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 .
Priority
Acknowledgement is made of Applicant’s claim of priority from International Application No. PCT/CN2021/087918, filed April 16, 2021.
Status of Claims
Claims 1, 6-7, 9-16 and 20, 23-24 and 26-27 are pending. Claims 2-5, 8, 17-19, 21-22 and 25 have been canceled.
Response to Arguments
Applicant's arguments filed February 5, 2026 have been fully considered but they are not persuasive. Applicant argues that the Yea reference fails to disclose, teach or suggest the elements of claim 1. Examiner respectfully disagrees. Applicant argues that Yea does not teach the limitation “the M neighbouring points comprise at least a part of first P neighbouring points of the current point” and states that “the M neighbouring points are selected from candidate neighbouring points (i.e., “first P neighbouring points”)”. However, Applicant is reminded that the specification is not read into the claims, and there is no recitation in the claims that requires a selection from a first set of candidate points to obtain a final set of points. As recited in the claims, the M neighbouring points comprise at least a part of first P neighbouring points and P≥M. Therefore, under the broadest reasonable interpretation of the claim, the M neighbouring points could include all of the first P neighbouring points, and no “selection” process is necessary. Thus, Yea’s teaching that a group of neighboring points is determined as a set of nearest neighboring points in terms of geometric distances to the current point (see, for example, Yea, Para. [0080]) is sufficient to teach this limitation because Yea’s set of nearest neighbouring points determined from the geometric distance to the current point is “the first P neighbouring points from the current point based on the distance from near to far” and in turn is “the M neighbouring points” comprising “at least a part of the first P neighbouring points” used to calculate the weighted average. Therefore, the 35 USC 103 rejection of the claims is maintained.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 6, 9-11 and 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yea et al. (US 2020/0107048 A1).
Regarding claim 1, Yea teaches an encoding method, comprising:
determining neighbouring points of a current point in a point cloud to be encoded (Para. [0041], a set of neighboring points of the current point can first be determined using various algorithms), and calculating a first parameter according to the neighbouring points, wherein the first parameter is a difference between a maximum value and a minimum value among reconstructed values of first attributes of the neighbouring points (Para. [0070], maxDiff is a measurement of sample-value variability, which is defined as the maximum difference of attribute values among the neighbors);
in response to the first parameter being less than a threshold, determining a prediction value of the first attribute of the current point by using a preset first prediction mode (Para. [0070], If maxDiff(NN.sub.Q) is smaller than a specified threshold, then a neighboring area surrounding the current sample may be smooth and homogeneous and a weighted average of neighboring samples may be used as the prediction for the current sample);
calculating a difference between an original value of the first attribute of the current point and the prediction value as a residual value of the first attribute of the current point (Para. [0091], a distortion can be measured by a difference between the original (or true) attribute value of the current point and a candidate prediction (candidate reconstructed attribute value));
encoding the residual value subjected to quantization (Para. [0038], a residual signal can be generated by subtracting the attribute prediction value from a respective original attribute value of the current point. The residual signal can then, individually or in combination with other residual signals, be further compressed. For example, transform and/or quantization operations may be performed, and followed by entropy coding of resulting signals. The compressed residual signal can be transmitted to the encoder in a bit stream); and
signalling identification information of the first prediction mode, wherein the identification information is used for indicating a prediction mode (Para. [0073], the encoder may perform the maxDiff condition check using the original (uncoded) neighbor samples and then signal a one-bit flag indicating whether the weighted average prediction is used),
wherein the first prediction mode comprises: using a weighted average of attribute values of neighbouring points as the prediction value (Para. [0074], a weighted average of neighboring samples may be used as the prediction for the current sample if maxDiff(NN.sub.Q) is determined to be smaller than the specified threshold),
wherein using the weighted average of the attribute values of the neighbouring points as the prediction value comprises:
using a weighted average of attribute values of M neighbouring points as the prediction value, where M is a positive integer greater than 1 (Para. [0081], reconstruction of the current point in a prediction step in a lifting scheme is based on a weighted average prediction of the reconstructed attribute values of the plurality of neighboring points (i.e., greater than 1). Para. [0076], N is the number of nearest-neighbor samples in NN.sub.Q),
wherein the M neighbouring points comprise at least a part of first P neighbouring points of the current point, where P≥M (Para. [0080], A group of neighboring points of the current point can be determined from the first points. For example, the group of neighboring points can be a set of nearest neighboring points in terms of geometric distances to the current point),
wherein the first P neighbouring points of the current point are the first P neighbouring points from the current point based on the distance from near to far, where P is a positive integer (Para. [0080], A group of neighboring points of the current point can be determined from the first points. For example, the group of neighboring points can be a set of nearest neighboring points in terms of geometric distances to the current point).
