining accurate positions, angles, and distances along all three mutually perpendicular spatial directions. Captured images may quickly and easily be recalibrated should a camera be located to a new viewing location, for example to record a motion capture subject from a new viewing angle… Turning to FIG. 1, an example of an apparatus embodiment 100 includes a camera 114… The camera may optionally be mounted on an adjustable-height tripod 116 or another stable camera support; capturing, by a single supported motion capture device from a capture position, visual data of the subject (e.g. motion capture system that records movements of a motion capture subject in a sequence of digital images (i.e. capturing visual data of the subject) includes capturing a sequence of digital images of the motion capture subject, including a person (i.e. a human) acting as the motion capture subject, for example, by using a single digital camera (Fig. 1, No. 114) that remains entirely stationary in a fixed position for capturing (i.e. a capture position) images of the motion capture subject (i.e. capturing, by a single supported motion capture device from a capture position, visual data of the subject), as shown in Fig. 1 below:
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, for example) as it moves between two distance markers that are disposed at a distance apart from each other and in a field of vision of the motion capture device (Par. [0009-51]: example of an apparatus embodiment includes a digital camera, a central processing unit in data communication with the digital camera, a memory in data communication with the central processing unit, and an image calibration tool… The image calibration tool includes a first calibration marker, a second calibration marker… The central processing unit is adapted to scale an image recorded by the digital camera in the memory by comparing a distance measured between the first and second calibration markers on the image calibration tool to a corresponding distance between the first and second calibration markers in the image… apparatus embodiment of the invention further includes a camera and a computer implemented in hardware, wherein the camera is in data communication with the computer and the computer is adapted to receive an image from the camera, convert the image to a silhouette, and extract parameters for an actor file from the image… method embodiment includes the capturing a sequence of digital images of a motion capture subject, each of the sequence of digital images including an image of an image calibration tool… comparing a separation distance between two calibration markers on the image calibration tool to a corresponding distance between the same two calibration markers a digital image, and overlaying a biomechanical skeleton over each of the sequence of digital images, with a biomechanical reference location superimposed over a movable joint of the motion capture subject and at least one link rotatably coupled to the biomechanical reference location… The image calibration tool enables accurate determination of angles, positions, and lengths in captured images without the use of multiple cameras or multiple camera positions for a single camera… A mathematical model in accord with an embodiment may be referred to as an actor file. An actor file represents a motion capture subject, for example a person engaged in a sports activity or an actor in a video game or motion picture, as an articulated biomechanical skeleton comprising rigid links joined to one another at biomechanical reference locations… image calibration tool 102 optionally includes at least two calibration markers 106 connected by at least two struts 104. In the example of FIG. 1, each of the calibration markers may take the form of a sphere or ball, although other shapes for calibration markers may be used in other embodiments… The image calibration tool 102 in the examples of FIGS. 1-2 comprises 12 struts and eight calibration markers 106, including an upper right front ball 132, an upper left front ball 134, a lower right front ball 136, and a lower left front ball 138, where left and right have been labeled with respect a viewing direction along the optical axis 128 toward the image calibration tool 102. Continuing on the back side of the image calibration 102, an upper right back ball 140, an upper left back ball 142, a lower right back ball 144, and a lower left back ball 146 are joined to one another and to the front balls by struts… A digital image captured by the digital camera 114 may be processed by a CPU included in some embodiments to extract parameters for an actor file… An image of a motion capture subject 148 may optionally be processed by the computer (ref. FIG. 1) to form a silhouette 150 corresponding to an outline of the person 148 from the camera's viewing direction. An example of a silhouette is shown in FIG. 3. As the motion capture subject moves, the camera collects images, for example a sequence of video images recorded at 30 frames per second or a sequence of still images recorded at selected positions of the subject 148… method in accord with an embodiment includes the steps of capturing a sequence of digital images of a motion capture subject, each of the sequence of digital images including an image of an image calibration tool, determining a separate scale factor for each of three mutually orthogonal spatial directions by comparing a separation distance between two calibration markers on the image calibration tool to a corresponding distance between the same two calibration markers in each of said sequence of digital images, and overlaying a biomechanical skeleton over each of the sequence of digital images with a biomechanical reference location superimposed over a movable joint of the motion capture subject and at least one link rotatably coupled to the biomechanical reference location… positioning the camera 114 along the y-axis at a distance 118 selected to fit the motion capture subject 148 into the camera's field of view; as it moves between two distance markers that are disposed at a distance apart from each other and in a field of vision of the motion capture device (e.g. motion capture system records movements of a motion capture subject in a sequence of digital images (i.e. capturing visual data of the subject) includes capturing a sequence of digital images of the motion capture subject, including a person acting as the motion capture subject (Fig. 2, No. 148) , as shown in Fig. 2 below:
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, for example, in which each of the sequence of digital images includes of a calibration tool captured by using a single digital camera (Fig. 1, No. 114), as shown in Fig. 1 below:
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, for example, including two calibration markers (Fig. 2, Nos. 132, 134, 136, 138, 104, 142, 144, 146), as shown in Fig. 2 below:
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, for example, separated by a distance measured between the first and second calibration markers and a corresponding distance in the captured images within the camera's field of view (i.e. two distance markers that are disposed at a distance apart from each other and in a field of vision of the motion capture device), including a width dimension 110 measured in the direction of the X axis, as shown in Fig. 1 below:
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, for example, and as the motion capture subject moves, the camera collects images of the motion capture subject (i.e. capturing visual data of the subject as it moves between two distance markers that are disposed at a distance apart from each other and in a field of vision of the motion capture device), as indicated above), for example).
