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 .
Status of Claims
Claims 1, 12 and 14 are amended. Claims 2 and 15 are cancelled.
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, 9, 10, 12, 13, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over GRUTESER (No. US-20210110191-A1 “Gruteser”) in view of MENG (No. US-20230326204-A1 “Meng”).
Regarding claim 1, Gruteser teaches “A system for reducing latency and bandwidth usage in reality devices comprising:” (System; Para 0046); (save bandwidth and thereby reduce latency; Para 0046); (AR device; Para 0046);
“a reality device comprising a camera to operably capture a frame of a view external to a vehicle; and” (a frame captured by the camera from the AR device; Para 0056); (detecting surrounding vehicles; Para 0032);
“one or more processors operable to:” (one processor being dedicated to the functions of the AR device; Para 0071);
“send the frame to an edge server;” (frame captures performed by the AR device 12, as well as the transmission to and processing of such frames by the edge cloud device 14; Para 0048); (Fig. 1, shows capture frame n of the AR device to receive frame n in the edge cloud);
“instruct the reality device to render a mixed reality environment with the object detection data.” (the system requires only 2.24 ms latency and less than 15% resources on the AR device, which leaves the remaining time between frames to render high quality virtual elements for high quality AR/MR experience; Para0047; object detection, Para 0056); (the AR device to render high-quality virtual overlays; Para 0046);
However, while Gruteser fails to teach “receive object detection data from the edge server, wherein the object detection data comprises object information in the frame, wherein a size of the object detection data received from the edge server is smaller than a size of the frame sent to the edge server; and”
Meng teaches “receive object detection data from the edge server, wherein the object detection data comprises object information in the frame, wherein a size of the object detection data received from the edge server is smaller than a size of the frame sent to the edge server; and” (send, to a server, the first compressed image frame. ... receive, from the server, object detection results that identify locations of objects depicted in the first image frame; Para 0004); (object detection results comprise, for each detected object, four floating point values that define a rectangle (e.g., bounding box) in the image frame that encompasses the detected object, and a prediction confidence for the detected object; Para 0024); (Each frame, the camera 18 captures an image at the particular resolution of the camera 18. The resolution may be, for example, HD, 4 K, or any other resolution; Para 0026); Meng discloses representing the returned object detection information with a small set of numerical object descriptors rather than transmitting an image frame back to the AR device. Meng also discloses that the image frame of the image data generated by the camera sensor at the resolution of the sensor. This contains image information of the scene. Thus, Meng teaches returning numerical object detection information and for each detected object, the information consists of four bounding box points and confidence prediction. It would be obvious to that the object detection metadata would contain less data than the corresponding image form the results generated of the scene. An image frame contains image information for the captured scene of the camera’s resolution and the returned detection results contains only four bounding box points within the image frame for the detected object. An ordinary person skilled in the art would be able to recognize that transmitting such compact metadata, like four floating point values of a bounding box coordinates and confidence information per detected object would require less data than transmitting the corresponding image frame data used as the input to object detection.
Gruteser and Meng are analogous art as both of them are related to AR device, and object detection.
The motivation for the above is to reduce amount of data transmitted and bandwidth usage while accurate object detection results.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser by receive object detection data from the edge server, wherein the object detection data comprises object information in the frame, wherein a size of the object detection data received from the edge server is smaller than a size of the frame sent to the edge server as taught by Meng.
Regarding claim 9, Gruteser further teaches “The system of claim 1, wherein the one or more processors are further operable to superimpose the object detection data onto a real-world view.” (the AR device, which leaves the remaining time between frames to render high quality virtual elements for high quality AR/MR experience; Para 0047);
(Fig. 4A-4C showcase the object detection data of the objects in the real-world view.)
Regarding claim 10, Gruteser further teaches “The system of claim 9, wherein the object detection data are superimposed onto a vision of a user or a current frame.” (the AR device to render high-quality virtual overlays; Para 0046);
(the RoI can be derived as the area that a user chooses to look at; 0072);
(Fig. 4A-4C showcase the object detection data of the objects in the current frame.)
