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 .
Response to Amendment
This office action is responsive to the amendment received 07/24/2026.
In response to the Non-Final Office Action 04/29/2026, the applicant states that claims 1, 3, and 15 have been amended. Claims 7 and 18 have been cancelled without prejudice or disclaimer.
Claims 1, 3, and 5 have been amended. Claims 7 and 18 have been cancelled. In summary, claims 1-6, 8-11, 15-17, and 19-22 are pending in current application.
Response to Arguments
Applicant's arguments filed 07/24/2026 have been fully considered but they are not persuasive.
Regarding to claim 1, the applicant argues that if the first data indicates that a sensing reliability score is present in one encapsulating container, writing the sensing reliability score in one of the encapsulating containers, the sensing reliability score being a value belonging to a range of values indicating a degree of sensing reliability of a spatial region. The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
Kammachi Sreedhar discloses “if the first data indicates that a flag is present in one encapsulating container, writing the flag in one of the encapsulating containers”. For example, in paragraph [0046], Kammachi Sreedhar teaches each box has a header and a payload; Kammachi Sreedhar further teaches the box header indicates the type of the box and the size of the box in terms of bytes. In paragraph [0047], Kammachi Sreedhar teaches a file includes media data and metadata that are encapsulated into boxes; Kammachi Sreedhar further teaches each box is identified by a four character code (4CC) and starts with a header which informs about the type and size of the box. In paragraph [0141], Kammachi Sreedhar teaches an indication along the bitstream may refer to metadata in a container file that encapsulates the bitstream. In Fig. 6 and paragraph [0166], Kammachi Sreedhar teaches a media presentation description (MPD) file includes a first representation belonging to a first adaptation set associated with the first V-PCC bitstream, i.e. first group, and a second representation belonging to a second adaptation set associated with the second V-PCC bitstream, i.e. second group; indication and MPD indicate two groups; Kammachi Sreedhar further teaches writing (606), in the MPD file, at least one information element describing grouping information of the first representation belonging to the first adaptation set and the second representation belonging to the second adaptation set. In paragraph [0168], Kammachi Sreedhar teaches encapsulating the first and the second V-PCC bitstream in a single-track or in a multi-track container.
Zeng discloses “flag is sensing reliability score”. For example, in paragraph [0072], Zeng teaches a confidence score, e.g., a percentage score, indicates the degree of uncertainty. In paragraph [0074], Zeng teaches the classification includes assigning a confidence score. In paragraph [0076], Zeng teaches the confidence score from a camera-based object recognition; Zeng further teaches the confidence score is obtained from a LiDAR based object recognition; Zeng further more teaches the confidence score of camera and LiDAR are sensing reliability score. In paragraph [0077], Zeng teaches the confidence scores are obtained from the camera, i.e. sensing device; Zeng further teaches the confidence scores are obtained from LiDAR sources, i.e. sensing device. In paragraph [0078], Zeng teaches the sensor fusion algorithm 60 assesses the respective confidence scores, i.e. reliability score. In paragraph [0080], Zeng teaches sensor confidence scores.
Zeng discloses further teaches “the sensing reliability score being a value belonging to a range of values indicating a degree of sensing reliability of a spatial region”. For example, in paragraph [0036], Zeng teaches if the LiDAR returns sensor data that confirms the presence of an object in that region of interest, the Perception layer computes a new uncertainty factor, perhaps 5%, meaning that recognition of the oncoming vehicle is now 95% certain. In paragraph [0072], Zeng teaches a confidence score, e.g., a percentage score, indicates the degree of uncertainty. In paragraph [0076], Zeng teaches the confidence score from a camera-based object recognition; Zeng further teaches the confidence score is obtained from a LiDAR based object recognition; Zeng further more teaches the confidence score of camera and LiDAR are sensing reliability score. In paragraph [0077], Zeng teaches the confidence scores obtained from the camera and LiDAR sources may well be different. In paragraph [0082], Zeng teaches the confidence scores from the respective sensors that allows the sensor fusion algorithm to assess how confident the system is about different regions within the scene.
Independent claims 3 and 15 are not allowable due to the similar reasons as discussed above.
Dependent claims 2, 4-6, 8-11, 16, 17 and 19-22 are also not allowable due to similar reasons as discussed above.
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.
