DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1-2, 9-11, 13 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iguchi et al (US20220327745) in view of Tourapis et al (US20200021856).
Regarding claim 1, Iguchi teaches a method for encoding point cloud attribute information of a point cloud, the method comprising:
acquiring the point cloud that includes a plurality of points, each of the plurality of points in the point cloud including N pieces of attribute information, and N being a positive integer greater than 1, the N pieces of attribute information including at least one of a color attribute, a reflectivity attribute, a normal vector attribute, or a material attribute;
(Iguchi, Figs. 1 and 5, encoding system 4601; encoder 4613 takes point cloud data from point cloud data generator 4618; “Point cloud data includes data on a plurality of points. Data on each point includes geometry information (three-dimensional coordinates) and attribute information associated with the geometry information”, [0208]; and explicitly teaching the types of attributes: “Data on each point may include attribute information (attribute) on a plurality of types of attributes. A type of attribute is color or reflectance, for example”, [0209]; an encoding system acquires point cloud data containing multiple points; each point possesses a "plurality of types" of attributes (which maps to N pieces of attribute information where N > 1) and specifically lists "color" and "reflectance" (reflectivity) as examples of those attribute values)
Iguchi does not expressly disclose but Tourapis teaches:
determining whether N pieces of attribute information of a previous point of a current point in the plurality of points have been encoded;
(Tourapis teaches searching for and determining if prior points have been processed/encoded to use them as predictors: "For each point i, look for the h nearest neighbors (n1, n2, ..., nh) already processed (nj<i)", [0230]; "If the distance between the current point and the last processed point is lower than a threshold, use the neighbors of the last point... The previous idea could be generalized to n=1, 2, 3, 4 ... last points", [0266-0267]; "The prediction strategy for the current point attribute could also be adapted based on already encoded/decoded attribute/attribute channel values", [0407]; furthermore, Iguchi supports this by teaching verifying an encoded point in memory: “predictor 3143 may generate the predicted value using a decoded value of an item of attribute information on each three-dimensional point stored in memory 3149”, [0273]; Tourapis describes a sequential encoding process where the system specifically checks for "already processed" (encoded) neighboring points, such as the "last processed point" or previous points in an index; evaluating whether these prior points are already encoded so they can be used for prediction maps directly to the claimed step of determining whether a previous point's attribute information has been encoded; Iguchi confirms this by checking previously processed attribute values stored in memory)
determining a to-be-coded value for each of the N pieces of attribute information of the current point of the plurality of points based on the N pieces of attribute information of the previous point of the current point in the plurality of points having been encoded;
(Tourapis teaches an encoder determining a predicted attribute value and subsequent prediction residual for an attribute of a point in a point cloud using the attribute values of the already encoded/processed previous points: “an encoder may include a predictor that determines a predicted attribute value of an attribute of a point in a point cloud based on attribute values for similar attributes of neighboring points”, [0046]; "Predict attributes and entropy encode them", [0230]; calculating a prediction for the current point's attributes based on the known, already-processed attribute values of neighboring (previous) points; by calculating a predicted value and generating a prediction residual (the "to-be-coded value") derived from these previously encoded points, Tourapis teaches this limitation)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Tourapis into the system or method of Iguchi in order to enable a predictive coding method of predicting, encoding, and decoding the attribute value of a current point in a point cloud from the attribute values of its already encoded/processed previous neighboring points for exploiting Spatial Redundancy and reducing data size. The combination of Iguchi and Tourapis also teaches other enhanced capabilities.
The combination of Iguchi and Tourapis further teaches:
selecting at least one of
(i) an encoder among plural encoders or
(ii) a coding mode among plural coding modes for the to-be-coded value for each of the N pieces of attribute information of the current point; and
(Iguchi, Fig. 13; “The attribute information encoder may include a plurality of encoders that perform different encoding methods. For example, the attribute information encoder may selectively use any of the two methods described below in accordance with the use case”, [0255], “Attribute information encoder A100 includes LoD attribute information encoder A101 and transformed-attribute-information encoder A102”, [0256]; an encoding system possesses multiple, distinct attribute encoders (e.g., an LoD encoder and a transformed-attribute encoder); "selectively" choosing which of these plural encoders or encoding methods to use depending on the specific use case)
encoding the to-be-coded values of the N pieces of attribute information of the current point respectively based on the selected at least one of the encoder or the coding mode for each to-be-coded value to obtain a code stream of the point cloud.