Regarding claim 12, Gruteser teaches “A server for reducing latency and bandwidth usage in reality devices comprising:” (the server; Para 0094); (save bandwidth and thereby reduce latency; Para 0046); (AR device; Para 0046);
“one or more processors operable to:” (one processor being dedicated to the functions of the AR device; Para 0071);
“receive a frame of a view external to a vehicle from a reality device comprising a camera to operably capture the frame;” (a frame captured by the camera from the AR device; Para 0056);
(frame captures performed by the AR device 12, as well as the transmission to and processing of such frames by the edge cloud device 14; Para 0048); (Fig. 1, shows capture frame n of the AR device to receive frame n that was captured by the camera to the edge cloud); (detecting surrounding vehicles; Para 0032);
“send the object detection data to the reality device to render a mixed reality environment with the object detection data by the reality device.” (Fig. 1 Showcases T3 sending the detection results of object to T4 then T5 to be render in the MR.);
(the system requires only 2.24 ms latency and less than 15% resources on the AR device, which leaves the remaining time between frames to render high quality virtual elements for high quality AR/MR experience; Para0047); (the AR device to render high-quality virtual overlays; Para 0046);
However, while Gruteser fails to teach “generate object detection data, wherein the object detection data comprises information about objects in the frame, wherein a size of the generated object detection data is smaller than a size of the frame received from the reality device”.
Meng teaches “generate object detection data, wherein the object detection data comprises information about objects in the frame, wherein a size of the generated object detection data is smaller than a size of the frame received from the reality device; and” (Object detection is the identification of objects in a scene viewed by a user; Para 0018); (The server 20 sends, to the AR device 12, the object detection results 47 generated by the object detection MLM; Para 0037); (send, to a server, the first compressed image frame. ... receive, from the server, object detection results that identify locations of objects depicted in the first image frame; Para 0004); (object detection results comprise, for each detected object, four floating point values that define a rectangle (e.g., bounding box) in the image frame that encompasses the detected object, and a prediction confidence for the detected object; Para 0024); (Each frame, the camera 18 captures an image at the particular resolution of the camera 18. The resolution may be, for example, HD, 4 K, or any other resolution; Para 0026);
Meng discloses representing the returned generated object detection information with a small set of numerical object descriptors rather than transmitting an image frame back to the AR device. Meng also discloses that the image frame of the image data generated by the camera sensor at the resolution of the sensor. This contains image information of the scene. Thus, Meng teaches returning numerical object detection information and for each detected object, the information consists of four bounding box points and confidence prediction. It would be obvious to that the object detection metadata would contain less data than the corresponding image form the results generated of the scene. An image frame contains image information for the captured scene of the camera’s resolution and the returned detection results contains only four bounding box points within the image frame for the detected object. An ordinary person skilled in the art would be able to recognize that transmitting such compact metadata, like four floating point values of a bounding box coordinates and confidence information per detected object would require less data than transmitting the corresponding image frame data used as the input to object detection.
The motivation for the above is to reduce amount of data transmitted and bandwidth usage while accurate object detection results.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser by generate object detection data, wherein the object detection data comprises information about objects in the frame, wherein a size of the generated object detection data is smaller than a size of the frame received from the reality device as taught by Meng.
Regarding claim 13, Gruteser further teaches “The server of claim 12, wherein the server sends the object detection data without sending the frame to the reality device.” (the detection result is sent back to the AR device 12; Para 0059);
Regarding claim 14, Gruteser further teaches “A method for reducing latency and bandwidth usage in reality devices comprising:” (methods; Para 0025); (save bandwidth and thereby reduce latency; Para 0046); (AR device; Para 0046);
“sending a frame of a view external to a vehicle to an edge server, wherein the frame is captured by a reality device;” (frame captures performed by the AR device 12, as well as the transmission to and processing of such frames by the edge cloud device 14; Para 0048); (Fig. 1, shows capture frame n of the AR device to receive frame n in the edge cloud); (detecting surrounding vehicles; Para 0032);
“instructing the reality device to render a mixed reality environment with the object detection data.” (the system requires only 2.24 ms latency and less than 15% resources on the AR device, which leaves the remaining time between frames to render high quality virtual elements for high quality AR/MR experience; Para0047); (the AR device to render high-quality virtual overlays; Para 0046);
However, while Gruteser fails to teach “receiving object detection data from the edge server, wherein the object detection data comprises object information in the frame, wherein a size of the object detection data received from the edge server is smaller than a size of the frame sent to the edge server”.
