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 Arguments
Applicant’s arguments, filed September 12, 2025, have been noted; however, these arguments are moot in view of a new grounds of rejection discussed below.
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 9, 10, 11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sugio et al. (US 2021/0297698 A1, already of record, referred to herein as “Sugio”) in view of Sugio (US 2023/0360276 A1, referred to herein as “Sugio II”).
Regarding claim 1, Sugio discloses: A method of encoding point cloud data, the method comprising:
encoding geometry data of point cloud data (Sugio: Fig. 7, paragraphs [0194] through [0198], disclosing a method of encoding three-dimensional data; paragraph [0233], disclosing where the encoded 3D data is point cloud data; paragraph [0134], disclosing that the 3D
point cloud data may include geometry information);
encoding attribute data of the point cloud data (Sugio: paragraph [0136], disclosing that the 3D
point cloud data may include attribute information)…;
[…]
wherein the geometry data and the attribute data are included in a bitstream (Sugio: paragraphs [0493], [0645] and [0647], disclosing that the encoded information—e.g., including geometry and attribute data—is generated as a bitstream ),
[…].
Sugio does not explicitly disclose:
encoding attribute data based on a reference frame for the point cloud data;
wherein the attribute data is predicted by reference a point in the reference frame based on a position for the attribute data and a range related to the attribute data;
wherein the bitstream includes information for representing the attribute data is coded based on the position for the attribute data.
However, Sugio II discloses:
encoding attribute data based on a reference frame for the point cloud data (Sugio II: paragraphs [0155] and [0180], disclosing attribute information of the point cloud that may be encoded; paragraph [0357], disclosing that a reference node may belong to a different frame);
wherein the attribute data is predicted by reference a point in the reference frame based on a position for the attribute data and a range related to the attribute data (Sugio II: paragraph [0481], disclosing that a predicted value of attribute information may be determined based on a position of attribute data in another frame—e.g., the reference frame; paragraphs [0482] – [0043], disclosing that the reference point exists within a predetermined distance—e.g., a range—from the target point);
wherein the bitstream includes information for representing the attribute data is coded based on the position for the attribute data (Sugio II: Figs. 82 and 83, paragraphs [0531], [0536] and [0042] – [0043], disclosing syntax for coding attribute data including a prediction threshold representing an upper limit of the distances between the reference and target points).
At the time the application was effectively filed, it would have been obvious for a person having ordinary skill in the art to use the attribute reference information of Sugio II in the point cloud encoding method of Sugio.
One would have been motivated to modify Sugio in this manner in order to improve coding efficiency by using attribute information of neighboring three-dimensional points (Sugio II: paragraphs [0108] – [0110]). Additionally, Sugio and Sugio II are directed to the same field of endeavor—namely, point cloud encoding of video data (Sugio: paragraphs [0003] – [0006]; Sugio II: paragraphs [0002] – [0006]).
Regarding claim 2, Sugio and Sugio II disclose: The method of claim 1, wherein the point cloud data is encoded based on a prediction unit (Sugio: paragraph [0155], disclosing encoding based on a prediction unit).
Regarding claim 9, the claim recites analogous limitations to claim 1, above, and is therefore rejected on the same premise. (Note that Sugio discloses implementation via memory and processor in paragraphs [0148] and [0152]).
Regarding claim 10, Sugio and Sugio II disclose: A method of receiving point cloud data, the method comprising:
decoding geometry data of point cloud data in a bistream (Sugio: paragraph [0134], disclosing that the 3D point cloud data may include geometry information);
decoding attribute data of the point cloud data ((Sugio: paragraph [0136], disclosing that the 3D
point cloud data may include attribute information) based on a reference frame for the point cloud data (Sugio II: paragraphs [0155] and [0180], disclosing attribute information of the point cloud that may be encoded; paragraph [0357], disclosing that a reference node may belong to a different frame),
wherein the attribute data is predicted by referencing a point in the reference frame based on a position for the attribute data and a range related to the attribute data (Sugio II: paragraph [0481], disclosing that a predicted value of attribute information may be determined based on a position of attribute data in another frame—e.g., the reference frame; paragraphs [0482] – [0043], disclosing that the reference point exists within a predetermined distance—e.g., a range—from the target point),
wherein the bitstream includes information for representing the attribute data is coded based on the position for the attribute data (Sugio II: Figs. 82 and 83, paragraphs [0531], [0536] and [0042] – [0043], disclosing syntax for coding attribute data including a prediction threshold representing an upper limit of the distances between the reference and target points).
