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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/22/2026 has been entered.
Response to Arguments
Applicant's arguments filed on 05/22/2026 have been fully considered but they are not persuasive.
Regarding section 112, Applicant’s representative provides an explanation: “In the instant application, quantization is performed on an unbiased latent representation (y* = y - μ; see Eq. 5). For each layer, the quantization intervals of the current layer are determined such that the sum of their sizes equals the size of the single quantization interval in the previous layer that contains the unbiased latent representation component (see page 15 and the description of FIG. 4). The current-layer intervals are formed by deriving temporary boundaries that are spaced according to the learned quantization step-size vector b.1,i, centered on the center value vf_ 1 i of the previous-layer interval that contains the latent value, then clipping any temporary boundaries that fall outside the previous-layer interval's bottom/top boundaries (L B1,i and U B1,i) back to those boundaries (see Eq. 8 on pp. 15-16 and the accompanying explanation).”
Examiner notes that while Applicant’s representative provides his own interpretation of the Specification which naturally aligns with the proposed claim amendments. However, the arguments of counsel cannot take the place of evidence in the record. In re Schulze, 346 F.2d 600, 602, 145 USPQ 716, 718 (CCPA 1965); In re Geisler, 116 F.3d 1465, 43 USPQ2d 1362 (Fed. Cir. 1997) (“An assertion of what seems to follow from common experience is just attorney argument and not the kind of factual evidence that is required to rebut a prima facie case of obviousness.”).
Specification does not provide support for the quantization interval to be partitioned. The portions cited by the Applicant’s representative are not directed to a partitioning operation. Examiner suggested amending the claim language with details that are particularly described in the Specification, perhaps some features from the portions of the Specification cited above.
Regarding Section 101, Applicant argues: “Similar to the claims found to be patent eligible in the "2019 Revised Patent Subject Matter Eligibility Guidance," published by the USPTO on January 4, 2019 ("2019 Revised 101 Guidance"), claims 1, 3, and 6-13 are not directed to an abstract idea, and are allowable under 7 Attorney Docket No.: 022176.0050 U.S. Patent Application No. 18/962,612 35 U.S.C. § 101. See Ex Parle Baba, Appeal 2019-000116 (PTA B Dec. 30, 2019). For at least the reasons established below, and because no federal circuit case has found such method of encoding/decoding a latent representation of an input image in a neural-network-based progressive image compression system to be patent ineligible, claims 1, 3, and 6-13 are not directed to an abstract idea, and are allowable under 35 U.S.C. § 101.”
Examiner notes that (a) the claims are not required to be reviewed by a Federal Circuit to be found ineligible under the present guidance, and (b) Applicant fails to cite “similar to the claims found to be patent eligible.” Materially, the present claims do not require encoding an image and do not recite a supported improvement to the operation of a neural network or a computer. See the precedential decision in Ex parte Desjardins, 2024-000567.
Examiner suggests including the requisite steps of image encoding and/or neural network optimization.
Applicant argues: “Here, the Office has failed to meet its initial burden of "presenting a prima facie case of unpatentability," as required by the MP EP. Thus, for at least the reasons established below, claims 1-6 are not directed to an abstract idea and are allowable under 35 U.S. C. § 101.”
Examiner notes that Examiner’s initial burden has been satisfied by the articulated and cited reasons noted in the reasons for rejection below. At this point Applicant may articulate arguments or amendments based on the reasons for rejection, but Applicant may not generally deny that the reasons for rejection were presented.
Applicant argues: “Claims 1, 3, and 6-13 Do Not Articulate Any "Abstract Idea," Under the Directives of the Revised 101 Guidance … Applicants respectfully submit that the present claims are not directed to a "mathematical calculations," or mathematical concepts, as indicated in the above-noted Guidance. Specifically, the claims are directed to a specific computer-implemented process in a neural-network-based progressive image compression system. It operates on latent representations (outputs of a trained neural encoder), not generic data or mathematical 9 Attorney Docket No.: 022176.0050 U.S. Patent Application No. 18/962,612 concepts in the abstract.”
