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 3/26/26 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
(pages 7-11 made by Applicant’s Representative – filed 12/30/25)
Applicant's arguments (pages 7-10) regarding the 35 USC 103 rejection to independent claims (1 & 13) have been fully considered and are persuasive in view of the recent claim amendments. Therefore, the rejection has been withdrawn.
However, upon further consideration, a new ground(s) of rejection is made in view of newly cited NPL art GSCHWINDT MIRKO ET AL (see attached NPL titled: "Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography"), which is taken in obviousness combination with the previous applied references – FRITSCH (US 20080316368) & GOVE (US 20160127641) – which are still considered pertinent to the claimed invention as respectively noted under “Closest Prior Art” section and “35 USC 103 Rejection” section detailed below.
Therefore, Applicant's arguments (page 7-10) regarding the 35 USC 103 rejection to independent claims (1 & 13) have been considered but are moot because the arguments do not apply to the combination of references being used in the current rejection. See 35 USC § 103 rejection below.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
Closest Prior Art
The prior art (cited on PTO-892) is considered pertinent to applicant's disclosure. Among these, the following references are considered to be the closest, collectively disclosing the state of the art concerned with a method for controlling a camera-robot for shooting a video sequence.
FRITSCH (US 20080316368) – applied to 35 USC 103 rejection, see Abstract, Fig. 1, 3, 5, and para [0001, 0009, 0012, 0015, 0023-25, 0037-38, 0052-53, and 0055].
GSCHWINDT MIRKO ET AL – applied to 35 USC 103 rejection, see attached NPL titled: "Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography", 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), IEEE, November 3, 2019, pages 1107-1114. See Abstract, Fig. 2, 4 & 7, and pages 1107-1114. Shown Below are Figures 2 & 7, and a portion from page 1109, left column, lines 3-7 and Section III.
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GOVE (US 20160127641) – applied to 35 USC 103 rejection, see Fig. 2A (steps 1-4), para [0005, 0038, 0040-44, 0052, 0056, 0059, 0190, 0198] and Abstract, discloses a Media Capture Device (MCD) which may have a robotic arm for horizontal & vertical movement/rotation of a camera (para [0056 & 0059]), and in the preferred embodiment the MCD is a camera drone having autonomous intelligence using machine learning to determine control parameters to move / capture interesting images/videos of subjects/actors in a scene.
GOVE also discloses that the autonomous intelligence of camera drone used in aerial cinematography could be suitably down implemented into various other conventional camera devices (para [0005, 0040-44, 0052, 0190]) that includes a camera moved on a surface – see para [0052] states “…Many of the techniques and algorithms used by an MCD can also apply to next generation conventional, stationary, or semi-autonomous cameras or other media capture components. …Cameras with this intelligence can also reposition themselves within small domains, like moving around a surface (e.g., a table, floor, or wall), rather than being in free flight. Some of the advanced capture technologies may have more limited use, but, likewise, many of the fundamental concepts of autonomous photography (e.g., with embedded “machine thinking” to automatically capture media) described herein may still apply, with substantial benefit in terms of likability of captured photographs and video”.
YOO HWAN SOO (KR 2016 0072481 A) – (English translation attached), see Abstract, Fig. 9-11 discloses a camera dolly with a chassis moved on a ground surface and comprises a holder mounted with a camera for tilt, rotation, and lift movements (para [0066]). The camera dolly has automatic navigation capabilities (para [0073]) and a remote display user interface screen 100 allows a user to input movement controls, designate a route depending on the nature of the shooting environment, and select a categorization by dividing a predefined universal route (para [0050-51]).
NOTE: Examiner welcomes INTERVIEW(s) to discuss the instant application’s claimed invention as it corresponds to the specification embodiments, as well as, discussing the similarities/differences taught/not taught by prior art. In the interest of compact prosecution, Applicant’s arguments/amendments should not only address the cited closest art applied/relied on in the 35 USC 103 rejection (below), but also address the other cited closest art not applied/relied on.