Regarding claim 6, Yea teaches the encoding method of claim 1, and further teaches wherein the neighbouring points of the current point comprise R neighbouring points, where R is a positive integer greater than 1 (Para. [0081], reconstruction of the current point in a prediction step in a lifting scheme is based on a weighted average prediction of the reconstructed attribute values of the plurality of neighboring points (i.e., greater than 1). Para. [0076], N is the number of nearest-neighbor samples in NN.sub.Q).
Regarding claim 9, Yea teaches the encoding method of claim 1, further comprising:
in response to the first parameter being not less than the threshold, selecting a second prediction mode by adopting a rate-distortion optimization (RDO) (Para. [0071], if maxDiff is not smaller than the specified threshold, the one of the neighboring samples may be chosen as the best candidate and is used as the prediction for the current sample. The chosen neighboring sample may have a lowest RD cost of each of the neighboring samples calculated based on the above-described cost function. Para. [0069], a rate-distortion (RD) decision-based predictor may be used for the prediction step. The predictor may choose the prediction signal for a sample Q given its neighborhood);
determining the prediction value of the first attribute of the current point by using the second prediction mode (Para. [0071], if maxDiff is not smaller than the specified threshold, the one of the neighboring samples may be chosen as the best candidate and is used as the prediction for the current sample. The chosen neighboring sample may have a lowest RD cost of each of the neighboring samples calculated based on the above-described cost function); and
signalling identification information of the second prediction mode, wherein the identification information is used for indicating a prediction mode (Para. [0073], the encoder may perform the maxDiff condition check using the original (uncoded) neighbor samples and then signal a one-bit flag indicating whether the weighted average prediction is used. If the signal does not indicate the weighted average prediction is used, it may then have to additionally signal which one of the neighboring samples is used as prediction after performing an RD cost function on each of the neighboring samples).
Regarding claim 10, Yea teaches the encoding method of claim 9, and further teaches wherein the second prediction mode comprises at least one of:
using a weighted average value of attribute values of neighbouring points as the prediction value (Para. [0070], If maxDiff(NN.sub.Q) is smaller than a specified threshold, then a neighboring area surrounding the current sample may be smooth and homogeneous and a weighted average of neighboring samples may be used as the prediction for the current sample); or using an attribute value of one of the neighbouring points as the prediction value (Para. [0071], if maxDiff(NN.sub.Q) is not smaller than the specified threshold, then one of the neighboring samples may be chosen as the best candidate and is used as the prediction for the current sample. The chosen neighboring sample may have a lowest RD cost among the cost of each one of the neighboring samples calculated based on the above-described cost function).
