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 .
This Action is in reply to the May 11, 2026 Amendment filed under 37 CFR 1.111. In the Amendment, claims 3, 10, 11, and 20 are amended, claim 16 is cancelled, and claims 21, 22, 1nd 23 are added. After Entry of the Amendment, claims 1-23 are pending.
Response to Amendment
In view of the Amendment, the objection to claims 3, 16 and 20 is obviated, and accordingly withdrawn.
In view of the Amendment, the rejection of claim 10 under 35 USC 112(b) is obviated, and accordingly withdrawn.
In light of the Amendment, the rejection of the claimed invention employing at least U.S. Patent Application Publication 2021/0049353 A1, to Bain has been clarified, and a new grounds of rejection has been added to the claimed invention.
In view of the Amendment, at least one new grounds of rejection has been made to the claimed invention. Therefore, in light of the Amendment, this rejection is made Final.
Response to Arguments
Applicants’ Representative, asserts that Bain fails to anticipate claim 1, or claim 17 at least because Bain fails to disclose or suggest these limitations: “capturing, by a single supported motion capture device from a capture position, visual data of a subject as it moves between at least two distance markers in a filed of vision of the motion capture device” Reply page 6.
The Reference, U.S. Patent Publication No. 205/0097937 to Kord discloses: 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. FIGS. 2-5 show different views of an example of a biomechanical skeleton 154 superimposed over an image recorded by the camera 114 of a motion capture subject 148 standing in an image calibration tool 102. The image calibration tool 102, 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. 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, para. 0032-0033).
The motion capture subject preferably wears close-fitting clothing to improve the accuracy of positions determined for limb lengths, joint locations, and other parameters, para. 0033).
Claim Interpretation
During patent examination, pending claims must be “given their broadest reasonable interpretation consistent with the specification.” MPEP 2111; See also, MPEP 2173.02. Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969). See also, In re Zletz, 893 F.2d 319, 321-22, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (“During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow”). The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed. An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.
The Examiner respectfully requests of the Applicant in preparing responses, to consider fully the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20, 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2021/0049353 A1 to Bian et al. (hereinafter Bian) in view of U.S. Patent Publication No. 205/0097937 to Kord.
With regards to claim, 1, Bian discloses:
1. A method for generating a motion performance metric including the steps of:
[capturing, by a single supported motion capture device from a capture position, visual data of a subject as it moves between at least two distance markers in a field of vision of the motion capture device];
from the captured visual data, extracting kinematic data of the subject (e.g., number of steps, cadence, stride length, arm swing) (see, as above, and detailed description, including, performing assessment analytics based on at least one of determined gait parameters, para. 0197); and
based on the extracted kinematic data, formulating a motion performance metric (see, as above, and detailed description, including, in performing the gait test, the device 130 may determine a gait estimate and/or gait classification confidence score as described herein, para. 0197).
With regard to claim 1, Bain fails to explicitly disclose:
capturing, by a single supported motion capture device from a capture position, visual data of a subject as it moves between at least two distance markers in a field of vision of the motion capture device.
Kord discloses:
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. FIGS. 2-5 show different views of an example of a biomechanical skeleton 154 superimposed over an image recorded by the camera 114 of a motion capture subject 148 standing in an image calibration tool 102. The image calibration tool 102, 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. 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, para. 0032-0033).
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Bain and Kord before her, to be motivated to employ the features from Kord with Bain, including, the candidates includes 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. FIGS. 2-5 show different views of an example of a biomechanical skeleton 154 superimposed over an image recorded by the camera 114 of a motion capture subject 148 standing in an image calibration tool 102. The image calibration tool 102, 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. 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, para. 0032-0033).
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art1.
With regards to claim 2, Bain discloses:
2. A method according to claim 1 wherein the at least two distance markers that are disposed at a predetermined distance from each other (see detailed description, including, the device 130 estimates stride length of a subject in a Timed Up and Go Test (TUG) using body key points. This length is measured during which the subject is performing the three-meter walk to and from the starting position during the TUG test, para. 0193).