Examiner notes that Applicant’s arguments are not directed to the claim language. The claims do recite mathematical operations such as quantization, subtraction, entropy-coding, averaging. The present claims are not limited to specific neural network operations or to steps specific to image compression. The fact that the claimed mathematical operations are expected to operate on data generated by a neural network does not limit the claimed operations themselves to using a neural network. See claim construction below.
Applicant argues: “The claimed features are not "quantization" or "entropy coding" in the abstract (like basic arithmetic coding or uniform quantization). The claims recite a hierarchical, learned, boundary-clipped, progressive refinement tailored for neural network latent spaces in variable rate/ progressive codecs.”
Examiner notes that all quantization operations are clipping operations that have boundaries, this does not change their mathematical nature. The fact that Applicant believes that the claimed operations are tailored for neural network latent spaces in variable rate/ progressive codecs does not limit the claims to performing such operations. Applicant should address these deficiencies.
Applicant argues: “Moreover, the claims improve progressive image coding - a single bitstream usable across quality/transmission environments - with measurable compression gains, reduced bit consumption, and better reconstruction. Accordingly, the claimed features are integrated into a practical application.”
Examiner notes that Applicant fails to cite support for this belief or to cite the specific claim language that performs supported image coding and its improvements.
Regarding section 103, Applicant argues that the newly amended claims are patentable over the prior art.
Examiner notes the updated reason for rejection below.
Examiner suggests explicitly claiming steps of image compression or neural network training that Applicant regards to direct the claimed operations to a practical application or improvement in the functioning of a computer.
Claim Construction
Note that, for purposes of compact prosecution, multiple reasons for rejection may be provided for a claim or a part of the claim. The rejection reasons are cumulative, and Applicant should review all the stated reasons as guides to improving the claim language and advancing the prosecution toward an allowance.
Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed by a method claim, or by claim language that does not limit an apparatus claim to a particular structure. However, examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are: (A) “adapted to” or “adapted for” clauses; (B) “wherein” clauses; and (C) “whereby” clauses. M.P.E.P. 2111.04. Other examples are where the claim passively indicates that a function is performed or a structure is used without requiring that the function or structure is a limitation on the claim itself. The clause may be given some weight to the extent it provides "meaning and purpose” to the claimed invention but not when “it simply expresses the intended result” of the invention. In Hoffer v. Microsoft Corp., 405 F.3d 1326, 1329, 74 USPQ2d 1481, 1483 (Fed. Cir. 2005). Further, during prosecution, claim language that may or may not be limiting should be considered non-limiting under the standard of the broadest reasonable interpretation. See M.P.E.P. 904.01(a); In re Morris, 127 F.3d 1048, 44 USPQ2d 1023 (Fed. Cir. 1997).
"[E]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself. The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process." In re Thorpe, 777 F.2d 695, 698, 227 USPQ 964, 966 (Fed. Cir. 1985). See MPEP 2113(I).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3, 6-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 3, 6-13 recite: “such that the previous layer interval is partitioned into a plurality of sub-intervals that form the intervals of the current layer;” however Examiner did not find support in the Specification for the quantization interval to be partitioned. Further, it is not clear what quantization function Applicant intends to describe by this claim language. Examiner suggests clarifying this claim language to be consistent with the terms of art and embodiments in the Specification.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, 6-13 are rejected as being directed toward patent ineligible subject matter under 35 U.S.C. 101, under the “Revised Patent Subject Matter Eligibility Guidance” issued on January 7, 2019 (Federal Register, Vol. 84, No. 4, 50), and in view of “2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence” published on July 17, 2024 (89 FR 58128) and in view of Ex parte Desjardins, 2024-000567.
The claims are directed to statutory categories of methods, articles of manufacture (under Step 1).
Upon analysis of the present claims under the broadest reasonable interpretation (under Step 2A, prong one), the claims appear to recite a judicial exception, an abstract idea directed to mathematical relationships and mathematical calculations embodied in determining quantization intervals, quantizing, entropy-encoding, subtracting, determining an average.