(Iguchi, Figs. 6 and 13, “First encoder 4630 generates encoded data (encoded stream) by encoding point cloud data in the first encoding method. First encoder 4630 includes ..., attribute information encoder 4632”, [0229], which generates encoded attribute information; “attribute information (attribute) on a plurality of types of attributes”, [0209]; Figs. 13, 15 and 16, attribute information encoder 3140 or 6600 that generates bitstream output; once the specific encoder/mode is selected, the designated attribute information encoder processes the attribute values (the to-be-coded values) and performs entropy or arithmetic encoding to output the final "encoded data (encoded stream)" or bitstream for the point cloud)
Regarding claim 2, the combination of Iguchi and Tourapis teaches its/their respective base claim(s).
The combination further teaches the method according to claim 1, wherein the to-be-coded value for each of the N pieces of attribute information comprises one of
a residual value of the respective one of the N pieces of attribute information,
(Iguchi, Fig. 13, [0256]; Fig. 15, “Prediction residual calculator 3144 calculates (generates) a prediction residual of the predicted value of the item of the attribute information”, [0266])
a transformation coefficient of the respective one of the N pieces of attribute information, and
(Iguchi, Fig. 16, “Haar transformer 6602 generates the coding coefficient by applying the Haar transform to the attribute information”, [0275]
a transformation coefficient of an attribute residual of the respective one of the N pieces of attribute information.
Regarding claims 9 and 19, Iguchi teaches a method for decoding point cloud attribute information of a point cloud, the method comprising:
receiving a code stream of the point cloud that includes a plurality of points, each of the plurality of points in the point cloud including N pieces of attribute information, and N being a positive integer greater than 1, each of the N pieces of attribute information including a respective to-be-decoded value, the N pieces of attribute information including at least one of a color attribute, a reflectivity attribute, a normal vector attribute, or a material attribute;
(Iguchi, Fig. 8; “First decoder 4640 generates point cloud data by decoding encoded data (encoded stream) encoded in the first encoding method in the first encoding method. First decoder 4640 includes ..., attribute information decoder 4643”, [0237], which generates decoded attribute information; Figs. 14, 17 and 18; “Data on each point may include attribute information (attribute) on a plurality of types of attributes. A type of attribute is color or reflectance, for example”, [0209]; receiving an encoded stream and decoding it; each point possesses a "plurality of types" of attributes (which maps to N pieces of attribute information where N > 1) and specifically lists "color" and "reflectance" (reflectivity) as examples of the attribute values that are to be decoded)
Iguchi does not expressly disclose but Tourapis teaches:
determining whether N pieces of attribute information of a previous point of a current point in the plurality of points have been decoded;
(Tourapis teaches searching for and determining if prior points have been processed/decoded to use them as predictors: "The prediction strategy for the current point attribute could also be adapted based on already encoded/decoded attribute/attribute channel values", [0407]; "If the distance between the current point and the last processed point is lower than a threshold, use the neighbors of the last point... The previous idea could be generalized to n=1, 2, 3, 4 . . . last points", [0266-0267]; furthermore, Iguchi supports this by teaching verifying a decoded point in memory at the decoder: “predictor 3153 generates the predicted value using a decoded value of an item of attribute information on each three-dimensional point stored in memory 3157”, [0286]; Tourapis describes a sequential decoding process where the system checks for "already decoded" attribute values of neighboring points (such as the "last processed point") to adapt its prediction strategy; evaluating whether these prior points are already decoded so they can be used for predicting the current point maps directly to the claimed step of determining whether a previous point's attribute information has been decoded; Iguchi confirms this by checking previously decoded attribute values stored in the decoder's memory)
decoding the to-be-decoded values of the N pieces of attribute information of the current point respectively based on the selected at least one of the decoder or the decoding mode for each to-be-decoded value in response to the N pieces of attribute information of the previous point of the current point in the plurality of points being decoded; and