Meng teaches “receiving object detection data from the edge server, wherein the object detection data comprises object information in the frame, wherein a size of the object detection data received from the edge server is smaller than a size of the frame sent to the edge server; and” (send, to a server, the first compressed image frame. ... receive, from the server, object detection results that identify locations of objects depicted in the first image frame; Para 0004); (object detection results comprise, for each detected object, four floating point values that define a rectangle (e.g., bounding box) in the image frame that encompasses the detected object, and a prediction confidence for the detected object; Para 0024); (Each frame, the camera 18 captures an image at the particular resolution of the camera 18. The resolution may be, for example, HD, 4 K, or any other resolution; Para 0026);
Meng discloses representing the returned object detection information with a small set of numerical object descriptors rather than transmitting an image frame back to the AR device. Meng also discloses that the image frame of the image data generated by the camera sensor at the resolution of the sensor. This contains image information of the scene. Thus, Meng teaches returning numerical object detection information and for each detected object, the information consists of four bounding box points and confidence prediction. It would be obvious to that the object detection metadata would contain less data than the corresponding image form the results generated of the scene. An image frame contains image information for the captured scene of the camera’s resolution and the returned detection results contains only four bounding box points within the image frame for the detected object. An ordinary person skilled in the art would be able to recognize that transmitting such compact metadata, like four floating point values of a bounding box coordinates and confidence information per detected object would require less data than transmitting the corresponding image frame data used as the input to object detection.
The motivation for the above is to reduce amount of data transmitted and bandwidth usage while accurate object detection results.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser by receiving object detection data from the edge server, wherein the object detection data comprises object information in the frame, wherein a size of the object detection data received from the edge server is smaller than a size of the frame sent to the edge server as taught by Meng.
Regarding claim 19, Gruteser further teaches “The method of claim 14, wherein the method further comprises superimposing the object detection data onto a real-world view.” (the AR device, which leaves the remaining time between frames to render high quality virtual elements for high quality AR/MR experience; Para 0047); (Fig. 4A-4C showcase the object detection data of the objects in the real-world view.)
Claim(s) 3, 4, 5, 7, 8, 11, 17, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over GRUTESER in view of MENG and in further view TRAN (No. US-10816993-B1 “Tran”).
Regarding claim 3, while Gruteser and Meng fails to teach all of claim 3, Tran teaches “The system of claim 1, wherein the object detection data comprises box cords of detected objects in the frame, confidence of each corresponding box cord, and object information of each detected object.” (bounding box involving the x, y coordinate and the width and height and the confidence; Col 31, Line 25-26);
(As noted above, cameras can still be used to detect short range objects/ symbols useful for navigation. For example, objects can include pavement markings which are used to convey messages to roadway users and to the camera and vision system. They indicate which part of the road to use, provide information; Col, 31, Line 33-38);
Tran discloses a bounding box that has coordinates, confidence and other information of the detect object the bounding box surrounds. This relates to the claimed box cords of detect objects.
Gruteser, Meng and Tran are analogous art as they are related to object detection.
The motivation for the above is to have more accurate data of the detected object in the frame.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser and Meng by the object detection data comprising box cords of detected objects in the frame, confidence of each corresponding box cord, and object information of each detected object as taught by Tran.
Regarding claim 4, while Gruteser and Meng fails to teach all of claim 4, Tran teaches “The system of claim 3, wherein each box cord comprises coordinates of three or more vertices of the corresponding detected object.” (at least three points and likewise identifies the corresponding position vectors in the map; Col 17, Line 19-21);
((at least 3 non – collinear points) on the sign then the HD map has enough data and can continue; Col 23, Line 44-46);
Tran discloses three points or position vectors that correspond to the detected object box coordinates.