According to Ex parte Desjardins, such mathematical operations recite an abstract idea. Cumulatively, according to the Federal Circuit, the claims include several categories of this abstract idea: information (latent representation, layer, quantization intervals, size of a quantization interval, boundary of a quantization interval, etc.), collecting information (determining quantization intervals for the current layer, ); outputting information (entropy-encoding a quantized latent representation), and/or analyzing information at a high degree of algorithmic generality (determining quantization intervals for the current layer, … a boundary of a quantization interval is determined based on …). These categories have been identified as abstract ideas by the Federal Circuit as summarized in Electric Power Group, LLC v. ALSTOM SA, 830 F. 3d 1350, 1354 (Fed. Cir. 2016):
Information as such is an intangible. See Microsoft Corp. v. AT & T Corp., 550 U.S. 437, 451 n.12, 127 S.Ct. 1746, 167 L.Ed.2d 737 (2007); Bayer AG v. Housey Pharm., Inc., 340 F.3d 1367, 1372 (Fed. Cir. 2003). Accordingly, we have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas. See, e.g., Internet Patents, 790 F.3d at 1349; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat'l Ass'n, 776 F.3d 1343, 1347 (Fed. Cir. 2014); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014); CyberSource Corp. 1354*1354 v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011). In a similar vein, we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category. See, e.g., TLI Commc'ns, 823 F.3d at 613; Digitech, 758 F.3d at 1351; SmartGene, Inc. v. Advanced Biological Labs., SA, 555 Fed.Appx. 950, 955 (Fed. Cir. 2014); Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011); SiRF Tech., Inc. v. Int'l Trade Comm'n, 601 F.3d 1319, 1333 (Fed. Cir. 2010); see also Mayo, 132 S.Ct. at 1301; Parker v. Flook, 437 U.S. 584, 589-90, 98 S.Ct. 2522, 57 L.Ed.2d 451 (1978); Gottschalk v. Benson, 409 U.S. 63, 67, 93 S.Ct. 253, 34 L.Ed.2d 273 (1972). And we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis. See, e.g., Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014).
Upon consideration of the record (under Step 2A, prong two), Examiner did not find that the additional elements of the present claims integrate the judicial exception into a practical application of that judicial exception “in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” The additional elements, when considered individually or in a claim as a whole, “A computer-implemented method … of encoding … of decoding … computer readable recording medium storing instructions … optimized via neural-network training … generated by a neural-network …”, do not seem to reflect a substantive improvement in the functioning of a computer or a neural network. Unlike the example in Ex parte Desjardins, the present specification does not describe an improvement to the operation of the computer or the neural network and the present claims merely apply the calculations using a general purpose computer or claim data products as products of a general neural network. Cumulatively, the present claims are too broad to recite an improvement to a particular patentable technology or technical field under the standards of the present judicial guidance; (The claimed methods are mathematical operations that apply to data in general without application to specific industry and without particular practical inputs and outputs in mind.); do not seem use a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim (The claims are mathematical methods separate from computation platforms or computer or automated application.); do not seem to effect a transformation or reduction of a particular article to a different state or thing (Transformation of data at the claimed level of generality is not a transformation of matter or a reduction to a practical application that transforms matter.).
This is further evidenced in that the additional elements, merely recite the words ‘‘determine … based on … generated by …” in connection with the judicial exception, or merely includes instructions to record an abstract idea on a computer readable medium, or merely suggests a computer or a neural network as a tool to perform an abstract idea; adds insignificant extra-solution activity to the judicial exception (i.e. obtaining, analyzing, transforming, or outputting information for use with the judicial exception as in CyperSource and Mayo); do no more than generally link the use of a judicial exception to a particular technological environment or field of use (i.e. data operations linked to a computer or other well-established activities in the art). Substantially similar subject matter has been found ineligible in RecogniCorp, LLC v. Nintendo Co., Ltd., 855 F. 3d 1322 (Fed. Cir. 2017), Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1340-41 (Fed. Cir. 2017).
Finally, the claimed elements, when considered individually and in combination (under step 2B), do not seem to provide an Inventive Concept that is “significantly more” than the ineligible subject matter. The claims perform data operations (quantization, entropy-coding) on general numerical information not limited to a particular application or field, with unspecified inputs or outputs used in a practical application, with no link to a practical automation (other than storing the data on a CRM in Claim 13).