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed 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 GSCHWINDT MIRKO ET AL (see attached NPL) 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. 5 in view of Fig. 1 & 3, para [0001, 0009, 0012, 0015, 0023-25, 0037-38, 0052-53, and 0055]), the camera robot comprising:
a chassis that can be moved on a surface (Fig. 3 & 5, para [0037-38, 0052-53, 0055]: chassis 32 with wheels 33-34);
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 [0012-13, 0052-53, 0055]: camera 3 is held by an articulated-arm robot 8 having six axes of rotation);
and a control unit configured to control the chassis and/or the holding device (Fig. 3 & 5, controller as disclosed in para [0012-13 and 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 for controlling the chassis and/or the holding device 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 GSCHWINDT, discloses a Robotic Movie Director with the ability of Learning Artistic Principles for Aerial Cinematography (see Title, Abstract, Pages 1107-1109, 1111, Fig. 2 and Fig. 7). This reference is a camera drone (Fig. 7: drone with support body for a camera, LIDAR, and computer) which is considered to at least teach “determining a characteristic shooting scene, and determining control parameters of the control unit for controlling” a camera drone “depending on the determined characteristic shooting scene” over the teachings on pages 1107-1109 in view of Fig. 2. These citations include determining a shot mode / different shot types (i.e. ACTION) for different situations, which defines a cinematographic path used to optimize a trajectory for following the actor, keeping the actor in view within desired shot angle limits, keeping the shots interesting, and avoiding collision with obstacles (see page 1109, left column, lines 3-7 and Section III) – which require determining control parameters used to both navigate the camera drone within the environment and capture the desired shots of the actor.
The teachings of GSCHWINDT differs from Applicant’s claimed invention in that GSCHWINDT’s teachings are applied to a camera drone, and therefore, the taught chassis which holds the camera moves above the surface in aerial fashion rather than Applicant’s claimed “chassis that can be moved on a surface” which is a ground moving camera robot.
Given GSCHWINDT’s teachings for autonomous aerial cinematography, the said claimed features “for controlling the chassis and/or the holding device” for a “chassis that can be moved on a surface” would have been considered obvious to implement into FRITSCH’s ground moving camera robot using suitable modification understood by one of ordinary skill in the art according to the differences / capabilities between a ground moving camera robot and a aerial moving camera drone for the motivated reason of producing a camera robot with autonomous capabilities for determining control parameters for controlling movement of the chassis and/or the holding device depending on the determined characteristic shooting scene.
For example, to show further evidence that it was previously known that the autonomous intelligence of camera drone used in aerial cinematography could be suitably down implemented into a camera moved on a surface, see related robotics prior art GOVE (see Fig. 2A (steps 1-4) and para [0005, 0038, 0040-44, 0052, 0056, 0059, 0190, 0198]). GOVE discloses a Media Capture Device (MCD) which may have a robotic arm for horizontal & vertical movement/rotation of a camera (para [0056 & 0059]), and in the preferred embodiment the MCD is a camera drone having autonomous intelligence using machine learning to determine control parameters to move / capture interesting images/videos of subjects/actors in a scene.
GOVE also discloses that the autonomous intelligence of camera drone used in aerial cinematography could be suitably down implemented into various other conventional camera devices (para [0005, 0040-44, 0052, 0190]) that includes a camera moved on a surface – see para [0052] states “…Many of the techniques and algorithms used by an MCD can also apply to next generation conventional, stationary, or semi-autonomous cameras or other media capture components. …Cameras with this intelligence can also reposition themselves within small domains, like moving around a surface (e.g., a table, floor, or wall), rather than being in free flight. Some of the advanced capture technologies may have more limited use, but, likewise, many of the fundamental concepts of autonomous photography (e.g., with embedded “machine thinking” to automatically capture media) described herein may still apply, with substantial benefit in terms of likability of captured photographs and video”.
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 GSCHWINDT in view of 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 a ground-driven robot’s navigation capabilities and camera control capabilities based on a determined characteristic shooting scene in the analogous art of a robotic vehicle mounted with a digital camera.
As per CLAIM 2, FRITSCH in view of GSCHWINDT in view of GOVE teaches the method according to claim 1, wherein the characteristic shooting scene is determined by evaluating a user input (This feature is obvious over the prior art combination discussed in claim 1. FRITSCH discloses a trajectory model to be executed can be activated by the user as needed by manually selecting it on an operating device coupled with the controller per para [0017 & 0020]. GOVE discloses the MCD may receive user commands and social feedback per Fig. 2A: steps 1-4. GSCHWINDT discloses in Section I on pages 1107-1108 that conventionally to optimize cinematographic trajectories, this process further needs input from a user to determine the “shot mode” which defines the cinematographic path).