The motivation for the above is to have more accurate coordinate vertices that surround the detect object.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser and Meng by the box cord comprising coordinates of three or more vertices of the corresponding detected object as taught by Tran.
Regarding claim 5, while Gruteser and Meng fails to teach all of claim 5, Tran teaches “The system of claim 3, wherein the confidence is between 0 and 1.” (probabilities, a number between 0 and 1);
Tran discloses the probabilities being between 0 and 1 which correlates to the confidence of the bounding box confidence.
The motivation for the above is to have accurate confidence value when detecting the object.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser and Meng by the confidence being between 0 and 1 as taught by Tran.
Regarding claim 7, while Gruteser and Meng fails to teach all of claim 7, Tran teaches “The system of claim 3, wherein the object information comprises a class of the detected object.” (Object detection combines these two tasks and draws a bounding box around each object of interest in the sensor output and assigns them a class label; Col 29, Line 31-33);
(determining a classification and a state of the detected object; Col 46, Line 1-2);
The motivation for the above is to have accurate classification of the detect objects for better user usage.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser and Meng by the object information comprising a class of the detected object as taught by Tran.
Regarding claim 8, while Gruteser and Meng fails to teach all of claim 8, Tran teaches “The system of claim 3, wherein the object information of each detected object is associated with the corresponding box cord as an annotation.” (The 3D points that project within the image bounding box created by the sign's vertices are considered sign points. These 3D points are used to fit a plane, wherein the HD map projects the sign's image vertices onto that 3D plane to find the 3D coordinates of the sign's vertices. At which point the HD map has all of the information to describe a sign: its location in 3D space, its orientation described by its normal and the type of sign produced from classifying the sign in the image; Col 19, Line 56-64);
Tran discloses sign points that are created from the bounding boxes vertices. This relates to the box cords as an annotation since the sign points are positioned near the vertices of the bounding box of the detected object.
The motivation for the above is to have accurate annotation of the detected object coordinate for better user usage.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser and Meng by the object information of each detected object which is associated with the corresponding box cord as an annotation as taught by Tran.
Regarding claim 11, while Gruteser and Meng fails to teach all of claim 11, Tran teaches “The system of claim 1, wherein the one or more processors are further operable to autonomously drive the vehicle based on the object detection data.” (The HD map stores objects or data structures representing lane elements that comprise information representing geometric boundaries of the lanes; driving direction along the lane; vehicle restriction for driving in the lane, for example, speed limit, relationships with connecting lanes including incoming and outgoing lanes; a termination restriction, for example, whether the lane ends at a stop line, a yield sign, or a speed bump; and relationships with road features that are relevant for autonomous driving, for example, traffic light locations, road sign locations and so on; Col 19, Line 1-11);
Tran discloses autonomous driving based on the detection data from lane and road information, this relates to autonomous driving a vehicle of the claimed subject matter.
The motivation for the above is to have an accurate vehicle driving system and to have reliable data to have an autonomous driving vehicle.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser and Meng by operations to autonomously drive the vehicle based on the object detection data as taught by Tran.
Regarding claim 17, while Gruteser and Meng fails to teach all of claim 17, Tran teaches “The method of claim 16, wherein:
each box cord comprises coordinates of three or more vertices of the corresponding detected object; and” (at least three points and likewise identifies the corresponding position vectors in the map; Col 17, Line 19-21);
((at least 3 non – collinear points) on the sign then the HD map has enough data and can continue; Col 23, Line 44-46);
“the confidence is between 0 and 1.” (probabilities, a number between 0 and 1);
The motivation for the above is to have more accurate coordinate vertices that surround the detect object and accurate confidence value when detecting the object.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser and Meng by the box cord comprising coordinates of three or more vertices of the corresponding detected object and the confidence is between 0 and 1 as taught by Tran.