Martinetz and Kord are considered to be analogous art because they pertain to image processing applications related to generating biomechanical skeletal models of subjects. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and an apparatus for real time-capable analysis of a sequence of electronic images for estimating the positions and orientations of a movable object captured in the image sequence, to generate a skeleton model that is modelled according to an anatomy of an object observed, including 3D point clouds that represent a moving person (as disclosed by Martinetz) with a biomechanical model of a subject and capturing visual data of the subject as it moves between two distance markers that are disposed at a distance apart from each other and in a field of vision of the motion capture device (as taught by Kord, Abstract, Par. [0009-51]) to create an articulated, movable mathematical model of a person from position, size, and angle information extracted from digital camera images, to accurately emulate a motion capture subject's movements and body positions, to create accurately calibrated motion capture images from a single stationary digital camera, and to enable accurate determination of angles, positions, and lengths in captured images (Kord, Abstract, Par. [0001-51]).
The combination of Martinetz and Kord, as a whole, teaches the method, as indicated above, but fails to teach the following as further recited in claim 1.
However, McGibbon teaches kinematic data (Par. [0001-7]: system and method for analyzing kinetic and kinematic information of human motion, and for viewing the information… present invention addresses these drawbacks by providing a full four-dimensional analysis (three space dimensions, one time dimension) of human movement data captured by a motion analysis system. The invention enables detailed biomechanical analysis of human movement data, as well as the visualization of data… a system for displaying kinematic and kinetic information of a subject is provided. The system includes an image input stage for acquiring image data of the subject, a transformation stage for transforming the image data into three dimensional coordinates corresponding to one or more body segments of the subject, and an output data stage for calculating the kinematic and kinetic information of the subject from the three dimensional coordinates… method comprises the steps of acquiring image data of the subject, transforming the image data into three dimensional coordinates corresponding to one or more body segments of the subject, and calculating the kinematic and kinetic information of the subject from the three dimensional coordinates; Par. [0020-28]: system and method, and a software facility, for the analysis of kinematics and kinetics of human movement. The present invention utilizes an eleven segment three-dimensional model of human movement analysis… FIG. 1 is a schematic block diagram of a movement analysis system… present invention relies on the acquisition of image data to provide an accurate estimation of movement. The image input stage 2 is utilized for acquiring, obtaining or receiving image data needed for the movement analysis system. The image input stage 2 can be any device or structure suitable for receiving, obtaining or acquiring image data… The image data acquired by the image input stage is converged to the transformation stage 4 which utilizes the acquired image data to track and build a 3-D model of the human body. In particular, the transformation stage 4 performs the coordinate transformation needed to calculate the various kinetics and kinematics discussed in more detail below. The output data stage 6 generates output containing an array of information used in modeling human movement. In particular, the output data stage 6 provides output analysis for the various kinematic and kinetic parameters… FIG. 7 is a schematic block diagram illustration of the output data stage 6 of FIG. 1. The output data stage 6 generates numerous output files containing a variety of useful biomechanical measures. In general, the output data stage 6 provides the kinematic output information and kinetic output information. The illustrated kinematic analysis module 64 provides for kinematic analysis on all of the eleven segmented body parts mentioned above. In particular, the kinematic analysis module 64 provides for a greater understanding of how the body of the subject 30 move relative to one another (coordination), as well as the rates at which they move (velocities). Thus, the kinematic analysis module 64 includes analysis information regarding the subject's bodily motions; kinematic data (e.g. system and method for analyzing kinetic and kinematic information (i.e. kinematic data) of human motion and for providing detailed biomechanical analysis of human movement data includes acquiring image data of a subject, transforming the image data into three dimensional coordinates (i.e. 3D point cloud) corresponding to one or more body segments of the subject (i.e. predefined anatomical components of the subject), for example, and calculating (i.e. extracting, computing, etc.) the kinematic information of the subject from the three dimensional coordinates, as indicated above), for example).
Martinetz, Kord, and McGibbon are considered to be analogous art because they pertain to image processing applications related to generating biomechanical skeletal models of subjects. Therefore, the combined teachings of Martinetz, Kord, and McGibbon, as a whole, would have rendered obvious the invention recited in claim 1 with a reasonable expectation of success in order to modify the method and an apparatus for real time-capable analysis of a sequence of electronic images for estimating the positions and orientations of a movable object captured in the image sequence, to generate a skeleton model that is modelled according to an anatomy of an object observed, including 3D point clouds that represent a moving person (as disclosed by Martinetz) with kinematic data (as taught by McGibbon, Abstract, Par. [0001-7, 20-28]) to provide a software facility for computing and displaying kinematic and kinetic information of a subject to a user, to analyze various human movements, to provide output analysis for various kinematic and kinetic parameters, thus allowing a more detail output of various modeled segments acquired, to provide an accurate estimation of movement, and to analyze aspects of motion of a body and body segments (McGibbon, Abstract, Par. [0001-7, 20-29]).
