N O N - F I N A L A C T I O N
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/31/23 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over FRITSCH (US 20080316368) in view of SUO (US 20180198988) in view of GOVE (US 20160127641) -- hereafter, termed as shown “underlined”.
As per INDEPENDENT CLAIM 1, FRITSCH teaches a method for controlling a camera robot for shooting a video sequence (Fig. 1-5, para [0001, 0049-55]), the camera robot comprising:
a chassis that can be moved on a surface (Fig. 3 & 5, para [0055]);
a camera for shooting the video sequence (Fig. 3 & 5, para [0055]: camera 3);
a holding device for connecting the camera to the chassis as well as for orienting the camera relative to the chassis (Fig. 3 & 5, para [0055]: robot 8);
and a control unit configured to control the chassis, the holding device and/or the camera (Fig. 3 & 5, para [0055]).
FRITSCH’s disclosure is silent to “the method comprising the following steps: determining a characteristic shooting scene; and determining control parameters of the control unit depending on the determined characteristic shooting scene”.
However, the said underlined limitation was known in the related art for a digital camera. For example, prior art SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract.
Thus, when considering the collective knowledge bestowed by each applied prior art, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to COMBINE the teachings of SUO and GOVE into suitable modification with the teachings of FRITSCH to produce Applicant’s claimed invention with the structural arrangement / functional configuration stated in said underlined limitation for the MOTIVATED REASON of improving the robot’s navigation capabilities based a shooting scene type in the analogous art of a digital camera.
As per CLAIM 2, FRITSCH in view of SUO in view of GOVE teaches the method according to claim 1, wherein the characteristic shooting scene is determined by evaluating a user input (See prior art combination in claim 1. SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract).
As per CLAIM 3, FRITSCH in view of SUO in view of GOVE teaches the method according to claim 1, wherein the characteristic shooting scene is determined by a method based on machine learning (See prior art combination in claim 1. SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract).
As per CLAIM 4, FRITSCH in view of SUO in view of GOVE teaches the method according to claim 1, wherein, when determining the characteristic shooting scene, a system previously trained with training data is used which was trained during a training process with image data or video sequences and corresponding labels which identify the affiliation of the image data or video sequences to the shooting scenes (See prior art combination in claim 1. SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract).
As per CLAIM 5, FRITSCH in view of SUO in view of GOVE teaches the method according to claim 1, wherein the characteristic shooting scene comprises one of the following shooting scenes: action, horror, romance, dance, presentation, interview, or panel discussion (See prior art combination in claim 1. SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract).
As per CLAIM 6, FRITSCH in view of SUO in view of GOVE teaches the method according to claim 1, wherein the control parameters are determined depending on the determined characteristic shooting scene by a method based on machine learning, wherein in particular a system previously trained with training data is used which was trained during a training process with image data or video sequences as well as shooting scene information and control parameters (See prior art combination in claim 1. SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract).
As per CLAIM 7, FRITSCH in view of SUO in view of GOVE teaches the method according to claim 1, wherein determining the control parameters comprises determining chassis control parameters which are used to control the movement of the chassis on the surface along a determined route (See prior art combination in claim 1. SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract).
As per CLAIM 8, FRITSCH in view of SUO in view of GOVE teaches the method according to claim 1, wherein chassis control parameters are determined by a method based on machine learning, wherein a system previously trained with training data is used which was trained during a training process with image data or video sequences as well as chassis control parameters (See prior art combination in claim 1. SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract).
As per CLAIM 9, FRITSCH in view of SUO in view of GOVE teaches the method according to claim 1, wherein objects located in the environment of the camera robot are detected, and wherein chassis control parameters are determined depending on the detected objects and/or their position (See prior art combination in claim 1. SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract).
As per CLAIM 10, FRITSCH in view of SUO in view of GOVE teaches the method according to claim 1, wherein the objects located in the environment of the camera robot are detected by using a LIDAR sensor (GOVE, para [0168]).
As per CLAIM 11, FRITSCH in view of SUO in view of GOVE teaches the method according to claim 1, wherein the holding device is configured to adjust the position of the camera along a vertical axis and/or a tilt angle of the camera, and that determining the control parameters comprises determining holding device control parameters used to control the position of the camera along a vertical axis and/or the tilt angle of the camera (See prior art combination in claim 1. SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract).
As per CLAIM 12, FRITSCH in view of SUO in view of GOVE teaches the method according to claim 1, wherein the holding device control parameters are determined by a method based on machine learning, wherein a system previously trained with training data is used which was trained during a training process with image data or video sequences as well as holding device control parameters (See prior art combination in claim 1. SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract).
As per INDEPENDENT CLAIM 13, FRITSCH in view of SUO in view of GOVE teaches a camera robot for shooting a video sequence, comprising a chassis that can be moved on a surface; a camera for shooting the video sequence; a holding device for connecting the camera to the chassis as well as for orienting the camera relative to the chassis; and a control unit configured to control the chassis, the holding device and/or the camera, wherein the control unit is configured to determine the control parameters of the control unit depending on a currently available characteristic shooting scene (This claim is rejected for same reasons as claim 1).
As per CLAIM 14, FRITSCH in view of SUO in view of GOVE teaches the camera robot according to claim 13, wherein the holding device is configured to adjust the vertical position of the camera and/or the tilt angle of the camera (See prior art combination in claim 1. SUO, discloses this feature per Fig. 6 and para [0050], [0063], [0064] and [0078] and related robotics prior art GOVE discloses these features using machine learning and user input per Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052] and Abstract).
As per CLAIM 15, FRITSCH in view of SUO in view of GOVE teaches the camera robot according to claim 13, wherein at least one LIDAR sensor configured to detect objects located in the environment of the camera robot (GOVE, para [0168]).
Contact Information
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/AKSHAY TREHAN/
Examiner, Art Unit 2639
/TWYLER L HASKINS/Supervisory Patent Examiner, Art Unit 2639
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