Regarding claim 11, Yea teaches the encoding method of claim 10, and further teaches wherein using the weighted average of the attribute values of the neighbouring points as the prediction value comprises:
using a weighted average of attribute values of S neighbouring points as the prediction value, where S is a positive integer greater than 1 (Para. [0070], If maxDiff(NN.sub.Q) is smaller than a specified threshold, then a neighboring area surrounding the current sample may be smooth and homogeneous and a weighted average of neighboring samples (i.e., greater than one sample) may be used as the prediction for the current sample), and
the identification information is a preset index value and has global uniqueness (Para. [0077], the encoder here may need to signal only one flag, which is a single index (i.e., preset index value) indicating the best candidate among the neighboring samples and the weighted average predictor. The decoder may receive the single index and the single index may indicate whether the weighted average predictor is used or which one of the neighboring samples is used as the prediction for the current sample).
Regarding claim 13, Yea teaches the encoding method of claim 11, and further teaches wherein the S neighbouring points comprise at least a part of first U neighbouring points of the current point, where U≥S (Para. [0081], reconstruction of the current point in a prediction step in a lifting scheme is based on a weighted average prediction of the reconstructed attribute values of the plurality of neighboring points (i.e., S comprises at least a part of U neighboring points). Para. [0076], N is the number of nearest-neighbor samples in NN.sub.Q).
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.
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 7, 12 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Yea et al. (US 2020/0107048 A1) in view of Hur et al. (US 2021/0319581 A1, filed January 25, 2021).
Regarding claim 7, Yea teaches the encoding method of claim 1, as described above.
Although Yea teaches a predicted attribute value is a weighted average of the attribute values of the neighboring points (i.e., M is greater than 1) (Yea, Para. [0081]), Yea does not explicitly teach “wherein a value of M is 3, 4 or 5”. However, in an analogous field of endeavor, Hur teaches the neighbor point set configuration unit searches for X (e.g., 3) NN points among points within a search range in a group having the same or lower LOD (i.e., a large distance between nodes) (i.e., a value of M is 3) (Hur, Para. [0425]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Yea with the teachings of Hur by including that the M neighbour points is a value of 3, 4 or 5. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for efficiently processing a large amount of point data, as recognized by Hur. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Regarding claim 12, Yea teaches the encoding method of claim 10, as described above.
Although Yea teaches an index value indicating prediction mode (Para. [0077]), Yea does not explicitly teach “wherein using the attribute value of one of the neighbouring points as the prediction value comprises: using an attribute value of a T-th neighbouring point of the current point as the prediction value, where T is a positive integer, and the identification information is an index value of the T-th neighbouring point”. However, in an analogous field of endeavor, Hur teaches When configuring the neighbor point set, the number of neighbor points included in the neighbor point set registered in each predictor is equal to or less than X (e.g., 3) by applying the maximum neighbor point distance (i.e., T=3) (Hur, Para. [0447]). Hur further teaches the value of the prediction mode (or predictor index) equal to 0 may indicate that the attribute value is predicted through the weighted average, and the value equal to 1 may indicate that the attribute value is predicted through the first neighbor node (i.e., the neighbor point). The value equal to 2 may indicate that the attribute value is predicted through the second neighbor node, and the value equal to 3 may indicate that the attribute value is predicted through the third neighboring node (Hur, Para. [0456]).
The proposed combination as well as the motivation for combining the Yea and Hur references presented in the rejection of Claim 7, apply to Claim 12 and are incorporated herein by reference. Thus, the method recited in Claim 12 is met by Yea in view of Hur.
Regarding claim 14, Yea teaches the encoding method of claim 11, as described above.
Although Yea teaches a predicted attribute value is a weighted average of the attribute values of the neighboring points (i.e., M is greater than 1) (Yea, Para. [0081]), Yea does not explicitly teach “wherein a value of S is 3, 4 or 5”. However, in an analogous field of endeavor, Hur teaches the neighbor point set configuration unit searches for X (e.g., 3) NN points among points within a search range in a group having the same or lower LOD (i.e., a large distance between nodes) (i.e., a value of S is 3) (Hur, Para. [0425]).
The proposed combination as well as the motivation for combining the Yea and Hur references presented in the rejection of Claim 7, apply to Claim 14 and are incorporated herein by reference. Thus, the method recited in Claim 14 is met by Yea in view of Hur.