Claims 1-6, 8-11, 15-17, and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Kammachi Sreedhar (US 20210314626 A1) and in view of Zeng (US 20200355820 A1).
Regarding to claim 1 (Currently Amended), Kammachi Sreedhar discloses a method of encapsulating point cloud data in encapsulating containers ([0047]: encapsulate a file, media data and metadata into boxes; [0126]: simple ISOBMFF encapsulation of a V-PCC encoded bitstream; Fig. 5a; Fig. 5b; [0147]: dynamic point cloud compression;
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[0148]: decompose the point cloud frame; Fig. 6; [0166]: writing (600), in a container file, a first video-based point cloud compression (V-PCC) bitstream and a second V-PCC bitstream; [0208]: parse (802), from a media presentation description (MPD) file, a first representation belonging to a first adaptation set associated with the first V-PCC bitstream and a second representation belonging to a second adaptation set associated with the second V-PCC bitstream), the method comprising:
writing point cloud data in at least one of the encapsulating containers ([0118]: point clouds; [0120]: point cloud is a set of data points in a coordinate system, for example in a three-dimensional coordinate system being defined by X, Y, and Z coordinates; Fig. 6; [0166]: write (600), in a container file, a first video-based point cloud compression (V-PCC) bitstream and a second V-PCC bitstream;
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; [0214]: one or more coded media bitstreams are encapsulated into a container file; [0215]: the server 1540 encapsulates the coded media bitstream into RTP packets according to an RTP payload format.); and
for at least one spatial region of a spatial sensing coverage of a sensing device, writing first data on a presence of a flag in one of the encapsulating containers ([0083]: a spatial relationship; a video represents a spatial part of another full-frame video, e.g. a region of interest; [0088]: define a reference space; [0089]: the size of the reference space; [0117]: a combination of cameras and depth sensors; [0118]: video is captured from real-world scenes using a variety of capture solutions, e.g. a multi-camera, a laser scan, a combination of video and dedicated depths sensors, etc.; Fig. 6; [0166]: write (602), in the container file, an indication, i.e. first data, about the common group between the first V-PCC bitstream and the second V-PCC bitstream; generate (604) and write a media presentation description (MPD) file, i.e. first data, with a first representation belonging to a first adaptation set associated with the first V-PCC bitstream and a second representation belonging to a second adaptation set associated with the second V-PCC bitstream; an indication and MPD are first data); and
if the first data indicates that a flag is present in one encapsulating container, writing the flag in one of the encapsulating containers ([0141]: an indication along the bitstream may refer to metadata in a container file that encapsulates the bitstream; Fig. 6; [0166]: a media presentation description (MPD) file includes a first representation belonging to a first adaptation set associated with the first V-PCC bitstream, i.e. first group, and a second representation belonging to a second adaptation set associated with the second V-PCC bitstream, i.e. second group; indication and MPD indicate two groups; write (606), in the MPD file, at least one information element describing grouping information of the first representation belonging to the first adaptation set and the second representation belonging to the second adaptation set; [0168]: encapsulate the first and the second V-PCC bitstream in a single-track or in a multi-track container).
Kammachi Sreedhar fails to explicitly disclose:
flag is sensing reliability score;
the sensing reliability score being a value belonging to a range of values indicating a degree of sensing reliability of a spatial region.
In same field of endeavor, Zeng teaches:
flag is sensing reliability score ([0072]: a confidence score, e.g., a percentage score, indicates the degree of uncertainty; [0074]: the classification includes assigning a confidence score; [0076]: the confidence score from a camera-based object recognition; the confidence score is obtained from a LiDAR based object recognition; the confidence score of camera and LiDAR are sensing reliability score; [0077]: the confidence scores are obtained from the camera, i.e. sensing device; the confidence scores are obtained from LiDAR sources, i.e. sensing device; [0078]: the sensor fusion algorithm 60 assesses the respective confidence scores, i.e. reliability score; [0080]: sensor confidence scores);
the sensing reliability score being a value belonging to a range of values indicating a degree of sensing reliability of a spatial region (Zeng; [0036]: if the LiDAR returns sensor data that confirms the presence of an object in that region of interest, the Perception layer computes a new uncertainty factor, perhaps 5%, meaning that recognition of the oncoming vehicle is now 95% certain; [0072]: a confidence score, e.g., a percentage score, indicates the degree of uncertainty; [0076]: the confidence score from a camera-based object recognition; the confidence score is obtained from a LiDAR based object recognition; the confidence score of camera and LiDAR are sensing reliability score; [0077]: the confidence scores obtained from the camera and LiDAR sources may well be different; [0082]: the confidence scores from the respective sensors allows the sensor fusion algorithm to assess how confident the system is about different regions within the scene).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kammachi Sreedhar to include flag is sensing reliability score; the sensing reliability score being a value belonging to a range of values indicating a degree of sensing reliability of a spatial region as taught by Zeng. The motivation for doing so would have been to cost-effectively improve accuracy, precision and confidence in sensor readings; to capture what is called a point cloud image of the scene; to produce a point cloud image of a scene; to classify detected point-cloud regions as belonging to a previously trained object as taught in paragraphs [0001], [0026], [0029], and [0074] by Zeng.
