Detailed Office Action
1. This communication is being filed in response to the submission having a mailing date of 10/06/2025 in which a (3) month Shortened Statutory Period for Response has been set.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgements
3. Upon initial entry, claims (1 -20) appear pending for examination, of which (1, 18, 19, 20) are the four (4) parallel running independent claims on record.
Information Disclosure Statement
4. The two (2) submitted IDS dated 10/28/2025 and 10/30/2025 is/are partially in compliance with the provisions of 37 CFR 1.97, being reviewed and considered by the Examiner.
4.1. Multiple entries from the IDS have been discarded, for failure to cite the relevant pages in the publication. Each of the submitted publications must comply with the 37 CFR 1.98 provisions, in order evaluate the corresponded information listed, to be considered by the Office. See also MPEP [37 CFR 1.98(b); - Each publication must be identified by publisher, author (if any), title, relevant pages of the publication, and date and place of the publication.]
Specification
5. The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant's cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Drawings
6. The submitted Drawings on date 10/06/2025 has been accepted and considered under the 37 CFR 1.121 (d).
Claim rejection section
35 USC 103
7. 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 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.
7.1. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
7.2. Claims (1 -3, 7 -11, 13, 15 -20) is/are rejected under 35 U.S.C. 103 as being unpatentable over Dricot; et al. (“On improvement of RAHT to exploit attribute correlation; 2016” hereafter “Lasserre”) in view of Flynn; et al. US 10,964,068 B2; hereafter “Flynn,”
Claim 1. Lasserre discloses the invention substantially as claimed - A method for processing media data, comprising: (e.g. an encoder (page 11) and decoder (page 12) respectively, employing an improving prediction model for region-adaptive hierarchical transform (RAHT) as described in [pages (7 -8), for different nodes/layers [page 6],
More specifically, Lasserre discloses - determining at least one prediction model for region-adaptive hierarchical transform (RAHT), (e.g. see model prediction [pages 2, 14]);
wherein in each prediction model, a first coefficient component is predicted from a second coefficient component; (e.g. see similar model, where combination of AC, DC value components is implemented; [page 11]);
and performing a conversion between a visual media data and a bitstream based on the determining; (encoder (page 11), decoder (page 12) data conversion of the same.)
Given the teachings of Lasserre; et al. as a whole, and under the obvious assumption and purpose of his papers, it is noted that some of the functional steps/components as listed (i.e. no encoder/decoder schematic, nor codec flow chart disclosed), are missed or not fully described in the papers.
For the purpose of additional clarification, and in the same field of endeavor, Flynn discloses – (e.g. a codec ecosystem of the same, (encoder Figs. (9, 15) and decoder Figs (10, 16), similarly employing layer-prediction model for RAHT, Figs (6-10), where combination of AC, DC value components is analogously implemented (Figs. 9, 10), for different nodes/layers [Flynn; 13: 25].)
Therefore, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention, to modify the papers of Lasserre, with the codec architecture of Flynn, in order to provide - an efficient method and device, able to effectively compress large amount of attribute data for point clouds, with RAHT implementation in the process; [Flynn; 1: 50; 4: 30].
Claim 2. Lasserre/Flynn discloses - The method of claim 1, wherein the first coefficient component and the second coefficient component are two different components of an attribute value, or wherein the first coefficient component and the second coefficient component are from a same attribute value or a same sample, or wherein the first coefficient component and the second coefficient component are reconstructed values or transformed domain values; (e.g. see similar in at least Figs. 9, 10), wherein first and second coefficient components are originated from a same attribute or a same sample; [Flynn]; the same motivation applies herein.)
Claim 3. Lasserre/Flynn discloses - The method of claim 1, wherein a linear function or a non-linear function is applied during prediction of the first coefficient component from the second coefficient component and wherein function parameters of the linear function or the non-linear function are derived, pre-defined, or included in the bitstream; (e.g. see similar in at least Figs. 9, 10), wherein first and second coefficient components are originated from a same attribute(s), derived from the bitstream (1002), using by-linear functions operators [Flynn]; the same motivation applies herein.)