The claims should be amended to include meaningful limitations within the technical field.
Examiner suggests clarifying the intended technical field of the claims (such as steps of encoding / decoding image pixels using an HEVC or VVC standard), clarifying the practical inputs to be received and practical outputs to be improved, clarifying the “based on” operations to describe the specific relationships and operations performed by the claims, and clarifying the intended contribution of the invention using terms of art rather than relying on Applicant’s own lexicography to convey the meaning.
Also note that the dependent Claims 3, 6-11 elaborate on the variant conditions of the mathematical operations but do not limit the claim to performing steps of a practical application or to reciting a supported improvement in the functioning of a computer.
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.
Claims 1, 3, 6-13 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210335019 to Li (“Li”) in view of US 20060245495 to Han (“Han”) in view of US 6148101 to Tanaka (“Tanaka”).
Examiner notes that the present Specification may be a literal translation rather than application of relevant terms of art in English. The claims tend to broadly base functionality on terms that are not clearly defined or limited in the claims. Examiner suggests clarifying the functions and the relationships between the claimed elements. The reasons for rejection below construe the claim language in the context of the Specification and the corresponding features as they are described in the art.
Regarding Claim 1: “A computer-implemented method of encoding a latent representation of an input image in a neural-network-based progressive image compression system using hierarchical quantization, the method comprising:
determining, for a current layer, a plurality of quantization intervals (“In the encoding method shown in FIG. 1, … the position coordinates of each 3D data point can be quantified based on the difference between the maximum and minimum values of the position coordinates on the three axes, and the quantization accuracy determined based on the input parameter,” which define the maximum, minimum, and the interval parameters of the quantization. Li, Paragraphs 34, 43.)
deriving temporary boundary values based on a learned quantization step size vector that is different for each layer (For example, “bitstream can describe the relevant information about the layered coding attribute, that is, the LOD. … the quantization step size of each layer of the LOD ( quantizationSteps) [i.e. previous layer, present layer], … and the size of the dead zone of each layer of the LOD (quantizedDeadZoneSize) (that is, the residual interval to quantized the residual to 0) can be selected” dictating boundary values of the next layer. Li, Paragraph 139. In this case, a boundary of the quantization interval of the current layer is, by design, clipped to the residual quantization interval based on the quantization interval of the previous layer. As explained more clearly in Roodaki, “there is a very strong interaction between the distortions and the bit rates of the layers, as finer or coarser quantization of one layer, leaves less or larger distortion to be coded by the corresponding enhancement layer.” Roodaki, Page 278, first paragraph.)
clipping each temporary boundary to a bottom boundary or a top boundary of a quantization interval of a previous layer when the temporary boundary exceeds that previous-layer boundary such that a size of the quantization intervals of the current layer equals a size of the quantization interval of the previous layer to which an unbiased latent representation belongs, and (Prior art provides an example of this: “bitstream can describe the relevant information about the layered coding attribute, that is, the LOD. … the quantization step size of each layer of the LOD ( quantizationSteps) [i.e. previous layer, present layer], … and the size of the dead zone of each layer of the LOD (quantizedDeadZoneSize) (that is, the residual interval to quantized [dictating the quantization interval of the present layer] the residual to 0) can be selected” Li, Paragraph 139. In this case, a boundary of the quantization interval of the current layer is, by design, clipped to the residual quantization interval based on the quantization interval of the previous layer. As explained more clearly in Roodaki, “there is a very strong interaction between the distortions and the bit rates of the layers, as finer or coarser quantization of one layer, leaves less or larger distortion to be coded by the corresponding enhancement layer.” Roodaki, Page 278, first paragraph.)
such that the previous layer interval is partitioned into a plurality of sub-intervals that form the intervals of the current layer; (For example, “If the minimum distance [sub-interval] is greater than a distance threshold (dist2) set in the current LOD layer, this point will be categorized to the current LOD layer.” Li, Paragraphs 136, 139.)
quantizing, component-wise, the unbiased latent representation which belongs to one of the determined quantization intervals of the for a current layer; and (Under the broadest reasonable interpretation consistent with the specification and ordinary skill in the art, (a) the latent representation can embody data coded in a particular layer of one or more layers, and (b) quantizing employs specified quantization intervals. See Specification, Page 3, first paragraph and Page 4, last 5 paragraphs.