Regarding claim 18, while Gruteser and Meng fails to teach all of claim 18, Tran teaches “The method of claim 16, wherein:
the object information comprises a class of the detected object; and” (Object detection combines these two tasks and draws a bounding box around each object of interest in the sensor output and assigns them a class label; Col 29, Line 31-33);
(determining a classification and a state of the detected object; Col 46, Line 1-2);
“the object information of each detected object is associated with the corresponding box cord as an annotation.” (The 3D points that project within the image bounding box created by the sign's vertices are considered sign points. These 3D points are used to fit a plane, wherein the HD map projects the sign's image vertices onto that 3D plane to find the 3D coordinates of the sign's vertices. At which point the HD map has all of the information to describe a sign: its location in 3D space, its orientation described by its normal and the type of sign produced from classifying the sign in the image; Col 19, Line 56-64);
The motivation for the above is to accurate classification of the detect objects and have accurate annotation of the detected object coordinate for better user usage.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser and Meng by the object information comprising a class of the detected object and the object information of each detected object is associated with the corresponding box cord as an annotation as taught by Tran.
Regarding claim 20, while Gruteser and Meng fails to teach all of claim 20, Tran teaches “The method of claim 14, wherein the method further comprises autonomously driving the vehicle based on the object detection data.” (The HD map stores objects or data structures representing lane elements that comprise information representing geometric boundaries of the lanes; driving direction along the lane; vehicle restriction for driving in the lane, for example, speed limit, relationships with connecting lanes including incoming and outgoing lanes; a termination restriction, for example, whether the lane ends at a stop line, a yield sign, or a speed bump; and relationships with road features that are relevant for autonomous driving, for example, traffic light locations, road sign locations and so on; Col 19, Line 1-11);
The motivation for the above is to have an accurate vehicle driving system and to have reliable data to have an autonomous driving vehicle.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser and Meng by further comprising autonomously driving the vehicle based on the object detection data as taught by Tran.
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over GRUTESER in view of MENG and in further of TRAN in further view of XU (No. US-10997433-B2 “Xu”).
Regarding claim 6, while Gruteser, Meng and Tran fail to teach all of claim 17, Xu teaches “The system of claim 3, wherein the object detection data further comprise a cropped image path.” (The ROI images may represent a cropped image (e.g., center crop, right crop, left crop, half size, etc.). The cropped image may include a portion of a polygon (e.g., a polygon from the annotations of the original image) representing a lane or boundary outside; Col 18, Line 25-29);
Xu discloses a cropped image that comprises crop and polygon of a boundary box, this relates to the cropped image path of the claimed subject matter.
Gruteser, Meng, Tran and Xu are analogous art as they are all related to object detection.
The motivation for the above is to have a user-friendly information image of the detected object the user can refer back too.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser, Meng and Tran by the object detection data further comprising a cropped image path as taught by Xu.
Regarding claim 16, while Gruteser, Meng fails to teach all of claim 17, Tran further teaches “The method of claim 14, wherein the object detection data comprises box cords of detected objects in the frame, confidence of each corresponding box cord, object information of each detected object,” (bounding box involving the x, y coordinate and the width and height and the confidence; Col 31, Line 25-26);
(As noted above, cameras can still be used to detect short range objects/ symbols useful for navigation. For example, objects can include pavement markings which are used to convey messages to roadway users and to the camera and vision system. They indicate which part of the road to use, provide information; Col, 31, Line 33-38);
However, while Tran fails to teach “a cropped image path”.
Xu teaches “a cropped image path.” (The ROI images may represent a cropped image (e.g., center crop, right crop, left crop, half size, etc.). The cropped image may include a portion of a polygon (e.g., a polygon from the annotations of the original image) representing a lane or boundary outside; Col 18, Line 25-29);
The motivation for the above is to have more accurate data of the detected object in the frame and a user-friendly information image of the detected object the user can refer back too.