Claim 7. Lasserre/Flynn discloses - The method of claim 1, wherein at least one model parameter of each prediction model is derived based on samples reconstructed before a current block or sample, (e.g. see similar in at least Figs. 9, 10), wherein first and second coefficient components are originated from a same attribute or a same sample; [Flynn]) or wherein the at least one prediction model comprises multiple prediction models, and the multiple prediction models are used for cross-component prediction; (e.g. see also cross-component correlation employing luma/color attributes pf the same; [Flynn; 12: 61]; the same motivation applies herein.)
Claim 8. Lasserre/Flynn discloses - The method of claim 1, wherein whether one or more prediction models are applied is signaled or a number of prediction models is signaled, or wherein at least one model parameter of each prediction model is derived based on a least square estimate, or based on an LDL decomposition; (e.g. see tree octree decomposition , by definition implemented in G-PCC; [Flynn; Col. 6 -7]; the same motivation applies herein.)
Claim 9. Lasserre/Flynn discloses - The method of claim 1, wherein the at least one prediction model comprises multiple prediction models, and one or more of the multiple prediction models are selected to be applied, wherein a selection of the one or more of the multiple prediction models is derived by an encoder or by a decoder, wherein additional flags are signalled to indicate a result of the selection, or wherein additional flags are signalled to indicate whether the selection is to be enabled; (e.g. see similar implementation in at least Figs. 9, 10); [Flynn]; the same motivation applies herein.)
Claim 10. Lasserre/Flynn discloses - The method of claim 1, wherein model parameters of the at least one prediction model are signaled, or wherein model parameters of the at least one prediction model are estimated based on least square minimization, or selected from a set of predetermined values, or wherein model parameters of the at least one prediction model are partially signaled and partially derived, or wherein model parameters of the at least one prediction model are estimated, and the model parameters that are estimated are quantized and signaled; (e.g. see similar implementation in at least Figs. 9, 10); [Flynn]; the same motivation applies herein.)
Claim 11. Lasserre/Flynn discloses - The method of claim 1, wherein a prediction model of the at least one prediction model is applied during RAHT coding, and a prediction residual of the first coefficient component is coded by RAHT coding. (e.g. see similar in at least Figs. 9, 10), wherein residue (920) is coded; [Flynn]; the same motivation applies herein.)
Claim 13. The method of claim 1, wherein the at least one prediction model is applied during RAHT coding for predicting a RAHT node of the first coefficient component from a RAHT node of the second coefficient component that has been reconstructed, or wherein the at least one prediction model is applied in a sum of attribute space or applied in a transform domain, or wherein the at least one prediction model is enabled for a subset of RAHT levels, or wherein the subset is signaled. (e.g. see similar in at least Figs. 9, 10), wherein residue (920) is coded; [Flynn]; the same motivation applies herein.)
Claim 15. Lasserre/Flynn discloses - The method of claim 1, wherein the at least one prediction model is enabled or disabled for different RAHT layers, nodes, or regions based on flag(s) signaled per-layer, per-node, or per-region, respectively. (The same rationale and motivation apply as given to Claim 1 above.)
Claim 16. Lasserre/Flynn discloses - The method of claim 1, wherein the conversion comprises encoding the visual media data into the bitstream. (The same rationale and motivation apply as given to Claim 1 above. In addition, see similar encoder/decoder construction of the same employing prediction model for region-adaptive hierarchical transform (RAHT).)
Claim 17. Lasserre/Flynn discloses - The method of claim 1, wherein the conversion comprises decoding the visual media data from the bitstream. (The same rationale and motivation apply as given to Claim 1 above. In addition, see similar encoder/decoder construction of the same employing prediction model for region-adaptive hierarchical transform (RAHT).)
Claim 18. Lasserre/Flynn discloses - An apparatus for processing media data, comprising: a processor; and a non-transitory memory with instructions thereon, wherein the instructions upon execution by the processor, cause the processor to: determine at least one prediction model for region-adaptive hierarchical transform (RAHT), wherein in each prediction model, a first coefficient component is predicted from a second coefficient component; and perform a conversion between a visual media data and a bitstream based on the determination. (Current lists all the same elements as recite in Claim 1 above, but in “Apparatus form” instead, and is/are therefore on the same premise.)
