DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stenzler et al. (hereinafter ‘Stenzler’, CA 2949522) in view of Worrall et al. (hereinafter ‘Worrall’, Pub. No. 2019/0354753).
Regarding claims 1, 11 and 18, Stenzler teaches a ride control system for controlling operation of an amusement park ride having a ride station area under supervision of one or more ride operators ([0021]; [0022]), the ride control system comprising:
a vision system configured to capture images of one or more of the one or more ride operators at one or more of locations within the ride station area ([0043]; [0045]; [0053]; [0073]; [0113]; [0114]; [0117]; [0120]-[0125]); and
a ride control processor coupled to receive one or more images from the vision system, the ride control processor comprising:
recognize one or more valid gestures within the one or more images, where a valid gesture corresponds to a gesture from at least one of the one or more ride operators ([0113]-[0125]), and program logic configured to process the one or more valid gestures within the images to enable a ride operation ([0113]-[0125]).
On the other hand, Stenzler does not explicitly teach the that the module for gesture recognition is machine learned.
However, in an analogous art, Worrall teaches a system that monitors/tracks procedure compliance (including, among other instances, monitoring ride operators/riders of amusement) using sensor obtained data (i.e. video cameras) and AI/ML analytics platform ([0074]-[0079]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Stenzler’s invention with Worrall’s feature of using machine learning for the benefit of faster automatic recognition of events/gestures to avoid ride accidents, etc.
Regarding claims 2 and 12, Stenzler and Worrall teach wherein the machine-learned module is configured to recognize one or more valid gestures within the one or more images by being trained to:
identify a gesture within the one or more images corresponding to a gesture within a set of programmed gestures; and determine the identified gesture is made by at least one of the one or more ride operators (Stenzler: [0121]-[0125]. Worrall: Figs. 6-10; [0049]; [0055]; [0057]; [0063]; [0074]; [0079]).
Regarding claim 3, Stenzler and Worrall teach wherein the machine-learned module is trained to identify a gesture within the one or more images corresponding to a gesture within the set of programmed gestures based on a labeled dataset of images of programmed gestures captured at one or more locations within the ride station area (Worrall: [0072]-[0076]).
Regarding claim 4, Stenzler and Worrall teach wherein images of programmed gestures within the labeled dataset are captured by the vision system (Worrall: [0072]-[0076]).
Regarding claim 5, Stenzler and Worrall teach wherein the machine-learned module is trained to determine the identified gesture is made by at least one of the one or more ride operators based on a labeled dataset of images of a feature associated with the one or more ride operators (Stenzler: [0113]-[0125]. Worrall: [0072]-[0076]).
Regarding claims 6, 9, 13 and 16, Stenzler and Worrall teach wherein the program logic is configured to enable a ride operation when: the one or more valid gestures within the one or more images comprise a plurality of a same valid gesture from at least two of the one or more ride operators (Stenzler: [0125]. Worrall: [0084]; [0088]).
Regarding claims 7, 10, 14 and 17, Stenzler and Worrall teach wherein the program logic is configured to enable a ride operation when: the one or more valid gestures within the one or more images comprise a plurality of a same valid gesture from at least two of the one or more ride operators (Stenzler: [0125]. Worrall: [0084]; [0088]), and each of the plurality of the same valid gesture is present within the one or more images for a threshold duration (Stenzler: [0055]. Worrall: [0034]; [0113]).
Regarding claims 8 and 15, Stenzler and Worrall teach wherein the ride control processor is coupled to an operator control console, and the program logic is configured to enable a ride operation when: the one or more valid gestures within the one or more images comprise a plurality of a same valid gesture from at least two of the one or more ride operators (Stenzler: [0125]. Worrall: [0084]; [0088]), each of the plurality of the same valid gesture is present within the one or more images for a threshold duration (Stenzler: [0055]. Worrall: [0034]; [0113]), and a corresponding operation signal is received from the operator control console (Stenzler: [0125]).
Claim(s) 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stenzler et al. (hereinafter ‘Stenzler’, CA 2949522) in view of Worrall et al. (hereinafter ‘Worrall’, Pub. No. 2019/0354753) in further view of Ulutan et al. (hereinafter ‘Ulutan’, Patent No. 12,236,705).
Regarding claim 19, although Stenzler and Worrall teach wherein the first model trained to identify a gesture corresponding to a gesture within the set of programmed gestures based on a labeled dataset of images of programmed gestures captured at one or more locations within the ride station area ((Stenzler: [0121]-[0125]. Worrall: Figs. 6-10; [0049]; [0055]; [0057]; [0063]; [0074]; [0079]), Stenzler and Worrall do not explicitly teach that the first model comprises a convolutional neural network.
However, in an analogous art, Ulutan teaches a system for recognizing people gestures by analyzing video content. Ulutan teaches using a convolutional neural network as the trained model (col. 12 line4 to col. 13 line 26). Additionally, Ulutan teaches using a dataset to recognize gestures through classified data as to recognize different gesture meanings on different areas (col. 5 line 7 to col. 6 line 17).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Stenzler and Worrall’s invention with Ulutan’s feature of using convolutional neural network as first model for the benefit of obtaining high accuracy and robustness to variations in input data.
Regarding claim 20, although Stenzler and Worrall teach wherein the trained model to determine the gesture is made by at least one of the one or more ride operators based on a labeled dataset of images of a feature associated with the one or more ride operators (Stenzler: [0113]-[0125]. Worrall: [0072]-[0076]), Stenzler and Worrall do not explicitly teach that the first model comprises a convolutional neural network.
However, in an analogous art, Ulutan teaches a system for recognizing people gestures by analyzing video content. Ulutan teaches using a convolutional neural network as the trained model (col. 12 line4 to col. 13 line 26). Additionally, Ulutan teaches using a dataset to recognize gestures through classified data as to recognize different gesture meanings on different areas (col. 5 line 7 to col. 6 line 17).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Stenzler and Worrall’s invention with Ulutan’s feature of using convolutional neural network as first model for the benefit of obtaining high accuracy and robustness to variations in input data.
Conclusion
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/OMAR S PARRA/ Primary Examiner, Art Unit 2421
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