With regards to claim, 3, Bian discloses:
3. A method according to claim 1 wherein extracting kinematic data of the subject includes recognizing human pose points on the subject (see, detailed description, including, object tracking, 3D reconstruction techniques, cluster analysis techniques, pose estimation, sensor fusion, and modern machine learning techniques such as but not limited to a convolutional neural network (CNN), para. 0077).
With regards to claim, 4, Bian discloses:
4. A method according to claim 1 including the further step of:
constructing a biomechanical model of the motion of the subject based on the extracted kinematic data (see, detailed description, including, The final output is retrieved from a PoseNet API consisting of a 17*2 tensor which holds the 17 different key points locations. A 17*1 array is also generated which holds the confidence scores for each key point, para. 0160),
whereby the motion performance metric is formulated based on the constructed biomechanical model (see, detailed description, including, the device 130 divides the video for the physical function assessment into a batch of m frames. Each video frame has a corresponding set of k feature data. For example, m video frames will have a corresponding total of m*k feature data. Each set of k feature data is obtained by applying a geometric calculation on the full key points from a single video frame. Hence, m video frames correspond to m groups of feature data where each group has k features, para. 0166).
With regards to claim, 5, Bian discloses:
5. A method according to claim 1 wherein the motion capture device is substantially stationarily supported (see, detailed description, including, The mobile device and the camera situated therein remain stationary during the video capturing process. For example, a tripod may be used, para. 0075).
With regards to claim, 6, Bian discloses:
6. A method according to claim 1 wherein the motion capture device is a camera (see, detailed description, including, The mobile device and the camera situated therein remain stationary during the video capturing process. For example, a tripod may be used, para. 0075).
With regards to claim, 7, Bian discloses:
7. A method according to claim 6 wherein the camera is a smartphone camera (see, detailed description, including, systems for physical function assessment analysis which can be implemented using personal computing devices such as, but not limited to, smartphones, tablets, and laptops, for example, para. 0069)(a smartphone typically has a camera).
With regards to claim, 8, Bian discloses:
8. A method according to claim 6 wherein the camera is an IP camera (see, detailed description, including, systems for physical function assessment analysis which can be implemented using personal computing devices such as, but not limited to, smartphones, tablets, and laptops, for example, para. 0069, and 0072)(a smartphone typically has a camera, which include IP, internet protocol).
With regards to claim, 9, Bian discloses:
9. A method according to claim 1 wherein the motion capture device includes two cameras (see, detailed description, including, real-time human physical function (e.g., gait and posture) analysis systems that require standalone depth or high-resolution cameras dedicated to this application and mounted on top of or alongside a wall in a space (e.g., room or hallway) and the use of high-end desktop or server hardware, para. 0076).
With regards to claim, 10, Bian discloses:
10. A method according to claim 1 wherein the visual data of a subject is captured without use of wearable subject makers on the subject (see, detailed description, including, identify bounding boxes surrounding each person by performing the first computer vision technique (e.g., Single Shot Detector with MobileNet) on the input video; identify a Person of Interest (POI) and Objects of Interest (OOI) within the frames based on the user's interaction with the input video; track the POI and OOI during at least a portion of the video duration by performing the second computer vision technique on the input video and store the bounding box coordinates of the POI and OOI for each frame of the input video in a memory element, para. 0081).
With regards to claim, 11, Bian discloses:
11. A method according to claim 1 wherein the motion performance metric includes one or more of:
velocity of the subject;
acceleration of the subject;
stride length of the subject (see, detailed description, including, indicators include, but are not limited to, certain body angles that are made during mobility and balance tests, and certain gait parameters (steps, cadence, stride length), for example, para. 0185);
stride frequency of the subject; and form of the subject, wherein the motion performance metric is expressed in absolute real-world units derived from the at least two distance markers.
With regards to claim, 12, Bian discloses:
12. A method according to claim 11 wherein a plurality of motion performance metrics is formulated (see, detailed description, including, indicators include, but are not limited to, certain body angles that are made during mobility and balance tests, and certain gait parameters (steps, cadence, stride length), for example, para. 0185).