As per CLAIM 3, FRITSCH in view of GSCHWINDT 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 (This feature is obvious over the prior art combination discussed in claim 1, in further view of GSCHWINDT’s teachings on pages 1107-1109 in view of Fig. 2 and Abstract in view of Section I. These citations include determining a shot mode / different shot types (i.e. ACTION) for different situations, which defines a cinematographic path used to optimize a trajectory for following the actor, keeping the actor in view within desired shot angle limits, keeping the shots interesting, and avoiding collision with obstacles (see page 1109, left column, lines 3-7 and Section III) – which require determining control parameters used to both navigate the camera drone within the environment and capture the desired shots of the actor.).
As per CLAIM 4, FRITSCH in view of GSCHWINDT 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 (This feature is obvious over the prior art combination discussed in claim 1, in further view of GSCHWINDT’s teachings on pages 1107-1109 in view of Fig. 2. These citations include determining a shot mode / different shot types (i.e. ACTION) for different situations, which defines a cinematographic path used to optimize a trajectory for following the actor, keeping the actor in view within desired shot angle limits, keeping the shots interesting, and avoiding collision with obstacles (see page 1109, left column, lines 3-7 and Section III) – which require determining control parameters used to both navigate the camera drone within the environment and capture the desired shots of the actor.).
As per CLAIM 5, FRITSCH in view of GSCHWINDT 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 (This feature is obvious over the prior art combination discussed in claim 1, in further view of GSCHWINDT’s teachings on pages 1107-1109 in view of Fig. 2. These citations include determining a shot mode / different shot types (i.e. ACTION) for different situations, which defines a cinematographic path used to optimize a trajectory for following the actor, keeping the actor in view within desired shot angle limits, keeping the shots interesting, and avoiding collision with obstacles (see page 1109, left column, lines 3-7 and Section III) – which require determining control parameters used to both navigate the camera drone within the environment and capture the desired shots of the actor.).
As per CLAIM 6, FRITSCH in view of GSCHWINDT 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 (This feature is obvious over the prior art combination discussed in claim 1. FRITSCH discloses a vehicle 32 mounted with a robotic arm 8 for holding/moving a camera 3 along 6 axes of rotation – see Fig. 5 and para [0015]. GSCHWINDT teaches determining navigation / movement of the camera, wherein control parameters are determined by a method based on machine learning and may use training data – see Fig. 2 in view of section III, and pages 1107-1110 per pages 1107-1109. GOVE’s taught MCD may also use machine learning per Abstract, Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052], wherein the MCD may have a robotic arm “holder” for adjusting vertical position and/or the tilt angle of the camera per para [0056 & 0059]).
As per CLAIM 7, FRITSCH in view of GSCHWINDT 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 (This feature is obvious over the prior art combination discussed in claim 1. FRITSCH discloses a vehicle 32 mounted with a robotic arm 8 for holding/moving a camera 3 along 6 axes of rotation – see Fig. 5 and para [0015]. GSCHWINDT teaches determining navigation / movement of the camera, wherein control parameters are determined by a method based on machine learning and may use training data – see Fig. 2 in view of section III, and pages 1107-1110 per pages 1107-1109. GOVE’s taught MCD may also use machine learning per Abstract, Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052], wherein the MCD may have a robotic arm “holder” for adjusting vertical position and/or the tilt angle of the camera per para [0056 & 0059]).
As per CLAIM 8, FRITSCH in view of GSCHWINDT 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 (This feature is obvious over the prior art combination discussed in claim 1. FRITSCH discloses a vehicle 32 mounted with a robotic arm 8 for holding/moving a camera 3 along 6 axes of rotation – see Fig. 5 and para [0015]. GSCHWINDT teaches determining navigation / movement of the camera, wherein control parameters are determined by a method based on machine learning and may use training data – see Fig. 2 in view of section III, and pages 1107-1110 per pages 1107-1109. GOVE’s taught MCD may also use machine learning per Abstract, Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052], wherein the MCD may have a robotic arm “holder” for adjusting vertical position and/or the tilt angle of the camera per para [0056 & 0059]).
As per CLAIM 9, FRITSCH in view of GSCHWINDT 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 (This feature is obvious over the prior art combination discussed in claim 1. FRITSCH discloses a vehicle 32 mounted with a robotic arm 8 for holding/moving a camera 3 along 6 axes of rotation – see Fig. 5 and para [0015]. GSCHWINDT teaches detecting objects in the environment, obstacles to avoid collision with, and navigation/movement of the camera, wherein control parameters are determined by a method based on machine learning and may use training data – see Fig. 2 in view of section III, and pages 1107-1110 per pages 1107-1109. GOVE’s taught MCD may also use machine learning per Abstract, Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052], wherein the MCD may have a robotic arm “holder” for adjusting vertical position and/or the tilt angle of the camera per para [0056 & 0059]).