The motivation for combining Sugio and Sugio II has been discussed in connection with claim 1, above.
Regarding claim 11, Sugio and Sugio II disclose: The method of claim 1, wherein the point cloud data is decoded based on a prediction unit (Sugio: paragraph [0155], disclosing encoding based on a prediction unit).
Regarding claim 15, the claim recites analogous limitations to claim 11, above, and is therefore rejected on the same premise. (Note that Sugio discloses implementation via memory and processor in paragraphs [0148] and [0152]).
Claims 3, 4, 6, 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sugio in view of Sugio II as applied to claim 2 above, and further in view of Yea et al. (US 2021/0217205 A1, already of record, referred to herein as “Yea”).
Regarding claim 3, Sugio and Sugio II disclose: The method of claim 2, wherein the encoding of the point cloud data further comprises:
grouping points included in the prediction unit based on the attribute data (Sugio: paragraph [0531],
disclosing grouping of points based on a volume whose attribute information is the most similar to
an encoding target volume to generate the predicted volume),
wherein the points included in the prediction unit are classified based on the attribute data about the points
(Sugio: paragraph [0531], disclosing calculation of a difference between the volume whose attribute
information is most similar to the target block and the target block), and an anchor is generated.
Sugio and Sugio II do not explicitly disclose: and an anchor is generated.
However, Yea discloses: and an anchor is generated (Yea: paragraphs [0008] through [0014],
disclosing generation of an anchor based on predicting attribute values of 3D point data).
At the time the application was effectively filed, it would have been obvious for a person
having ordinary skill in the art to use the anchor of Yea in the method of Sugio and Sugio II.
One would have been motivated to modify Sugio and Sugio II in this manner in order to provide better prediction using neighboring attribute samples (Yea: paragraphs [0017] and [0018]).
Regarding claim 4, Sugio, Sugio II, and Yea disclose: The method of claim 3, wherein the grouping comprises: grouping points included in a range related to the attribute data about the points based on the anchor; or
grouping points included in a range related to the geometry data about the points based on the anchor (Yea:
paragraphs [0008], disclosing grouping according to a set of k-nearest neighbors of a current point
based on associated attribute values).
The motivation for combining Sugio, Sugio II and Yea has been discussed in connection with claim 3, above.
Regarding claim 6, Sugio, Sugio II and Yea disclose: The method of claim 4, wherein the grouping comprises:
generating a group based on a threshold for the attribute data about the points; or generating a group based on
a threshold for the geometry data about the points (Yea: paragraphs [0008] through [0014], disclosing
generation of an anchor based on a variability threshold of neighboring values),
wherein the points classified based on the attribute data are included in the group based on the anchor and the
threshold (Yea: paragraphs [0008] through [0014], disclosing generation of an anchor based on a
attribute information and the threshold).
The motivation for combining Sugio, Sugio II, and Yea has been discussed in connection with claim 3, above.
Regarding claim 12, Sugio, Sugio II, and Yea disclose: The method of claim 11, wherein the decoding of the point cloud data further comprises:
grouping points included in the prediction unit based on the attribute data (Sugio: paragraph [0531],
disclosing grouping of points based on a volume whose attribute information is the most similar to
an encoding target volume to generate the predicted volume),
wherein the points included in the prediction unit are classified based on the attribute data about the points,
and an anchor is generated (Sugio: paragraph [0531], disclosing calculation of a difference between the
volume whose attribute information is most similar to the target block and the target block; Yea:
paragraphs [0008] through [0014], disclosing generation of an anchor based on predicting attribute
values of 3D point data).
The motivation for combining Sugio, Sugio II, and Yea has been discussed in connection with claim 3, above.
Regarding claim 13, Sugio, Sugio II, and Yea disclose: The method of claim 12, wherein the grouping comprises:
grouping points included in a range related to the attribute data about the points based on the anchor; or
grouping points included in a range related to the geometry data about the points based on the anchor (Yea:
paragraphs [0008], disclosing grouping according to a set of k-nearest neighbors of a current point
based on associated attribute values).
The motivation for combining Sugio, Sugio II, and Yea has been discussed in connection with claim 3, above.
Claims 5, 8 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sugio
in view of Sugio II and Yea as applied to claim 5 above, and further in view of Cao et al.
(US 20220108487 A1, already of record, referred to herein as “Cao”).