With regards to claim, 13, Bian discloses:
13. A method according to claim 1 including the further step of outputting the motion performance metric for visual display on a display device (see, Fig. 6, detailed description, including, a screen capture of an example video of an ongoing physical function assessment (e.g., selected from the various options in FIG. 3). A window 600 shows a view with a POI 610 performing a physical function assessment (e.g., Timed Up and Go) while being recorded by the device 130, para. 0104).
With regards to claim, 14, Bian discloses:
14. A method according to claim 13 wherein the display device is a smartphone (see, detailed description, including, use methods and systems for physical function assessment analysis which can be implemented using personal computing devices such as, but not limited to, smartphones, tablets, and laptops, for example. It should be understood by persons of ordinary skill in the art that the use of terms “physical function”, “mobility function” and “physical function assessment” in this disclosure refer to multiple clinically proven physical function assessment scales. In other words, at least one example embodiment described herein may be used for capturing and analyzing physical function or mobility assessments, as long as there is at least one person present in a setting (e.g., a room or a hallway) whose movements are being recorded, para. 0069).
With regards to claim, 15, Bian discloses:
15. A method according to claim 13 wherein the motion performance metric is outputted and displayed as one or more of:
a graph; a number;
a dynamically moving gauge; and
a tabular representation, (see, detailed description, including, use methods and systems for physical function assessment analysis which can be implemented using personal computing devices such as, but not limited to, smartphones, tablets, and laptops, for example. It should be understood by persons of ordinary skill in the art that the use of terms “physical function”, “mobility function” and “physical function assessment” in this disclosure refer to multiple clinically proven physical function assessment scales. In other words, at least one example embodiment described herein may be used for capturing and analyzing physical function or mobility assessments, as long as there is at least one person present in a setting (e.g., a room or a hallway) whose movements are being recorded, para. 0069).
With regards to claim, 17, Bian discloses:
With regard to claim 17, claim 17 (a system claim) recites substantially similar limitations to claim 2 (a method claim) (with the addition of a central data processing server, see, Fig. 1, item 115) and is therefore rejected using the same art and rationale set forth above.
With regards to claim, 18, Bian discloses:
18. A method according to claim 1 including the further steps of:
generating a target motion performance metric based on the formulated motion performance metric, such that the target motion performance metric represents a predefined improvement increment over the formulated motion performance metric (see, detailed description, including, m video frames will have a corresponding total of m*k feature data. Each set of k feature data is obtained by applying a geometric calculation on the full key points from a single video frame. Hence, m video frames correspond to m groups of feature data where each group has k features, para. 0166); and
generating motion performance feedback to be provided to the subject, the motion performance feedback based on the difference between the target motion performance metric and the formulated motion performance metric (see, Fig. 19, the device 130 feeds the m groups of feature data as input into a neural network such as a convolutional neural network (e.g., the fourth computer vision technique as shown in FIG. 19), and The value of n may be a predetermined proportion of m (e.g., 10% of m) or other value that may or may not change (e.g., based on desired accuracy or computational efficiency). The device 130 repeats this process to input a new set of m sets of feature data into the neural network to obtain new pose and gait classification confidence scores. This process continues until it slides to the end of the video, para. 0167-0169).
With regards to claim, 19, Bian discloses:
19. A method according to claim 1, including the initial step of:
capturing, by the motion capture device, a reference image including the at least two distance markers in the field of vision of the motion capture device at the capture position, the reference image recording respective positions of the at least two distance markers (see detailed description, including, the device 130 estimates stride length of a subject in a Timed Up and Go Test (TUG) using body key points. This length is measured during which the subject is performing the three-meter walk to and from the starting position during the TUG test, para. 0193) and such that subsequent visual data in the field of vision of the motion capture device at the capture position is captured without one or more of the at least two distance markers being in the field of vision of the motion capture device (see, detailed description, including, identify bounding boxes surrounding each person by performing the first computer vision technique (e.g., Single Shot Detector with MobileNet) on the input video; identify a Person of Interest (POI) and Objects of Interest (OOI) within the frames based on the user's interaction with the input video; track the POI and OOI during at least a portion of the video duration by performing the second computer vision technique on the input video and store the bounding box coordinates of the POI and OOI for each frame of the input video in a memory element; store the video file in the memory element; detect body joint locations within each video frame by performing the third computer vision technique (e.g., with PoseNet) on the input video; correlate body joints locations in each frame with the POI bounding box location stored in the memory element to correctly identify the POI body joints locations; and store the body joint locations of the POI for each video frame in the memory element, para. 0081).