(Tourapis, “a system includes a decoder configured to: receive compressed attribute information for a point cloud comprising at least one assigned attribute value for at least one point of the point cloud and data indicating, for other points of the point cloud, respective attribute correction values for respective attributes of the other points. The decoder is further configured to, for each of respective other ones of the points of the point cloud other than the at least one point, identify a set of neighboring points to a point being evaluated, determine a predicted attribute value for the point being evaluated based, at least in part, on predicted or assigned attribute values for the neighboring points, and adjust the predicted attribute value for the point being evaluated based, at least in part, on an attribute correction value for the point included in the compressed attribute information”, [0006]; Tourapis further teaches traversing sequentially and predicting based on the previous point: “a prediction evaluator may select a neighboring point to the starting point as a next point to evaluate... decoder may determine the same evaluation order for evaluating the points... identifying next nearest neighbors in an index according to the space-filling curve order”, [0080]; and "Here, the contexts to encode the k-th coefficient depending on the values of the previous coefficients", [0365]; Tourapis teaches a sequential, predictive decoding scheme where points are ordered along a space-filling curve; because the actual decoding of a "current point" relies on the predicted or reconstructed attribute values of neighboring points that have already been processed in the sequence (the "previous" points or "previous coefficients"), Tourapis teaches decoding the current point in direct response to the attribute information of the previous point(s) being decoded)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Tourapis into the system or method of Iguchi in order to enable a predictive coding method of predicting, encoding, and decoding the attribute value of a current point in a point cloud from the attribute values of its already-decoded previous neighboring points for exploiting spatial redundancy and reducing data size. The combination of Iguchi and Tourapis also teaches other enhanced capabilities.
The combination of Iguchi and Tourapis further teaches:
selecting at least one of
(i) a decoder among plural decoders or
(ii) a decoding mode among plural decoding modes for the to-be-decoded value for each of the N pieces of attribute information of the current point of the plurality of points;
(Iguchi, Fig. 14; “The attribute information decoder may include a plurality of decoders that perform different decoding methods. For example, the attribute information decoder may selectively use any of the two methods described below for decoding based on the information included in the header or metadata”, [0258], “Attribute information decoder A110 includes LoD attribute information decoder A111 and transformed-attribute-information decoder A112”, [0259]; a system equips with multiple different attribute decoders (e.g., an LoD decoder and a transformed-attribute decoder); "selectively" choosing which of these plural decoders (or decoding modes) to use for a given point based on metadata, directly mapping to the claimed selection step)
obtaining a reconstruction value for each of the N pieces of attribute information of the current point based on the decoded to-be-decoded value of the respective one of the N pieces of attribute information of the current point.
(Iguchi, Figs. 8 and 14; “Decoded value generator 3156 generates a decoded value by adding the predicted value generated by predictor 3153 and the prediction residual inverse-quantized by inverse quantizer 3155 together”, [0285]; Figs. 17-18, the attribute information decoder processes the decoded bitstream (the "to-be-decoded value"), such as by inverse-quantizing prediction residuals or coding coefficients, and combines them with predicted values to obtain the final reconstructed attribute values for each point)
Regarding claims 10 and 20, the combination of Iguchi and Tourapis teaches its/their respective base claim(s).
The combination further teaches the method according to claim 9, wherein the to-be-decoded value for each of the N pieces of attribute information comprises one of
a residual value of the respective one of the N pieces of attribute information,
(Iguchi, Figs. 14 and 17; “Arithmetic decoder 3154 arithmetically decodes the prediction residual in the bitstream obtained from attribute information encoder 3140 shown in FIG. 15”, [0283])
a transformation coefficient of the respective one of the N pieces of attribute information, and
(Iguchi, Fig. 18; “Arithmetic decoder 6611 arithmetically decodes ZeroCnt and the coding coefficient included in a bitstream”, [0288], “Inverse quantizer 6612 inverse quantizes the arithmetically decoded coding coefficient. Inverse Haar transformer 6613 applies the inverse Haar transform to the coding coefficient after the inverse quantization”, [0289])
a transformation coefficient of an attribute residual of the respective one of the N pieces of attribute information.