As per CLAIM 10, FRITSCH in view of GSCHWINDT in view of GOVE teaches the method according to claim 1. Regarding the limitation: “wherein the objects located in the environment of the camera robot are detected by using a LIDAR sensor”, GOVE teaches detecting objects in the environment (Fig. 2A: steps 1 & 2), and GSCHWINDT teaches detecting objects in the environment and obstacles to avoid collision with which could be interpreted to be objects (pages 1107-1109) wherein the navigation may use a LIDAR sensor in Fig. 7, which largely seems to imply that the LIDAR is also being used to detect objects.
It may not be explicitly clear that the LIDAR sensor is also used to detect objects, however, this feature is considered obvious and conventional. Therefore, Official Notice (MPEP § 2144.03) is taken that both the concepts and advantages of using LIDAR to detect a distance to objects and/or objects within proximity is well known and expected in the art. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use one LIDAR sensor configured to detect objects located in the environment of the camera robot for the MOTIVATED REASON of improving navigation capabilities and capturing capabilities in the analogous art of a vehicle mounted with a digital camera.
As per CLAIM 11, FRITSCH in view of GSCHWINDT 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 (This feature is obvious over the prior art combination discussed in claim 1. FRITSCH discloses a vehicle 32 mounted with a robotic arm 8 for holding/moving a camera 3 along 6 axes of rotation – see Fig. 5 and para [0015]. GSCHWINDT teaches control parameters are determined by a method based on machine learning and may use training data – see Fig. 2 in view of section III, and pages 1107-1110. GOVE’s taught MCD may also use machine learning per Abstract, Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052], wherein the MCD may have a robotic arm “holder” for adjusting vertical position and/or the tilt angle of the camera per para [0056 & 0059]).
As per CLAIM 12, FRITSCH in view of GSCHWINDT 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 (This feature is obvious over the prior art combination discussed in claim 1. FRITSCH discloses a vehicle 32 mounted with a robotic arm 8 for holding/moving a camera 3 along 6 axes of rotation – see Fig. 5 and para [0015]. GSCHWINDT teaches control parameters are determined by a method based on machine learning and may use training data – see Fig. 2 in view of section III, and pages 1107-1110. GOVE’s taught MCD may also use machine learning per Abstract, Fig. 2A (steps 1-4), para [0005, 0038, 0040, 0052], wherein the MCD may have a robotic arm “holder” for adjusting vertical position and/or the tilt angle of the camera per para [0056 & 0059]).
As per INDEPENDENT CLAIM 13, FRITSCH in view of GSCHWINDT 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, and/or the holding device, wherein the control unit is configured to determine the control parameters of the control unit for controlling the chassis and/or the holding device 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 GSCHWINDT 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. FRITSCH, Fig. 5 in view of Fig. 1B: see robotic arm 8 with camera 3 for adjusting pan A7 & tilt A8 & roll of the camera as discussed in para [0015]. GOVE also discloses that the MCD may have a robotic arm for adjusting vertical position and/or the tilt angle of the camera per para [0056 & 0059]).
As per CLAIM 15, FRITSCH in view of GSCHWINDT in view of GOVE teaches the camera robot according to claim 13. Regarding the limitation: “wherein at least one LIDAR sensor configured to detect objects located in the environment of the camera robot”, GOVE teaches detecting objects in the environment (Fig. 2A: steps 1 & 2), and GSCHWINDT teaches detecting objects in the environment and obstacles to avoid collision with which could be interpreted to be objects (pages 1107-1109) wherein the navigation may use a LIDAR sensor in Fig. 7, which largely seems to imply that the LIDAR is also being used to detect objects.
It may not be explicitly clear that the LIDAR sensor is also used to detect objects, however, this feature is considered obvious and conventional. Therefore, Official Notice (MPEP § 2144.03) is taken that both the concepts and advantages of using LIDAR to detect a distance to objects and/or objects within proximity is well known and expected in the art. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use one LIDAR sensor configured to detect objects located in the environment of the camera robot for the MOTIVATED REASON of improving navigation capabilities and capturing capabilities in the analogous art of a vehicle mounted with a digital camera.
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.
Contact Information
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/AKSHAY TREHAN/
Examiner, Art Unit 2639
/TWYLER L HASKINS/Supervisory Patent Examiner, Art Unit 2639