Prior art teaches: “The header information about the attribute in the coded bitstream can describe the relevant information about the layered coding attribute, that is, the LOD. … the quantization step size of each layer of the LOD ( quantizationSteps ), and the size of the dead zone of each layer of the LOD ( quantizedDeadZoneSize) (that is, the residual interval to quantized the residual to 0)” Li, Paragraph 139. This documents the layers and the intervals on which the quantization was performed for each layer.)
entropy-encoding a quantized latent representation obtained by quantizing the unbiased latent representation, (“In the encoding method shown in FIG. 1, … quantization … In the process at 150, entropy encoding is performed on a code stream” Li, Paragraph 34.)
wherein the unbiased latent representation is derived by subtracting an average value of latent representations from a latent representation.” (Li teaches that the quantified value can be a sum of multiple reference points, but does not explicitly teach that a reference point can be an average of reference points. See Li, Paragraph 138.
Han teaches the above claim feature in the context of coding an image under image coding standards using quantization: “an encoder can obtain [delta] by subtracting … an average of the residuals between each of the base reference frame and the FGS layer reference frame and the original image 0,” See Han, Paragraphs 44, 9.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to supplement the teachings of Li to use an unbiased latent representation that is derived by subtracting an average value of latent representations from a latent representation, in the manner taught in Han, in order to optimize “video coding which reduces the amount of computations required for a multilayer-based Progressive Fine Granular Scalability” under the video coding standards. Han, Paragraphs 3, 9.
Finally, in reviewing the present application, there does not seem to be objective evidence that the claim limitations are particularly directed to: addressing a particular problem which was recognized but unsolved in the art, producing unexpected results at the level of the ordinary skill in the art, or any other objective indicators of non-obviousness.)
Li and Han do not teach: “[deriving temporary boundary values based on a learned quantization step size vector that is different for each layer] and optimized via neural-network training”
First note that the “learned quantization step size vector” is a product described by a process of making it “via neural-network training,” a process which is not required to be performed within the scope of the claim. Thus the claim is limited to using an optimized result but not to implementing a particular neural network or performing a particular training. See Claim construction above.
Cumulatively, Tanaka teaches that using a neural network to optimize quantization steps and distortion is well known in the art of image processing. See Tanaka, Column 2, lines 42-49.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to supplement the teachings of Li and Han to PERFORM_FUNCTION as taught in Tanaka, in order to optimize quantization in image processing. See Tanaka, Column 2, lines 42-49.
Finally, in reviewing the present application, there does not seem to be objective evidence that the claim limitations are particularly directed to: addressing a particular problem which was recognized but unsolved in the art, producing unexpected results at the level of the ordinary skill in the art, or any other objective indicators of non-obviousness.
“a latent representation generated by a neural-network encoder,” (First note that the “latent representation” is a product described by a process of making it “by a neural-network,” a process which is not required to be performed within the scope of the claim. Thus, the claim is limited to using an optimized result but not to implementing a particular neural network or performing a particular training. See Claim construction above.
Cumulatively, Tanaka teaches that using a neural network to optimize perform and optimize image processing is well known in the art of image processing. See Tanaka, Column 4 lines 38-45 and application to quantization processing in Column 2, lines 42-49. See statement of motivation above.)
Regarding Claim 3: “The method of Claim 2,
a bottom boundary of a first quantization interval of the current layer is set to be the same as a bottom boundary of the quantization interval of the previous layer, and (“attributes can be set for each layer of LOD,” for example “the size of the dead zone of each layer of the LOD ( quantizedDeadZoneSize) (that is, the residual interval to quantized the residual to 0) can be selected” which represents the minimum value of a quantization interval that can be encoded and appears can be set to be the same across the layers. Li, Paragraph 139.)
wherein a top boundary of a last quantization interval of the current layer is set to be the same as a top boundary of the quantization interval of the previous layer.” (Under the broadest reasonable interpretation consistent with the specification and ordinary skill in the art, “according to the present disclosure, a bottom boundary and a top boundary of a quantization interval for a first layer may be determined based on a quantization step size vector for the first boundary and the total number of layers.” See Specification, Page 3, lines 1-3 and Page 22, second paragraph. Further, “and a size of the quantization intervals of the current layer may be the same as a size of quantization intervals to which the latent representation within a previous layer belongs” See Specification, Page 2, fourth paragraph.