Regarding claim 2, claim 1 incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]) wherein the captured visual data includes a plurality of video frames (Martinetz, Par. [0116]: exemplary embodiment for the inventive pose estimation method with the skeleton model from FIG. 1 b) is presented in FIG. 4 using exemplary images from video sequences. The video images of a depth sensor camera constantly provide 3D point clouds that represent a moving person), and the constructed biomechanical model is based on at least one frame capturing the subject in a predefined stance (i.e. position) (Kord, par. [0033-54]: As the motion capture subject moves, the camera collects images, for example a sequence of video images recorded at 30 frames per second or a sequence of still images recorded at selected positions of the subject 148… By measuring the positions of biomechanical reference locations in images of the subject and compensating the measured values with scaling information derived from images of the image calibration tool, the spatial coordinates may be determined for each biomechanical reference location on the biomechanical skeleton… positioning the motion capture subject 148 in the center of the camera's field of view, facing the camera 114 in a relaxed posture and close enough to the camera to achieve desired image resolution, also referred to as an initialization pose or alternately as a master pose; and the constructed biomechanical model is based on at least one frame capturing the subject in a predefined stance (e.g. motion capture system records movements of a motion capture subject in a sequence of digital images (i.e. capturing visual data of the subject) includes measuring positions of biomechanical reference locations (i.e. a predefined stance, position, etc.) in images of the subject, including images recorded at selected (i.e. predefined, predetermined, etc.) positions of the subject, for example, and by measuring positions of biomechanical reference locations in images of the subject and compensating the measured values with scaling information derived from images of the image calibration tool, spatial coordinates are determined for each biomechanical reference location on the biomechanical skeleton, as indicated above), for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1.
Regarding claim 4, claim 1 incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]) wherein the motion capture device is substantially stationarily supported (Par. [0009-27]: example of an apparatus embodiment includes a digital camera, a central processing unit in data communication with the digital camera, a memory in data communication with the central processing unit, and an image calibration tool… method embodiment includes the capturing a sequence of digital images of a motion capture subject… FIG. 1 shows a pictorial view of an example of an apparatus for creating accurately calibrated motion capture images from a single stationary digital camera… FIG. 2 is a pictorial view toward the front of a person acting as a motion capture subject, with the subject standing in an example of a scale frame, the scale frame positioned relative to a camera as in the example of FIG. 1, the subject's right hand at the left side of the figure, and an example of a biomechanical skeleton model overlaid on the motion capture subject showing examples of correspondence between links and joints in the biomechanical skeleton with the person's joints, limbs, head, neck, and torso… A single digital camera records images of a motion capture subject… the single camera in an embodiment may remain entirely stationary in a fixed position for capturing images used for determining accurate positions, angles, and distances along all three mutually perpendicular spatial directions. Captured images may quickly and easily be recalibrated should a camera be located to a new viewing location, for example to record a motion capture subject from a new viewing angle… Turning to FIG. 1, an example of an apparatus embodiment 100 includes a camera 114… The camera may optionally be mounted on an adjustable-height tripod 116 or another stable camera support; wherein the motion capture device is substantially stationarily supported (e.g. motion capture system that records movements of a motion capture subject in a sequence of digital images (i.e. capturing visual data of the subject) includes capturing a sequence of digital images of the motion capture subject, including a person (i.e. a human) acting as the motion capture subject, for example, by using a single digital camera (Fig. 1, No. 114) that remains entirely stationary in a fixed position for capturing (i.e. a capture position) images of the motion capture subject (i.e. wherein the motion capture device is substantially stationarily supported), as shown in Fig. 1 below:
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, for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1.
Regarding claim 5, claim 1 incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]) wherein the motion capture device is a camera (Martinetz, par. [0026-38]: method for pose estimation of a moving object (e.g. a person or a robot) by computer calculation of displacements of 3D position coordinates of the nodes of a skeleton model, that is continuously fitted into a sequence of 3D point clouds. The node coordinates are present in table form in an electronic memory and the 3D point clouds are determined from electronically recorded images from a depth sensor camera that represent the moving person… an apparatus for pose estimation of a moving object. This apparatus comprises a depth sensor camera, an electronic memory, and a programmable arithmetic unit, the memory storing the electronic images of the depth sensor camera and the arithmetic unit being designed to determine from the electronic images 3D point clouds representing the object, temporally in step with the image recording by the camera).
Regarding claim 6, claim 5 incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]) wherein the camera is a smartphone camera (Kord, Par. [0040]: In the example of FIG. 1, the digital camera 114 and computer 122 are provided as separated devices. In alternative embodiments, the camera and computer may be combined into a single device, for example the portable computer 182 with an integrated camera in FIG. 6. Examples of a portable computer with an integrated camera suitable for use with an embodiment include, but are not limited to, a laptop computer, a tablet computer, a personal digital assistant, and a smart phone having cellular telephone combined with a CPU, data and program memory).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1.