Regarding to claim 2 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 1, wherein the method further comprises, for each of the at least one spatial region, writing, in one of the encapsulating containers, second data on a presence of sensing reliability reason data, and wherein if the second data indicates that sensing reliability reason data is present, writing the sensing reliability reason data in one of the encapsulating containers (Kammachi Sreedhar; Fig. 4a; Fig. 4b; [0135]: a prediction error signal; a reconstructed prediction error signal; [0137]: the prediction error is coded in a sample domain; [0141]: an indication along the bitstream may refer to metadata in a container file that encapsulates the bitstream; Fig. 6; [0166]: a media presentation description (MPD) file includes a first representation belonging to a first adaptation set associated with the first V-PCC bitstream, i.e. first group, and a second representation belonging to a second adaptation set associated with the second V-PCC bitstream, i.e. second group; indication and MPD indicate two groups; write (606), in the MPD file, at least one information element describing grouping information of the first representation belonging to the first adaptation set and the second representation belonging to the second adaptation set; [0168]: encapsulate the first and the second V-PCC bitstream in a single-track or in a multi-track container).
Regarding to claim 3 (Currently Amended), Kammachi Sreedhar discloses a method of parsing point cloud data from encapsulating containers ([0047]: encapsulate a file, media data and metadata into boxes; [0126]: simple ISOBMFF encapsulation of a V-PCC encoded bitstream; Fig. 5a; Fig. 5b; [0147]: dynamic point cloud compression;
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[0148]: decompose the point cloud frame; Fig. 6; [0166]: writing (600), in a container file, a first video-based point cloud compression (V-PCC) bitstream and a second V-PCC bitstream; [0208]: parse (802), from a media presentation description (MPD) file, a first representation belonging to a first adaptation set associated with the first V-PCC bitstream and a second representation belonging to a second adaptation set associated with the second V-PCC bitstream), the method comprising:
reading, from at least one of the encapsulating containers, point cloud data associated with at least one spatial region of a spatial sensing coverage of a sensing device (Fig. 8; [0207]: operation of a decoder or a file reader/parser upon receiving the bitstream with said indications; receive (800) a bitstream comprising a media file including or inferring to a container file comprising a first video-based point cloud compression (V-PCC) bitstream and a second V-PCC bitstream; wherein said first and second V-PCC bitstreams are associated with a common group based on at least one logical context, and an indication about the common group between the first V-PCC bitstream and the second V-PCC bitstream; parse (802), from a media presentation description (MPD) file, a first representation belonging to a first adaptation set associated with the first V-PCC bitstream and a second representation belonging to a second adaptation set associated with the second V-PCC bitstream);
for each of the at least one spatial region, reading first data on a presence of a flag from one of the encapsulating containers (Fig. 5a; Fig. 5b; [0147]: compression/decompression processes; Fig. 8; [0207]: operation of a decoder or a file reader/parser upon receiving the bitstream with said indications; read the MPD file, i.e. first data; parse (804), from the MPD file, at least one information element describing grouping information of the first representation belonging to the first adaptation set and the second representation belonging to the second adaptation set); and
If the first data indicates that a flag is present, reading the flag from one of the encapsulating containers (Fig. 8; [0207]: parse (804), from the MPD file, at least one information element describing grouping information of the first representation belonging to the first adaptation set, i.e. first group, and the second representation belonging to the second adaptation set, i.e. second group; read group numbers; select (806) either the first representation or the second representation for rendering).