Claim 19. Lasserre/Flynn discloses - A non-transitory computer-readable storage medium storing instructions that cause a processor to: determine at least one prediction model for region-adaptive hierarchical transform (RAHT), wherein in each prediction model, a first coefficient component is predicted from a second coefficient component; and perform a conversion between a visual media data and a bitstream based on the determination. (Current lists all the same elements as recite in Claim 1 above, but in “CRM form” instead, and is/are therefore on the same premise.)
Claim 20. Lasserre/Flynn discloses - A non-transitory computer-readable recording medium storing a bitstream of a media data which is generated by a method performed by a media data processing apparatus, wherein the method comprises: determining at least one prediction model for region-adaptive hierarchical transform (RAHT), wherein in each prediction model, a first coefficient component is predicted from a second coefficient component; and generating a bitstream based on the determining. (Current lists all the same elements as recite in Claim 1 above, but in “CRM form” instead, and is/are therefore on the same premise.)
Claim objection section
8. The below list of claim (4 -6, 12 and 14) are objected to, because of the novel features implemented, but they may be considered for allowance if properly rewritten, and/or if incorporated into the four (4) parallel running independent claims on record.
8.1. The objected list of claims appears as follows:
Claim 4. The method of claim 1, wherein the first coefficient component and the second coefficient component are from different attribute values or from different samples, or wherein the first coefficient component is predicted from neighbors of the second coefficient component in a same or different layer, or wherein the first coefficient component and the second coefficient component are residue values, transformed residue values, or a combination of residue values and transformed residue values, or wherein the first coefficient component (Ypred) is predicted from the second coefficient component (Xrec) that has been reconstructed, and wherein a prediction model of the at least one prediction model is denoted as: Ypred=α*Xrec+β where α and β are parameters of the prediction model.
Claim 5. The method of claim 1, wherein an alternating current (AC) component of the first coefficient component is predicted from an AC component of the second coefficient component that has been reconstructed, and wherein a prediction model of the at least one prediction model is denoted as: Ypred,AC=α*Xrec,AC where Ypred,AC indicates the AC component of the first coefficient component, Xrec,AC indicates the AC component of the second coefficient component, which is a signal obtained by removing a direct current (DC) component from the second coefficient component, and α is a parameter of the prediction model corresponding to Xrec,AC.
Claim 6. The method of claim 1, wherein the first coefficient component is predicted from second coefficient components of a current sample and of neighboring samples, and wherein a prediction model of the at least one prediction model is denoted as: Ypred=α0*Xrec+α1*Xrec,1+…+αN*Xrec,N+β where Ypred indicates the first coefficient component, Xrec indicates a second coefficient component of the current sample, Xrec,i indicates a second coefficient component of an ith neighboring sample, α0, α1…αN and β are parameters of the prediction model, and i and N are integers, or wherein a number of the neighboring samples is signaled, fixed, or determined based on distance criteria, wherein the distance criteria comprises a distance threshold that is fixed or signaled, and wherein the distance criteria specifies that only neighboring samples within the distance threshold are included in the prediction model, or wherein the first coefficient component is predicted from the second coefficient component Xrec with a prediction model of the at least one prediction model with non-linear terms, and wherein the prediction model with squared reconstruction Xrec2as non-linear term is denoted as: Ypred=α0*Xrec+α1*Xrec2+β where α0, α1 and β are motion parameters of the prediction model, or wherein the non-linear terms are signalled, or wherein the prediction model is a fixed model with non-linear terms, or wherein at least one model parameter of each prediction model is signaled in the bitstream, or wherein the first coefficient component is predicted from second coefficient components of a current sample and of neighboring samples with a polynomial prediction model denoted as: Ypred=α0*Xrec+α1*Xrec,1+…+αN*Xrec,N+α'0*Xrec2+β where Ypred indicates the first coefficient component, Xrec indicates a second coefficient component of the current sample, Xrec,i indicates a second coefficient component of an ith neighboring sample, α0, α1…αN, α'0, and β are parameters of the polynomial prediction model, and i and N are integers.