Regarding claim 9, claim 1 incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]) wherein the visual data of a [the] subject is captured without the use of wearable subject makers on the subject (Martinetz, Par. [00119-120]: the method described for pose estimation can be carried out by an apparatus… a camera as a constructional unit having at least one arithmetic unit designed according to the invention is correspondingly suited to directly translate the image of a moving person into 3D coordinates of his essential body parts. This is comparable to a motion capture apparatus where, however, the markers on the body of the person that until now were common, can be dispensed with).
Regarding claim 11, claim 1 incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]) including the further step of formulating based on the constructed biomechanical model, a motion performance metric (McGibbon, Par. [0005-30]: providing a full four-dimensional analysis (three space dimensions, one time dimension) of human movement data captured by a motion analysis system. The invention enables detailed biomechanical analysis of human movement data, as well as the visualization of data… a system and method, and a software facility, for the analysis of kinematics and kinetics of human movement… output data stage 6 generates numerous output files containing a variety of useful biomechanical measures… the output data stage 6 provides the kinematic output information and kinetic output information… determine the forces that interact among the various body segments of the subject 30… body forces and torques 80 are useful in evaluating athletic performance; formulating based on the constructed biomechanical model, a motion performance metric (e.g. system and method for analyzing kinetic and kinematic information (i.e. kinematic data) of human motion and for providing detailed biomechanical analysis of human movement data includes acquiring image data of a subject, transforming the image data into three dimensional coordinates (i.e. 3D point cloud) corresponding to one or more body segments of the subject (i.e. the constructed biomechanical model), for example, and provides kinematic output information that determines forces that interact among various body segments of human movement of a subject, for example, including body forces and torques that are useful in evaluating (i.e. measure, metric, appraise, etc.) athletic performance (i.e. formulating based on the constructed biomechanical model, a motion performance metric), as indicated above), for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1.
Regarding claim 12, claim 1 [11] incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]) including the further step of outputting the motion performance metric for visual display on a display device (McGibbon, Par. [0005-31]: providing a full four-dimensional analysis (three space dimensions, one time dimension) of human movement data captured by a motion analysis system. The invention enables detailed biomechanical analysis of human movement data, as well as the visualization of data… providing a software facility for computing and displaying kinematic and kinetic information to a user… a system for displaying kinematic and kinetic information of a subject is provided. The system includes an image input stage for acquiring image data of the subject, a transformation stage for transforming the image data into three dimensional coordinates corresponding to one or more body segments of the subject, and an output data stage for calculating the kinematic and kinetic information of the subject from the three dimensional coordinates. The system can also include a user interface for displaying the calculated kinematic and kinetic information of the subject… a method for displaying kinematic and kinetic information of a subject is also provided… a system and method, and a software facility, for the analysis of kinematics and kinetics of human movement… user interface 8 displays in various formats the calculated outputs of the output data stage 6… output data stage 6 generates numerous output files containing a variety of useful biomechanical measures… the output data stage 6 provides the kinematic output information and kinetic output information… determine the forces that interact among the various body segments of the subject 30… body forces and torques 80 are useful in evaluating athletic performance… FIG. 10 is a detailed depiction of the user interface 8. The user interface 8 is a flexible tool for analyzing and displaying the output data stage 6 information; outputting the motion performance metric for visual display on a display device (e.g. system and method for analyzing kinetic and kinematic information (i.e. kinematic data) of human motion and for providing detailed biomechanical analysis of human movement data includes acquiring image data of a subject, transforming the image data into three dimensional coordinates (i.e. 3D point cloud) corresponding to one or more body segments of the subject (i.e. the constructed biomechanical model), for example, and provides kinematic output information that determines forces that interact among various body segments of human movement of a subject, for example, including body forces and torques that are useful in evaluating (i.e. measure, metric, appraise, etc.) athletic performance (i.e. motion performance metric), for example, and displaying the output data via a user interface display (i.e. outputting the motion performance metric for visual display on a display device), as indicated above), for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1.
Regarding claim 13, is a corresponding apparatus claim rejected as applied to the method claim 1 above.