Regarding claim 11, the combination of Iguchi and Tourapis teaches its/their respective base claim(s).
The combination further teaches the method according to claim 9, wherein the decoding the to-be-decoded values of the N pieces of attribute information of the current point comprises:
decoding the to-be-decoded value for each of the N pieces of attribute information of the current point in the code stream according to a preset decoding sequence to obtain the decoded to-be-decoded values of the N pieces of attribute information of the current point.
(Iguchi, Fig. 14, selectively choose either decoder A111 or decoder A112, [0258]; when using decoder A111 (Fig. 17, [0278]), a specific sequence as shown in Fig. 17 must be followed; similarly, when using decoder A112 (Fig. 18, [0287]), a specific sequence as shown in Fig. 18 must be followed)
Regarding claim 13, the combination of Iguchi and Tourapis teaches its/their respective base claim(s).
The combination further teaches the method according to claim 9, wherein:
the plural decoders are plural entropy decoders, and
(Iguchi, Fig. 61, “a plurality of attribute information decoders 7043”, [0524]; each of attribute information decoders 7043 may contain an entropy decoder 7051 (Fig. 62, [0530])
the plural decoding modes includes at least one of
an exponential Golomb decoding,
an arithmetic decoding, and
(Iguchi, Fig. 17, arithmetic decoder 3154)
a context-adaptive arithmetic decoding.
Regarding claim 16, the combination of Iguchi and Tourapis teaches its/their respective base claim(s).
The combination further teaches the method according to claim 13, wherein the obtaining the reconstruction value for each of the N pieces of attribute information comprises:
determining K reference points of the current point from decoded points of the plurality of points of the point cloud for jth attribute information of the N pieces of attribute information of the current point, K being a positive integer;
(Iguchi, Fig. 17, arithmetic
determining a predicted value of the jth attribute information of the current point according to jth attribute information of each of the K reference points; and
determining a reconstruction value of the jth attribute information of the current point according to the predicted value and a residual value of the jth attribute information of the current point, the residual value of the jth attribute information being included in the to-be-decoded value of the jth attribute information.
Regarding claim 18, the combination of Iguchi and Tourapis teaches its/their respective base claim(s).
The combination further teaches the method according to claim 13, wherein the obtaining the reconstruction value for each of the N pieces of attribute information of the current point comprises:
performing an inverse transformation on a transformation coefficient of jth attribute information of the current point according to the jth attribute information in the N pieces of attribute information of the current point to obtain the reconstruction value of the jth attribute information, the transformation coefficient being included in the to-be-decoded value of the jth attribute information.
(Iguchi, Figs. 14, 17 and 18, [0258-0260], [0278-0289])
Allowable Subject Matter
Claim(s) 3-8, 12 and 14-17 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening Claim(s).
The following is a statement of reasons for the indication of allowable subject matter:
Claim(s) 3-8, 12 and 14-17 recite(s) the limitation(s) directed to encoding point cloud attributes using run-length coding, utilizing a length mark to indicate non-zero values; context-adaptive arithmetic coding configurations by varying shared or individual entropy encoders and context models; and a length mark first to determine whether the subsequent attribute value is non-zero, which are found in the prior art cited in this office action and from the prior art search.
Response to Arguments
Applicant's arguments filed on 1/20/2026 with respect to one or more of the pending claims have been fully considered but they are not persuasive.
Regarding claim(s) 1, 9 and 19, Applicant, in the remarks, argues that the combination of the cited reference(s) fails to teach the newly amended limitations in the claims.
The Examiner respectfully disagreed. The office action has been updated to address applicant’s argument. See the updated review comments for details.
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 JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 3/26/2026
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