Regarding claim 15, Yea in view of Hur teaches the encoding method of claim 12, wherein a value of T is 1, 2 or 3 (Hur, Para. [0425], the neighbor point set configuration unit searches for X (e.g., 3) NN points among points within a search range in a group having the same or lower LOD (i.e., a large distance between nodes) (i.e., a value of T is 3)).
The proposed combination as well as the motivation for combining the Yea and Hur references presented in the rejection of Claim 7, apply to Claim 15 and are incorporated herein by reference. Thus, the method recited in Claim 15 is met by Yea in view of Hur.
Claims 16, 20, 22-23 and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over Yea et al. (US 2020/0107048 A1) in view of Sugio et al. (US 2021/0227259 A1, filed April 6, 2021).
Regarding claim 16, Yea teaches a decoding method, comprising:
parsing a bitstream to obtain identification information of a prediction mode of a current point in a point cloud to be decoded (Yea, Para. [0077], the decoder may receive the single index and the single index may indicate whether the weighted average predictor is used or which one of the neighboring samples is used as the prediction for the current sample);
determining a prediction value of a first attribute of the current point by using the prediction mode indicated by the identification information (Yea, Para. [0087], an attribute value predictor for the current point is determined used in a prediction step in a lifting scheme based on a weighted sum function when a measurement of variability of the reconstructed attribute values of the plurality of neighboring points is below a threshold, wherein the prediction step is performed before an updating step in the lifting. For example, the decoder determines may determine whether a measurement of variability of the reconstructed attribute values of the plurality of neighboring points is smaller than a specified threshold, and a weighted average of neighboring samples may be used as the prediction for the current sample if the measurement of variability of the reconstructed attribute values of the plurality of neighboring points determined to be smaller than the specified threshold. The measurement of variability of the reconstructed attribute values of the plurality of neighboring points may be a maximum difference of attribute values among the neighboring samples, maxDiff(NN.sub.Q). In the weighted average prediction, the weight may be inversely proportional to the geometric distance of the current sample from each of the neighbor samples. If the decoder determines that maxDiff(NN.sub.Q) is not smaller than the specified threshold, then the decoder may receive an index indicating which neighboring sample (best candidate) is to be used as the attribute value predictor for the current sample);
parsing the bitstream to obtain a residual value of the first attribute of the current point (Yea, Para. [0039], At the decoder, a residual signal can be recovered by performing an inverse of the coding process at the encoder for coding a residual signal. With the obtained attribute prediction and the recovered residual signal corresponding to the current point, a reconstructed attribute of the current point can be obtained),
wherein in response to the identification information being a preset index value, using a weighted average of attribute values of neighbouring points as the prediction value of the current point (Para. [0074], a weighted average of neighboring samples may be used as the prediction for the current sample if maxDiff(NN.sub.Q) is determined to be smaller than the specified threshold),
wherein using the weighted average of the attribute values of the neighbouring points as the prediction value comprises:
using a weighted average of attribute values of M neighbouring points as the prediction value, where M is a positive integer greater than 1 (Para. [0081], reconstruction of the current point in a prediction step in a lifting scheme is based on a weighted average prediction of the reconstructed attribute values of the plurality of neighboring points (i.e., greater than 1). Para. [0076], N is the number of nearest-neighbor samples in NN.sub.Q),
wherein the M neighbouring points comprise at least a part of first P neighbouring points of the current point, where P≥M (Para. [0079], the set of points can include first points of which attribute values have been reconstructed and available for attribute prediction of a current point included in the set of points. Para. [0080], A group of neighboring points (i.e., M neighbouring points) of the current point can be determined from the first points (i.e., first P neighbouring points). For example, the group of neighboring points can be a set of nearest neighboring points in terms of geometric distances to the current point),
wherein the first P neighbouring points of the current point are the first P neighbouring points from the current point based on the distance from near to far, where P is a positive integer (Para. [0080], A group of neighboring points of the current point can be determined from the first points. For example, the group of neighboring points can be a set of nearest neighboring points in terms of geometric distances to the current point).