With regards to claim, 20, Bian discloses:
20. A method according to claim 1, including the further step of:
recognizing a captured length of an object in the field of vision of the motion capture device, the object having a known real-world length (see, detailed description, including, object tracking, 3D reconstruction techniques, cluster analysis techniques, pose estimation, sensor fusion, and modern machine learning techniques such as but not limited to a convolutional neural network (CNN), para. 0077); and
mapping the known real-world length of the object to the captured length of the object (see, detailed description, including, The final output is retrieved from a PoseNet API consisting of a 17*2 tensor which holds the 17 different key points locations. A 17*1 array is also generated which holds the confidence scores for each key point, para. 0160,
wherein the motion capture device is associated with a display device and one or more of the at least two distance markers are implemented on the display device as virtual markers for marking a distance having a known real-world distance based on the mapped known real-world length of the object (see detailed description, including, the device 130 estimates stride length of a subject in a Timed Up and Go Test (TUG) using body key points. This length is measured during which the subject is performing the three-meter walk to and from the starting position during the TUG test, para. 0193).
With regard to claim 22, Bain fails to explicitly disclose:
22. (New) A method for generating a motion performance metric including the steps of:
capturing, by a single supported motion capture device from a capture position, a
reference image comprising at least two distance markers in a field of vision of the motion capture device;
capturing, by the single supported motion capture device from the capture position, visual data of a subject as it moves between the at least two distance markers in the field of vision of the motion capture device;
from the reference image and the captured visual data, extracting kinematic data of the subject; and
based on the extracted kinematic data, formulating a motion performance metric.
Kord discloses:
A method for generating a motion performance metric including the steps of:
capturing, by a single supported motion capture device from a capture position, a
reference image comprising at least two distance markers in a field of vision of the motion capture device (see, Fig. 1, and 2, and detailed description, including, 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. 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, para. 0032-0033);
capturing, by the single supported motion capture device from the capture position, visual data of a subject as it moves between the at least two distance markers in the field of vision of the motion capture device (see, Fig. 2, and 3, For a camera lens 126 of known focal length, the dimensions and angles of the image calibration tool 102 measured from an image recorded by the camera may be used to determine the separation distance 118 between the camera lens and the scale frame, para. 0032-0033);
from the reference image and the captured visual data, extracting kinematic data of the subject (see, detailed description, including, Each image is converted to a silhouette by the computer. Individual silhouette images are compared to one another by the computer to assign a location for each biomechanical reference location 152 on the biomechanical skeleton 154. 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, para. 0033); and
based on the extracted kinematic data, formulating a motion performance metric (see, detailed description, including, the motion capture subject 148 stands inside the image calibration tool 102 with hips and shoulders arranged parallel to a front plane defined by any three of the calibration markers (132, 134, 136, 138) on the front side of the frame. Alternative embodiments include different sizes and shapes of image calibration tools, each of the alternative embodiments including at least two calibration markers and at least two interconnecting struts to provide image scaling information for at least two mutually orthogonal spatial axe, para. 0034).
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Bain and Kord before her, to be motivated to employ the features from Kord with Bain, including, the candidates includes capturing, by a single supported motion capture device from a capture position, a reference image comprising at least two distance markers in a field of vision of the motion capture device (see, Fig. 1, and 2, and detailed description, including, 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. 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, para. 0032-0033);
capturing, by the single supported motion capture device from the capture position, visual data of a subject as it moves between the at least two distance markers in the field of vision of the motion capture device (see, Fig. 2, and 3, For a camera lens 126 of known focal length, the dimensions and angles of the image calibration tool 102 measured from an image recorded by the camera may be used to determine the separation distance 118 between the camera lens and the scale frame, para. 0032-0033);
from the reference image and the captured visual data, extracting kinematic data of the subject (see, detailed description, including, Each image is converted to a silhouette by the computer. Individual silhouette images are compared to one another by the computer to assign a location for each biomechanical reference location 152 on the biomechanical skeleton 154. 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, para. 0033); and
based on the extracted kinematic data, formulating a motion performance metric (see, detailed description, including, the motion capture subject 148 stands inside the image calibration tool 102 with hips and shoulders arranged parallel to a front plane defined by any three of the calibration markers (132, 134, 136, 138) on the front side of the frame. Alternative embodiments include different sizes and shapes of image calibration tools, each of the alternative embodiments including at least two calibration markers and at least two interconnecting struts to provide image scaling information for at least two mutually orthogonal spatial axe, para. 0034).