Kammachi Sreedhar fails to explicitly disclose:
flag is sensing reliability score;
the sensing reliability score being a value belonging to a range of values indicating a degree of sensing reliability of a spatial region.
In same field of endeavor, Zeng teaches: flag is sensing reliability score ([0072]: a confidence score, e.g., a percentage score, indicates the degree of uncertainty; [0074]: the classification includes assigning a confidence score; [0076]: the confidence score from a camera-based object recognition; the confidence score is obtained from a LiDAR based object recognition; [0077]: the confidence scores are obtained from the camera, i.e. sensing device; the confidence scores are obtained from LiDAR sources, i.e. sensing device; [0078]: the sensor fusion algorithm 60 assesses the respective confidence scores, i.e. reliability score; [0080]: sensor confidence scores);
the sensing reliability score being a value belonging to a range of values indicating a degree of sensing reliability of a spatial region (Zeng; [0036]: if the LiDAR returns sensor data that confirms the presence of an object in that region of interest, the Perception layer computes a new uncertainty factor, perhaps 5%, meaning that recognition of the oncoming vehicle is now 95% certain; [0072]: a confidence score, e.g., a percentage score, indicates the degree of uncertainty; [0076]: the confidence score from a camera-based object recognition; the confidence score is obtained from a LiDAR based object recognition; the confidence score of camera and LiDAR are sensing reliability score; [0077]: the confidence scores obtained from the camera and LiDAR sources may well be different; [0082]: the confidence scores from the respective sensors allows the sensor fusion algorithm to assess how confident the system is about different regions within the scene).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kammachi Sreedhar to include flag is sensing reliability score; the sensing reliability score being a value belonging to a range of values indicating a degree of sensing reliability of a spatial region as taught by Zeng. The motivation for doing so would have been to cost-effectively improve accuracy, precision and confidence in sensor readings; to capture what is called a point cloud image of the scene; to produce a point cloud image of a scene; to classify detected point-cloud regions as belonging to a previously trained object as taught in paragraphs [0001], [0026], [0029], and [0074] by Zeng.
Regarding to claim 4 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 3, wherein the method further comprises, for each of the at least one spatial region, reading second data on a presence of sensing reliability reason data from one of the encapsulating containers, and wherein if the second data indicates that sensing reliability reason data is present, reading the sensing reliability reason data from one of the encapsulating containers (Kammachi Sreedhar; Fig. 4a; Fig. 4b; [0135]: a prediction error signal; a reconstructed prediction error signal; [0137]: the prediction error is coded in a sample domain; [0168]: encapsulate the first and the second V-PCC bitstream in a single-track or in a multi-track container; Fig. 8; [0207]: parse (804), from the MPD file, at least one information element describing grouping information of the first representation belonging to the first adaptation set and the second representation belonging to the second adaptation set).
Kammachi Sreedhar in view of Zeng discloses sensing reliability reason data (Zeng; [0076]: the confidence score from a camera-based object recognition; the confidence score is obtained from a LiDAR based object recognition; [0077]: the confidence scores are obtained from the camera, i.e. sensing device; the confidence scores are obtained from LiDAR sources, i.e. sensing device; [0078]: the sensor fusion algorithm 60 assesses the respective confidence scores, i.e. reliability score).
Same motivation of claim 1 is applied here.
Regarding to claim 5 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 1, wherein the first data is a binary value (Kammachi Sreedhar; [0140]: context adaptive binary arithmetic coding; an entropy-coded bitstream; [0141]: an indication along the bitstream may refer to metadata in a container file that encapsulates the bitstream; [0152]: a binary map indicates for each cell of the grid; [0166]: V-PCC bitstream).
Regarding to claim 6 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 1, wherein the sensing reliability score is a binary value (Zeng; [0080]: the bitmap communicates binary (yes-no) instructions on whether to focus attention on a particular pixel or not; [0081]: the attention bitmap feeds the exact pointing coordinates to the LiDAR's laser directing system).
Same motivation of claim 1 is applied here.
Regarding to claim 8 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 2, wherein the second data is a binary value (Kammachi Sreedhar; 7[0140]: context adaptive binary arithmetic coding; an entropy-coded bitstream; [0141]: an indication along the bitstream may refer to metadata in a container file that encapsulates the bitstream; [0152]: a binary map indicates for each cell of the grid; [0166]: a first video-based point cloud compression (V-PCC) bitstream and a second V-PCC bitstream; V-PCC bitstream).