Regarding claim 14, claim 1 incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]) including the further steps of:
recognising [recognizing] a captured length of a component of the subject having a known real-world length (Kord, Par. [0011-32]: method embodiment includes the capturing a sequence of digital images of a motion capture subject… overlaying a biomechanical skeleton over each of the sequence of digital images, with a biomechanical reference location superimposed over a movable joint of the motion capture subject and at least one link rotatably coupled to the biomechanical reference location. A length of the link in each image corresponds to a projected length. A true length of each link is determined… A joint in the biomechanical skeleton is positioned at the biomechanical reference location and a link having a true length is coupled to the joint... A single digital camera records images of a motion capture subject. The images are analyzed to assign values to parameters for a biomechanical skeleton… Image scale information for creating the biomechanical skeleton may be determined along each of the three mutually perpendicular spatial axes by comparing known dimensions for an image calibration tool with corresponding dimensions of the tool's image recorded with images of the subject. After image scale has been determined for all three spatial axes in recorded images, the true length and true position of each link and joint in the biomechanical skeleton may be determined accurately from recorded images so that motions and positions of the motion capture subject may be accurately reproduced by the biomechanical skeleton… By comparing known sizes and positions of components on the image scale tool to sizes and positions of the same components measured in captured images, true dimensions, true angles, and true positions of objects in captured images may be determined accurately from the images. Examples of parameters which may be determined accurately from calibrated images of a motion capture subject include, but are not limited to, limb length, joint position, limb and joint positions with respect to a position reference, limb angles, and distances traversed by the motion capture subject. The image calibration tool enables accurate determination of angles, positions, and lengths in captured images… Known dimensions, shapes, and positions of the calibration markers with respect to one another are used to create calibrated digital images of the image calibration tool… The known lengths of each strut and the known diameter of each ball in the image calibration tool may be compared to their dimensions in a camera image of the image calibration tool to determine dimensions, angles, and positions for other objects in the image; recognizing a captured length of a component of the subject having a known real-world length (e.g. motion capture system records movements of a motion capture subject (i.e. a subject) in a sequence of digital images, converts the recorded images of the subject to a mathematical model representing a biomechanical skeleton, for example, includes determination (i.e. recognition, identification, calculation, etc.) of angles, positions, and lengths in captured images of a motion capture subject (i.e. captured length of a component) based on known dimensions (i.e. a known real-world length), shapes, and positions of calibration markers, with respect to one another (i.e. recognizing a captured length of a component of the subject having a known real-world length), for example, which are used to create calibrated digital images of the image calibration tool, as indicated above), for example);
mapping the known real-world length of the component to the captured length of the component (Kord, Par. [0002-32]: motion capture system records the movements of a motion capture subject in a sequence of digital images, converting the recorded images to a mathematical model that may be manipulated in a computer system. The mathematical model may represent a biomechanical skeleton having rigid links connected to one another by rotatable joints. A computer graphics system may map images of a character in a motion picture or video game onto the links and joints of the biomechanical skeleton to cause the character's movements to emulate the motion capture subject's movements… method embodiment includes the capturing a sequence of digital images of a motion capture subject… overlaying a biomechanical skeleton over each of the sequence of digital images, with a biomechanical reference location superimposed over a movable joint of the motion capture subject and at least one link rotatably coupled to the biomechanical reference location. A length of the link in each image corresponds to a projected length. A true length of each link is determined… A joint in the biomechanical skeleton is positioned at the biomechanical reference location and a link having a true length is coupled to the joint... A single digital camera records images of a motion capture subject. The images are analyzed to assign values to parameters for a biomechanical skeleton… Image scale information for creating the biomechanical skeleton may be determined along each of the three mutually perpendicular spatial axes by comparing known dimensions for an image calibration tool with corresponding dimensions of the tool's image recorded with images of the subject. After image scale has been determined for all three spatial axes in recorded images, the true length and true position of each link and joint in the biomechanical skeleton may be determined accurately from recorded images so that motions and positions of the motion capture subject may be accurately reproduced by the biomechanical skeleton… By comparing known sizes and positions of components on the image scale tool to sizes and positions of the same components measured in captured images, true dimensions, true angles, and true positions of objects in captured images may be determined accurately from the images. Examples of parameters which may be determined accurately from calibrated images of a motion capture subject include, but are not limited to, limb length, joint position, limb and joint positions with respect to a position reference, limb angles, and distances traversed by the motion capture subject. The image calibration tool enables accurate determination of angles, positions, and lengths in captured images… Known dimensions, shapes, and positions of the calibration markers with respect to one another are used to create calibrated digital images of the image calibration tool… The known lengths of each strut and the known diameter of each ball in the image calibration tool may be compared to their dimensions in a camera image of the image calibration tool to determine dimensions, angles, and positions for other objects in the image; mapping the known real-world length of the component to the captured length of the component (e.g. motion capture system records movements of a motion capture subject (i.e. a subject) in a sequence of digital images, converts the recorded images of the subject to a mathematical model representing a biomechanical skeleton, for example, includes determination (i.e. recognition, identification, calculation, etc.) of angles, positions, and lengths in captured images of a motion capture subject (i.e. captured length of the component) based on known dimensions (i.e. known real-world length), shapes, and positions of calibration markers, with respect to one another, for example, which are used to create calibrated digital images of the image calibration tool, for example and map images of a character in a motion picture or video game onto the links and joints of the biomechanical skeleton to cause the character's movements to emulate the motion capture subject's movements (i.e. mapping the known real-world length of the component to the captured length of the component), as indicated above), for example); and