Although Yea teaches that with the obtained attribute prediction and the recovered residual signal corresponding to the current point, a reconstructed attribute of the current point can be obtained (Yea, Para. [0039]) and teaches performing an inverse of the quantization operations (i.e., dequantization) on the residual signals (Yea, Para. [0051]), Yea does not explicitly teach “calculating a sum of a residual value subjected to dequantization and the prediction value as a reconstructed value of the first attribute of the current point”. However, in an analogous field of endeavor, Sugio teaches decoding the attribute information by summing the decoded difference (prediction residual) (i.e., residual value subject to dequantization) and the predicted value (i.e., prediction value) (Sugio, Para. [0829]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Yea with the teachings of Sugio by including determining the reconstructed value of the first attribute of the current point by summing the dequantized residual value and the predicted value. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for reducing the code amount in encoding of three-dimensional data, as recognized by Sugio. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Regarding claim 23, Yea in view of Sugio teaches the decoding method of claim 16, wherein the neighbouring points of the current point comprise R neighbouring points, where R is a positive integer greater than 1 (Yea, Para. [0081], reconstruction of the current point in a prediction step in a lifting scheme is based on a weighted average prediction of the reconstructed attribute values of the plurality of neighboring points (i.e., greater than 1). Para. [0076], N is the number of nearest-neighbor samples in NN.sub.Q).
Claims 20 and 26 recite decoders with elements corresponding to the steps recited in Claims 16 and 23, respectively. Therefore, the recited elements of these claims are mapped to the proposed combination in the same manner as the corresponding steps in their corresponding method claims. Additionally, the rationale and motivation to combine the Yea and Sugio references, presented in rejection of Claim 16, apply to these claims. Finally, the combination of the Yea and Sugio references discloses a processor and a memory (Yea, Para. [0057], the encoder and decoder can be implemented as software or firmware including instructions stored in a non-volatile (or non-transitory) computer-readable storage medium. The instructions, when executed by processing circuitry, such as one or more processors, causing the processing circuitry to perform functions of the encoder and decoder).
Claims 24 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Yea et al. (US 2020/0107048 A1) in view of Sugio et al. (US 2021/0227259 A1, filed April 6, 2021), as applied to claims 16, 20, 22-23 and 25-26 above, and further in view of Hur et al. (US 2021/0319581 A1, filed January 25, 2021).
Regarding claim 24, Yea in view of Sugio teaches the decoding method of claim 16, as described above.
Although Yea in view of Sugio teaches a predicted attribute value is a weighted average of the attribute values of the neighboring points (i.e., M is greater than 1) (Yea, Para. [0081]), they do not explicitly teach “wherein a value of M is 3, 4 or 5”. However, in an analogous field of endeavor, Hur teaches the neighbor point set configuration unit searches for X (e.g., 3) NN points among points within a search range in a group having the same or lower LOD (i.e., a large distance between nodes) (i.e., a value of M is 3) (Hur, Para. [0425]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Yea in view of Sugio with the teachings of Hur by including that the M neighbour points is a value of 3, 4 or 5. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for efficiently processing a large amount of point data, as recognized by Hur. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 27 recites a decoder with elements corresponding to the steps recited in Claim 24. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Yea, Sugio and Hur references, presented in rejection of Claim 16, apply to this claim. Finally, the combination of the Yea, Sugio and Hur references discloses a processor and a memory (Yea, Para. [0057], the encoder and decoder can be implemented as software or firmware including instructions stored in a non-volatile (or non-transitory) computer-readable storage medium. The instructions, when executed by processing circuitry, such as one or more processors, causing the processing circuitry to perform functions of the encoder and decoder).
Conclusion
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/Emma Rose Goebel/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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