Regarding to claim 9 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 2, wherein the sensing reliability reason data relates to one of:
no sensed data due to sensor failure;
bus transmission error;
network transmission error;
application decision to discard a spatial region;
user choice; or
erratic sensed data due to sensor failure (one of … or: is optional; Zeng; [0076]: Camera-based edge detection tends to fail in low contrast scenes where it becomes difficult to visually separate the object from the background; Point-cloud cluster detection tends to fail when two different nearby objects overlap along the laser beam line of sight).
Same motivation of claim 1 is applied here.
Regarding to claim 10 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 1, wherein the encapsulating containers are data structures of a file container (Kammachi Sreedhar; [0053]: TrackReferenceBox;
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; a class includes a structure; [0056]: TrackGroupBox;
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; a class includes a structure; [0128]: the container file; [0141]: metadata in a container file that encapsulates the bitstream; [0166]: writing (600), in a container file, a first video-based point cloud compression (V-PCC) bitstream and a second V-PCC bitstream).
Regarding to claim 11 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 1, wherein the encapsulating containers are network packets (Kammachi Sreedhar; [0215]: when the communication protocol stack is packet-oriented, the server 1540 encapsulates the coded media bitstream into packets; the server 1540 encapsulates the coded media bitstream into RTP packets according to an RTP payload format; [0216]: the sending file parser helps in creating the correct format for the communication protocol, such as packet headers and payloads; [0217]: translation of a packet stream according to one communication protocol stack to another communication protocol stack).
Regarding to claim 15 (Currently Amended), Kammachi Sreedhar discloses a non-transitory storage medium carrying instructions of program code for executing a method of encapsulating point cloud data in encapsulating containers ([0008]: one memory, said at least one memory stored with computer program code; Fig. 1; [0032]: the controller 56 is connected to memory 58; memory stores instructions for implementation on the controller 56; [0047]: encapsulate a file, media data and metadata into boxes; [0049]: random access memory RAM; [0126]: simple ISOBMFF encapsulation of a V-PCC encoded bitstream; Fig. 5a; Fig. 5b; [0147]: dynamic point cloud compression;
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[0148]: decompose the point cloud frame by converting 3D samples to 2D samples on a given projection plane using a strategy that provides the best compression; Fig. 6; [0166]: write (600), in a container file, a first video-based point cloud compression (V-PCC) bitstream and a second V-PCC bitstream; [0208]: parse (802), from a media presentation description (MPD) file, a first representation belonging to a first adaptation set associated with the first V-PCC bitstream and a second representation belonging to a second adaptation set associated with the second V-PCC bitstream), the method comprising:
The rest of the claim limitations are similar to claim limitations recited in claim 1. Therefore, same rational used to reject claim 1 is also used to reject claim 15.
Regarding to claim 16 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 3,
The rest claim limitations are similar to claim limitations recited in claim 5. Therefore, same rational used to reject claim 5 is also used to reject claim 16.
Regarding to claim 17 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 3,
The rest of the claim limitations are similar to claim limitations recited in claim 6. Therefore, same rational used to reject claim 6 is also used to reject claim 17.
Regarding to claim 19 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 4,
The rest claim limitations are similar to claim limitations recited in claim 8. Therefore, same rational used to reject claim 8 is also used to reject claim 19.
Regarding to claim 20 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 4,
The rest claim limitations are similar to claim limitations recited in claim 9. Therefore, same rational used to reject claim 9 is also used to reject claim 20.
Regarding to claim 21 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 3,
The rest claim limitations are similar to claim limitations recited in claim 10. Therefore, same rational used to reject claim 10 is also used to reject claim 21.
Regarding to claim 22 (Previously Presented), Kammachi Sreedhar in view of Zeng discloses the method of claim 3,
The rest of the claim limitations are similar to claim limitations recited in claim 11. Therefore, same rational used to reject claim 11 is also used to reject claim 22.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hai Tao Sun whose telephone number is (571)272-5630. The examiner can normally be reached 9:00AM-6:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Hajnik can be reached at 5712727642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAI TAO SUN/Primary Examiner, Art Unit 2616
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