based on the biomechanical model, formulating a motion performance metric (McGibbon, Par. [0005-30]: providing a full four-dimensional analysis (three space dimensions, one time dimension) of human movement data captured by a motion analysis system. The invention enables detailed biomechanical analysis of human movement data, as well as the visualization of data… a system and method, and a software facility, for the analysis of kinematics and kinetics of human movement… output data stage 6 generates numerous output files containing a variety of useful biomechanical measures… the output data stage 6 provides the kinematic output information and kinetic output information… determine the forces that interact among the various body segments of the subject 30… body forces and torques 80 are useful in evaluating athletic performance; based on the biomechanical model, formulating a motion performance metric (e.g. system and method for analyzing kinetic and kinematic information (i.e. kinematic data) of human motion and for providing detailed biomechanical analysis of human movement data includes acquiring image data of a subject, transforming the image data into three dimensional coordinates (i.e. 3D point cloud) corresponding to one or more body segments of the subject (i.e. the constructed biomechanical model), for example, and provides kinematic output information that determines forces that interact among various body segments of human movement of a subject, for example, including body forces and torques that are useful in evaluating (i.e. measure, metric, appraise, etc.) athletic performance (i.e. based on the biomechanical model, formulating a motion performance), as indicated above), for example), wherein the biomechanical model includes real-world lengths based on the mapping (Kord, Par. [0002-32]: motion capture system records the movements of a motion capture subject in a sequence of digital images, converting the recorded images to a mathematical model that may be manipulated in a computer system. The mathematical model may represent a biomechanical skeleton having rigid links connected to one another by rotatable joints. A computer graphics system may map images of a character in a motion picture or video game onto the links and joints of the biomechanical skeleton to cause the character's movements to emulate the motion capture subject's movements… method embodiment includes the capturing a sequence of digital images of a motion capture subject… overlaying a biomechanical skeleton over each of the sequence of digital images, with a biomechanical reference location superimposed over a movable joint of the motion capture subject and at least one link rotatably coupled to the biomechanical reference location. A length of the link in each image corresponds to a projected length. A true length of each link is determined… A joint in the biomechanical skeleton is positioned at the biomechanical reference location and a link having a true length is coupled to the joint... A single digital camera records images of a motion capture subject. The images are analyzed to assign values to parameters for a biomechanical skeleton… Image scale information for creating the biomechanical skeleton may be determined along each of the three mutually perpendicular spatial axes by comparing known dimensions for an image calibration tool with corresponding dimensions of the tool's image recorded with images of the subject. After image scale has been determined for all three spatial axes in recorded images, the true length and true position of each link and joint in the biomechanical skeleton may be determined accurately from recorded images so that motions and positions of the motion capture subject may be accurately reproduced by the biomechanical skeleton… By comparing known sizes and positions of components on the image scale tool to sizes and positions of the same components measured in captured images, true dimensions, true angles, and true positions of objects in captured images may be determined accurately from the images. Examples of parameters which may be determined accurately from calibrated images of a motion capture subject include, but are not limited to, limb length, joint position, limb and joint positions with respect to a position reference, limb angles, and distances traversed by the motion capture subject. The image calibration tool enables accurate determination of angles, positions, and lengths in captured images… Known dimensions, shapes, and positions of the calibration markers with respect to one another are used to create calibrated digital images of the image calibration tool… The known lengths of each strut and the known diameter of each ball in the image calibration tool may be compared to their dimensions in a camera image of the image calibration tool to determine dimensions, angles, and positions for other objects in the image; wherein the biomechanical model includes real-world lengths based on the mapping (e.g. motion capture system records movements of a motion capture subject (i.e. a subject) in a sequence of digital images, converts the recorded images of the subject to a mathematical model representing a biomechanical skeleton, for example, includes determination (i.e. recognition, identification, calculation, etc.) of angles, positions, and lengths in captured images of a motion capture subject (i.e. captured length of the component) based on known dimensions (i.e. real-world lengths), shapes, and positions of calibration markers, with respect to one another, for example, which are used to create calibrated digital images of the image calibration tool, for example and map images of a character in a motion picture or video game onto the links and joints of the biomechanical skeleton to cause the character's movements to emulate the motion capture subject's movements (i.e. wherein the biomechanical model includes real-world lengths based on the mapping), as indicated above), for example).
The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Martinetz, in view of Kord, in further view of McGibbon, as applied to claim 1 above, and in further view of Folland et al. (US PG Publication No. 2018/0279916 A1), hereafter referred to Folland.
Regarding claim 3, claim 2 incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]), but fails to teach the following as further recited in claim 3.
However, Folland teaches wherein the predefined stance is one or more of:
a toe-off stance whereby a back foot of the subject is lifted off a ground push-off point;
a touch down stance whereby a front foot of the subject is about to contact a ground drop point; and