Regarding claim 5, Sugio, Sugio II, and Yea disclose: The method of claim 4, as discussed above.
Sugio, Sugio II, and and Yea do not explicitly disclose: estimating a motion vector for a group based on the number of the grouped points; or estimating a motion vector for the prediction unit based on the number of the grouped points.
However, Cao discloses: estimating a motion vector for a group based on the number of the grouped
points; or estimating a motion vector for the prediction unit based on the number of the grouped points (Cao:
paragraph [0137], disclosing use of motion vectors for point cloud encoding; paragraphs [0040] and
[0051], disclosing coding of attribute and geometry information).
At the time the application was effectively filed, it would have been obvious for a person
having ordinary skill in the art to use the motion vector of Cao in the method of Sugio, Sugio II, and Yea.
One would have been motivated to modify Sugio, Sugio II, and Yea in this manner in order to improve coding efficiency by intra frame prediction (Cao: paragraph [0005]).
Regarding claim 8, Sugio, Sugio II, Yea, and Cao disclose: The method of claim 1, wherein the encoding of the point cloud data comprises:
quantizing the point cloud data (Sugio: paragraph [0266], disclosing encoding of point cloud data;
paragraph [0135], disclosing quantization of the point cloud data),
voxelizing the point cloud data (Sugio: paragraphs [0155] and [0156], disclosing voxelizing the
point cloud data);
splitting the point cloud data based on a prediction unit (Sugio: paragraph [0155], disclosing encoding
based on a prediction unit; Yea: paragraph [0059], disclosing partition of point cloud data);
classifying points of the point cloud data based on attribute data of the point cloud data (Sugio: paragraph
[0531], disclosing calculation of a difference between the volume whose attribute information is
most similar to the target block and the target block) and generating an anchor for the points (Yea:
paragraphs [0008] through [0014], disclosing generation of an anchor based on predicting attribute
values of 3D point data);
classifying, based on the anchor, points for motion vector estimation, the classified points being included in a
group (Yea: paragraphs [0008], disclosing grouping according to a set of k-nearest neighbors of a
current point based on associated attribute values; Cao: paragraph [0137], disclosing use of motion
vectors for point cloud encoding);
generating a motion vector for the group and performing motion compensation based on the motion vector
(Cao: paragraph [0137], disclosing use of motion vectors for point cloud encoding; paragraphs
[0040] and [0051], disclosing coding of attribute and geometry information); and
encoding the attribute data of the point cloud data based on the motion vector (Cao: paragraphs [0040]
and [0051], disclosing coding of attribute and geometry information; paragraphs [0137], [0200] and
[0201], disclosing use of motion vectors for point cloud encoding).
The motivation for combining Sugio, Sugio II, Yea, and Cao has been discussed in connection with claim 5, above.
Regarding claim 14, Sugio, Sugio II, Yea, and Cao disclose: The method of claim 10, wherein the decoding of the point cloud data comprises:
entropy-decoding the bitstream (Sugio: paragraph [0537], disclosing entropy-decoding of the
bitstream);
dequantizing the point cloud data (Sugio: paragraph [0266], disclosing coding of point cloud data;
paragraph [0538], disclosing inverse quantizing of coefficients);
inversely transforming coordinates of the point cloud data (Sugio: paragraph [0539], disclosing
inversing transforming of data);
splitting the point cloud data based on a prediction unit (Sugio: paragraph [0155], disclosing encoding
based on a prediction unit; Yea: paragraph [0059], disclosing partition of point cloud data);
decoding a motion vector for the point cloud data (Cao: paragraphs [0068], [0069] and [0137],
disclosing decoding of motion vector);
classifying points of the point cloud data based on an anchor and a threshold for the point cloud data based on
attribute data of the point cloud data (Yea: paragraphs [0008] through [0014], disclosing generation of an
anchor based on an attribute information and the threshold), and
performing motion compensation on the classified points (Cao: paragraph [0023], disclosing motion
compensation on the decoded point cloud).
The motivation for combining Sugio, Sugio II, Yea, and Cao has been discussed in connection with claim 5, above.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher Braniff whose telephone number is (571)270-5009. The examiner can normally be reached M-F 7AM to 4PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thai Tran can be reached at (571) 272-7382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHRISTOPHER T. BRANIFF
Primary Examiner
Art Unit 2484
/CHRISTOPHER BRANIFF/Primary Examiner, Art Unit 2484