Therefore, it would have been obvious for an ordinary skilled person in the art before the effective filing date of claimed invention to have modified Gruteser and Meng by the object detection data comprising box cords of detected objects in the frame, confidence of each corresponding box cord, and object information of each detected object as taught by Tran and to have modified Gruteser, Meng and Tran by a cropped image path as taught by Xu.
Response to Arguments
Applicant’s arguments, see pg.10-11, filed 06/05/2026, with respect to Specification have been fully considered and are persuasive. The Objection of 03/09/2026 has been withdrawn.
Applicant’s arguments, see 11-13, filed 06/05/2026, with respect to the rejection(s) of claim(s) 1, 12 and 14 under 102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of MENG.
Applicant argues that Gruteser describes systems and methods for edge-assisted, real-time object detection in mobile augmented reality using a low-latency offloading process. Gruteser, Abstract. As illustrated in FIG. 3, an AR device captures an image frame and performs a dynamic encoding process to identify one or more Regions of Interest (RoIs), which reduces transmission latency and bandwidth consumption in the offloading pipeline. See Gruteser, paras. [0061]-[0070]. The AR device then offloads the RoI to an edge cloud for object detection. Id. The edge cloud subsequently returns detected object information to the AR device, which renders the detected objects relative to the image frame.
Examiner replies that Applicant’s argument is persuasive only to the extent that Grutestr’s ROI encoding disclosure standing alone does not expressly compare the size of the returned detections results with the size of the transmitted encoded frame. The present rejection does not rely on that comparison. Gruteser is relied for transmitting the image frame to the edge cloud, remotely preforming object detection, returning the resulting detection information, and using the returned detection results for AR rendering/tracking. Meng is relied for teaching how such returned detection results may be presented as compact object information. The applicant’s distinction between Grutestr’s ROI encoding and the claimed returned object detection data therefore does not overcome the combination. The Examiner is not equating the ROI encoded frame with the returned object detection data. Rather Gruteser and Meng teach the image frame is transmitted from the AR device to the server, The server preforms the object detection on the image frame, and object detection results are returned to the AR device. Meng’s implantation of the results for each detected object, the result comprises four floating point values of a bounding box coordinates and confidence information. The amendment distinguishes between the same two categories of information recognized by the combination. Image data supplied as the input to the server-side object detection and compact object information produced as the output of that object detection.
Applicant argues that Gruteser does not disclose the features of "a size of the object detection data received from the edge server is smaller than a size of the frame sent to the edge server," recited in amended claims 1 and 14, and features of "a size of the generated object detection data rat the server] is smaller than a size of the frame received from the reality device," recited in amended claim 12.
Examiner replies that assuming that Gruteser alone doesn’t expressly disclose this size relationship, Meng provides the additional teaching that renders it obvious. Meng teaches that the uploaded frame comprises camera image data, while the object detection results comprise bounding box coordinates and confidence values for the detected objects. Thus, an ordinary person skilled in the art would understand that the server need not retransmit image data to communicate the object detection result. The information required for object placement can be represented by the smaller set of numerical detection values of the bounding box coordinates as taught by Meng. Meng’s express teaching of returning only a small set of numerical detection values per object and Gruteser’s teaching of reducing latency and bandwidth, provides reason to configure the returned detection data to be smaller than the transmitted image frame.
Applicant argues that Tran and Xu do not cure the deficiencies of Gruteser. Tran is narrowly cited to allegedly teach the features related to boxes, confidence, object information, and autonomous driving. Office Action, pp. 8-11. Xu is narrowly cited to allegedly teach the feature related to cropped image path. Office Action, p. 13. Thus, amended claims 1, 12, and 14 are patentable over Gruteser, Tran, and Xu, taken individually or in combination.
Examiner replies that Tran and Xu are relevant to the present combination of Gruteser and Meng. In addition, due to claims 1, 12 and 14 rejections under Gruteser in view of Meng, all dependent claims rejection are maintained under Gruteser in view of Meng and additional art, Tran and Xu.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/B.D.P./Examiner, Art Unit 2612
/Said Broome/Supervisory Patent Examiner, Art Unit 2612
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