a full support stance whereby the front foot flattens on the ground drop point and whereby hip and heel human pose points of the subject are vertically aligned (Par. [0009-58]: system for monitoring the running technique of a user undertaking a physical activity… sensors may also detect at least three parameters relating to the movement of the user… It has also been found that the analysis of the parameters relating to ground contact, stride pattern and centre of mass of the user can have utility in the assessment of running technique… the one or more sensors may detect at least one parameter relating to the ground contact of the user, such as relating to: ground contact time (GCT); flight time (FLT); duty factor (DF); touch down to centre of mass (CM) distance, such as the anterior-posterior distance between the CM and toe at touch down… take-off to centre of mass distance, such as the anterior-posterior distance between the CM and toe at take-off… or ground contact distance, such as the sum of the anterior-posterior distance between the CM and toe at touch down and take-off… the one or more sensors may detect for example: a parameter relating to duty factor and a parameter relating to take-off to centre of mass distance, such as the anterior-posterior distance between the CM and toe at take-off; or a parameter relating to the ground contact distance… and a parameter relating to lower spine angle, preferably relative to the angle during a standing stance… lternatively, or in addition, the one or more sensors may detect at least one parameter relating to the stride pattern of the user, such as relating to: stride rate (SR) or stride length, such as the anterior-posterior distance covered by the CM during a complete stride (e.g. right foot touch down to next right foot touch down)… change in velocity of the centre of mass of the user, such as the difference in anterior-posterior velocity of the CM between the minimum and maximum, for example during stance, such as around take-off; or change in vertical position of the centre of mass of the user, such as the difference between the highest and lowest vertical position of the CM during each step (right foot touch down to left foot touch down)… comparison with the biomechanical model may comprise an analysis of: the velocity of the pelvis, preferably the minimum forward pelvic velocity, and ground contact time… duty factor and take-off to centre of mass distance, such as the anterior-posterior distance between the CM and toe at take-off… anterior-posterior velocity of the CM between the minimum and maximum, for example during stance, such as around take-off; or ground contact distance… and lower spine angle, preferably relative to the angle during a standing stance… method may alternatively, or in addition, utilise at least one sensor attached to the body of the individual, attached to a garment worn by the individual, attached to or incorporated in a shoe or shoes worn by the individual, incorporated in a device carried by or attached to the individual, such as a portable electronic device or watch, or may utilise other methodology such as video recording and analysis… the method may comprise the measurement of at least one parameter relating to: the ground contact of the individual, such as relating to ground contact time (GCT); flight time (FLT); duty factor (DF); touch down to centre of mass distance (CM), such as the anterior-posterior distance between the CM and toe at touch down… take-off to centre of mass distance, such as the anterior-posterior distance between the CM and toe at take-off… or ground contact distance, such as the sum of the anterior-posterior distance between the CM and toe at touch down and take-off… method may comprise the measurement of at least one parameter relating to: the stride pattern of the individual, such as relating to stride rate (SR); or stride length, such as the anterior-posterior distance covered by the CM during a complete stride (e.g. right foot touch down to next right foot touch down); wherein the predefined stance is one or more of: a toe-off stance whereby a back foot of the subject is lifted off a ground push-off point; a touch down stance whereby a front foot of the subject is about to contact a ground drop point (e.g. system for monitoring the running technique of a user undertaking a physical activity includes video recording and analysis to detect parameters relating to the user’s stride pattern, such as a toe at take-off parameter of the user (i.e. predefined stance is one or more of: a toe-off stance whereby a back foot of the subject is lifted off a ground push-off point), for example, and a toe at touch down parameter of the user (i.e. a touch down stance whereby a front foot of the subject is about to contact a ground drop point), as indicated above), for example).
Martinetz, Kord, McGibbon, and Folland are considered to be analogous art because they pertain to image processing applications related to generating biomechanical skeletal models of subjects. Therefore, the combined teachings of Martinetz, Kord, McGibbon, and Folland, as a whole, would have rendered obvious the invention recited in claim 3 with a reasonable expectation of success in order to modify the method and an apparatus for real time-capable analysis of a sequence of electronic images for estimating the positions and orientations of a movable object captured in the image sequence, to generate a skeleton model that is modelled according to an anatomy of an object observed, including 3D point clouds that represent a moving person (as disclosed by Martinetz) with wherein the predefined stance is one or more of: a toe-off stance whereby a back foot of the subject is lifted off a ground push-off point; a touch down stance whereby a front foot of the subject is about to contact a ground drop point (as taught by Folland, Abstract, Par. [0009-58]) by using kinematic variables and biomechanical models for monitoring, assessing and improving a running technique of an individual undertaking a physical activity, to improve running performance and to prevent and/or to reduce injury, and to monitor the form and technique of the user and provide improvement feedback (Folland, Abstract, Par. [0001-10, 52, 110]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Martinetz, in view of Kord, in further view of McGibbon, as applied to claim 1 above, and in further view of Aginsky et al. (US PG Publication No. 2019/0274614 A1), hereafter referred to Aginsky.
Regarding claim 7, claim 5 incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]), but fails to teach the following as further recited in claim 7.
However, Aginsky teaches wherein the camera is an IP [internet protocol (IP)] camera (par. [0077-81]: Computing device 204 receives images (e.g., video) and/or body part locations of the target individual captured by one or more sensors(s) 212. The body part locations correspond to the captured images. The body part locations may be computed based on an analysis of the captured images (e.g., based on visual markers denoting the body part locations, and/or by code that performs a 3D analysis of the images), and/or the body part locations may be computed by a kinetic sensor component of sensor(s) 212 that captures depth data… sensor(s) 212 includes video camera(s) that captures the video and code for analyzing the video for computing the body location data… Sensor(s) 212 may be implemented as an external device and/or integrated into a computing device, for example, an IP camera and/or camera of a smartphone).