Claim 12. The method of claim 1, wherein a point cloud is divided into blocks of P×Q×R voxels, wherein a prediction model is selectively enabled or disabled for each block, wherein P, Q, and R are signaled or fixed, and wherein for each block that selects the prediction model, model parameters are signaled, are inherited from neighboring voxels, or are predictively coded, or wherein a point cloud is divided into blocks of N points and a prediction model is selectively enabled or disabled for each block, and wherein parameters N are signaled or fixed, wherein for each block that selects the prediction model, model parameters are signaled, are inherited from neighboring blocks, or are predictively coded, wherein the point cloud is reordered based on Morton code before being divided into the blocks, or the point cloud is used as a single block, or wherein a point cloud is divided into regions and a prediction model is selectively enabled or disabled for each region, wherein the regions are derived from clustering algorithms, are signaled, or are derived based on coded geometry information, and wherein for each region that selects the prediction model, model parameters are selectively signaled, are inherited from neighboring regions, or are predictively coded.
Claim 14. The method of claim 1, wherein the at least one prediction model is enabled for a first K1 or last K2 levels of RAHT levels, where K1 and K2 are integers, or wherein the at least one prediction model is enabled for a subset of RAHT levels and the subset is a fixed subset, or wherein model parameters of the at least one prediction model are derived from neighboring nodes, and wherein the neighboring nodes are used as training samples to derive the model parameters, or wherein a cost reduction by employing a prediction model is denoted as: Cost reduction, Cost pred-Cost un pred, where Cost reduction is the cost reduction, Cost pred is a prediction cost of neighboring nodes of a RAHT node with enabling the prediction model, Cost un pred is a prediction cost of the neighboring nodes without enabling the prediction model, and wherein the prediction model is applied for the RAHT node only if Cost reduction is less than a threshold, or wherein model parameters of a prediction model are derived from neighbors only for RAHT nodes included in some regions, or wherein model parameters of a prediction model are derived and signaled conditionally on an RAHT layer level, an RAHT node level, or an RAHT region level, or wherein model parameters of a prediction model are predictively coded across RAHT layers, nodes, or regions, or wherein the at least one prediction model is used in predictive transform attribute coding or is applied only for lossless compression.
Prior Art Citations
9. The following List of prior art, made of record and not relied upon, is/are considered
pertinent to applicant's disclosure:
9.1. Patent documentation
US 11,979,607 B2 H04N19/597; G06T9/001; H04N19/46; Oh; et al.
US 12,267,527 B2 H04N19/196; H04N19/172; H04N19/597; Pham Van; et al.
US 12,684,168 B2 H04N19/597; G06T9/001; H04N19/105; Yang; et al.
US 12,190,552 B2 G06T15/10; H04N19/96; G06T9/40; Gao; et al.
US 12,354,315 B2 H04N19/61; G06T9/40; G06T9/001 Zhang; et al.
US 20250232482 A1 G06T9/001; H04N19/60; H04N19/96 Wang; et al.
US 20260025519 A1 H04N19/33; H04N19/63; H04N19/96 Vishwanath; et al.
9.2. Non-Patent documentation:
_ On improvement of RAHT to exploit attribute correlation; 2016
_ Compression of 3D point cloud using RAHT; Queiroz; 08-2016
CONCLUSIONS
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS PEREZ-FUENTES (luis.perez-fuentes@uspto.gov) whose telephone number is (571) 270 -1168. The examiner can normally be reached on Monday-Friday 8am-5pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, WILLIAM VAUGHN can be reached on (571) 272-3922. The fax phone number for the organization where this application or proceeding is assigned is (571) 272 -3922. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated system, please call (800) 786 -9199 (USA OR CANADA) or (571) 272 -1000.
/LUIS PEREZ-FUENTES/
Primary Examiner, Art Unit 2481.
Prosecution Insights