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art2.
With regard to claim 23, Bain fails to explicitly disclose:
23. (New) A system according to claim 17, wherein the central data processing server is further configured to:
construct a biomechanical model of the motion of the subject based on the extracted kinematic data, wherein the motion performance metric is formulated based on the constructed biomechanical model.
Kord discloses:
A system according to claim 17, wherein the central data processing server is further configured to:
construct a biomechanical model of the motion of the subject based on the extracted kinematic data, wherein the motion performance metric is formulated based on the constructed biomechanical model (see, detailed description, including, the motion capture subject 148 stands inside the image calibration tool 102 with hips and shoulders arranged parallel to a front plane defined by any three of the calibration markers (132, 134, 136, 138) on the front side of the frame. Alternative embodiments include different sizes and shapes of image calibration tools, each of the alternative embodiments including at least two calibration markers and at least two interconnecting struts to provide image scaling information for at least two mutually orthogonal spatial axe, para. 0034).
It would have been obvious to one having ordinary skill at the time the invention was filed, and having the teachings of Bain and Kord before her, to be motivated to employ the features from Kord with Bain, including, the candidates includes capturing, by a single supported motion capture device from a capture position, a reference image comprising at least two distance markers in a field of vision of the motion capture device (see, Fig. 1, and 2, and detailed description, including, 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. 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, para. 0032-0033);
capturing, by the single supported motion capture device from the capture position, visual data of a subject as it moves between the at least two distance markers in the field of vision of the motion capture device (see, Fig. 2, and 3, For a camera lens 126 of known focal length, the dimensions and angles of the image calibration tool 102 measured from an image recorded by the camera may be used to determine the separation distance 118 between the camera lens and the scale frame, para. 0032-0033);
from the reference image and the captured visual data, extracting kinematic data of the subject (see, detailed description, including, Each image is converted to a silhouette by the computer. Individual silhouette images are compared to one another by the computer to assign a location for each biomechanical reference location 152 on the biomechanical skeleton 154. 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, para. 0033); and
based on the extracted kinematic data, formulating a motion performance metric (see, detailed description, including, the motion capture subject 148 stands inside the image calibration tool 102 with hips and shoulders arranged parallel to a front plane defined by any three of the calibration markers (132, 134, 136, 138) on the front side of the frame. Alternative embodiments include different sizes and shapes of image calibration tools, each of the alternative embodiments including at least two calibration markers and at least two interconnecting struts to provide image scaling information for at least two mutually orthogonal spatial axe, para. 0034).
Therefore, a rationale to support a conclusion that a claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art3.
Allowable Subject Matter
Claim 21 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
21. (New) A method according to claim 1, including the further steps of:
recognizing a captured length of a reference object located in the field of vision of the motion capture device, the reference object having a known real-world length;
using the known real-world length of the reference object to derive a pixel-to-real-world scale factor; and
expressing the motion performance metric in absolute real-world units using the pixel-to-real-world scale factor.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM D. TITCOMB whose telephone number is (571)270-5190. The examiner can normally be reached 9:30 AM - 6:30 PM (M-F).
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WILLIAM D. TITCOMB
Primary Examiner
Art Unit 2178
/WILLIAM D TITCOMB/Primary Examiner, Art Unit 2178 6-2-2026
1 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).
2 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).
3 1 KSR International Co. v. Teleflex Inc., 127 S.Ct. 1727, 82 U.S.P.Q.2d 1385 (2007).
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