Martinetz, Kord, McGibbon, and Aginsky are considered to be analogous art because they pertain to image processing applications related to generating biomechanical skeletal models of subjects. Therefore, the combined teachings of Martinetz, Kord, McGibbon, and Aginsky, as a whole, would have rendered obvious the invention recited in claim 7 with a reasonable expectation of success in order to modify the method and an apparatus for real time-capable analysis of a sequence of electronic images for estimating the positions and orientations of a movable object captured in the image sequence, to generate a skeleton model that is modelled according to an anatomy of an object observed, including 3D point clouds that represent a moving person (as disclosed by Martinetz) with wherein the camera is an internet protocol (IP) camera (as taught by Aginsky, Abstract, Par. [0077-81]) by using systems and methods for assessment of a musculoskeletal profile of a target individual to improve athletic performance, to estimate a value indicative of muscle strength ratio of at least one target muscle of a target individual based on an analysis of a plurality of digital images, to convert image-parameter to an estimate of a measured-parameter indicative of strength measurement ratios of at least one target muscle obtained, according to correlation code that correlates between image-parameters and measured-parameters obtained, to identify a plurality of image state-frames, each image state-frame corresponding to a certain state of a plurality of states of the certain physical movement test, and identifying the first and second defined movements according to corresponding image state-frames based on a predefined order of the corresponding plurality of states of the physical movement test, to compute respective image-metrics (Aginsky, Abstract, Par. [0001-7, 33, 47, 51, 54, 82]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Martinetz, in view of Kord, in further view of McGibbon, as applied to claim 1 above, and in further view of Sullivan et al. (US PG Publication No. 2010/0164862 A1), hereafter referred to Sullivan.
Regarding claim 8, claim 1 incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]), but fails to teach the following as further recited in claim 8.
However, Sullivan teaches wherein the motion capture device includes two synchronised cameras (Par. [0005-28]: system includes a visual data collector for collecting visual information from a first image of at least a first feature of an object… determine the position of a representation of the first feature in a virtual representation of the object from the combined visual information… motion data combiner may use the visual information to define a pose of the object… The motion data combiner may be configured to synchronize the visual information… While the one camera 206 is illustrated as being included in the visual motion sensing system 104, two or more cameras (which are typically synchronized for image capture) may also be included for capturing visual information from different perspectives).
Martinetz, Kord, McGibbon, and Sullivan are considered to be analogous art because they pertain to image processing applications related to generating biomechanical skeletal models of subjects. Therefore, the combined teachings of Martinetz, Kord, McGibbon, and Sullivan, as a whole, would have rendered obvious the invention recited in claim 8 with a reasonable expectation of success in order to modify the method and an apparatus for real time-capable analysis of a sequence of electronic images for estimating the positions and orientations of a movable object captured in the image sequence, to generate a skeleton model that is modelled according to an anatomy of an object observed, including 3D point clouds that represent a moving person (as disclosed by Martinetz) with wherein the camera is an internet protocol (IP) camera (as taught by Sullivan, Abstract, Par. [0005-28]) to allow motion data to be generated based on tracking and recording the movement of real objects, to increase accuracy for determining poses (e.g., position, orientation) of an object, to determine the position of a representation of the first feature in a virtual representation of the object from combined visual information, and to improve the accuracy of visual information (Sullivan, Abstract, Par. [0001-9, 17-28]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Martinetz, in view of Kord, in further view of McGibbon, as applied to claim 1 above, and in further view of Homsi et al. (US PG Publication No. 2012/0130514 A1), hereafter referred to Homsi.
Regarding claim 10, claim 1 incorporated and the combination of Martinetz, Kord, and McGibbon, as a whole, teaches a [the] method (Martinetz, Par. [0001-5]), but fails to teach the following as further recited in claim 10.
However, Homsi teaches wherein the distance between the two distance markers is a predetermined distance of 20 metres [meters] (Par. [0043-140]: methods of rating the performance of an athlete… different tests may be administered to determine an athlete's athleticism… the athletic performance tests may include… measuring sprint time of the athlete over a 20-meter distance, and Yo Yo Intermittent Recovery Test (YIRT)… The 20-meter dash is described above… In the Yo Yo Intermittent Recovery Test (YIRT) measures the "start-stop-recover-start" nature of soccer. With reference to FIG. 25, the athletes starts at a starting line 250 located between a pair of cones 252A and 252B, and completes pairs of 20-meter sprints to an intermediate line 254 positioned between a pair of cones 256A and 256B, at a distance of 20-meters from the starting line 250, until failure of the athlete. From a recorded CD, a first beep initiates the first 20-meter sprint, the second beep ends the first 20-meter sprint and initiates the second 20-meter sprint).
Martinetz, Kord, McGibbon, and Homsi are considered to be analogous art because they pertain to image processing applications related to generating biomechanical skeletal models of subjects. Therefore, the combined teachings of Martinetz, Kord, McGibbon, and Homsi, as a whole, would have rendered obvious the invention recited in claim 10 with a reasonable expectation of success in order to modify the method and an apparatus for real time-capable analysis of a sequence of electronic images for estimating the positions and orientations of a movable object captured in the image sequence, to generate a skeleton model that is modelled according to an anatomy of an object observed, including 3D point clouds that represent a moving person (as disclosed by Martinetz) with wherein the distance between the two distance markers is a predetermined distance of 20 meters (as taught by Homsi, Abstract, Par. [0043-140], Fig. 25) to evaluate and rate the performance of an athlete, to measure a different athletic skill that is needed to compete effectively in a defined sport, to map the range of performance and establish percentile rankings and thresholds for each test value observed during testing of the athletes, to yield an overall athleticism rating, and to assess the athletic ability and/or performance of a given athlete and measures an athletic performance skill related to a particular sport or physical activity (Homsi, Abstract, Par. [0003-12, 43-45, 63-64, 107, 139-140]).
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/GUILLERMO M RIVERA-MARTINEZ/ Primary Examiner, Art Unit 2677
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