Prior art teaches this feature: “the number of LOD levels (levelOfDetailCount) [the total number of layers], … the quantization step size of each layer of the LOD ( quantizationSteps), [quantization step size] … the size of the dead zone of each layer of the LOD ( quantizedDeadZoneSize) [a quantization interval] … (that is, the residual interval to quantized the residual to 0) can be selected” which represents the minimum value of a quantization interval that can be encoded and appears can be set to be the same across the layers. Li, Paragraph 139. Li, Paragraph 139 further notes that these three “attributes can be set for each layer of LOD.” Thus, just like in the Specification, Li indicates that these attributes may be selected within the available range, and thus may be selected to be the same as the previous layer thus preserving the top and bottom boundaries of the quantization interval of the previous layer, or can select values different from the previous layer that otherwise preserve the quantized zone having the same top and bottom boundaries.)
Regarding Claim 6: “The method of Claim 1, wherein determining the quantization intervals of the current layer comprises: … determining, based on whether at least one of a size of a first sub-interval or a last sub-interval is less than a threshold, whether to adjust the sub-intervals to form the quantization intervals of the current layer.” (For example, “If the minimum distance [sub-interval] is greater than a distance threshold (dist2) set in the current LOD layer, this point will be categorized to the current LOD layer.” Li, Paragraphs 136, 139.)
Regarding Claim 7: “The method of Claim 6, wherein in response to at least one of the size of the first sub-interval or the last sub-interval being less than the threshold, at least one of the first sub-interval or the last sub-interval whose size is less than the threshold is merged with a neighboring sub-interval.” (For example, “the quantized position coordinates of at least two position coordinates may be the same, and these at least two position coordinates may correspond to at least two attribute values. At this time, before encoding the quantized position coordinates, the duplicate coordinates can be removed (e.g., the process at 125 as shown in FIG. 15 and FIG. 16). That is, the quantized position coordinates of the at least two position coordinates can be one,” which effectively merges quantized positions that are the same, i.e. having intervals less than the detection threshold at that quantization level. Li, Paragraphs 143, 136.)
Regarding Claim 8: “The method of Claim 7, when the merged subinterval is generated by merging the first sub-interval with a neighboring sub-interval, a bottom boundary of the merged sub-interval corresponds to a bottom boundary of the first sub-interval, and a top boundary of the merged sub-interval is formed by extending a top boundary of the neighboring sub-interval.” (As noted in Claims 3 and 7 above, “the duplicate coordinates can be removed (e.g., the process at 125 as shown in FIG. 15 and FIG. 16). That is, the quantized position coordinates of the at least two position coordinates can be one,” which effectively means that the intervals of quantization of the first quantized coordinate would be the same as the duplicate quantized coordinates and thus a bottom boundary of the first coordinate and a top boundary of a further coordinate would be preserved. Li, Paragraphs 143, 136.)
Regarding Claim 9: “The method of Claim 8, wherein the top boundary of the neighboring sub-interval is extended based on a median value of the quantization interval of the previous layer to which the unbiased latent representation belongs and an extended quantization step size vector.” (As noted in Claims 3 and 8 above, “the duplicate coordinates can be removed (e.g., the process at 125 as shown in FIG. 15 and FIG. 16). That is, the quantized position coordinates of the at least two position coordinates can be one,” which effectively means that the intervals of quantization of the first quantized coordinate would be the same as the duplicate quantized coordinates and thus preserve a value that is the same as the first value, the last value, or a median value of the interval deemed to be the same at that level of quantization. Li, Paragraphs 143, 136.)
Regarding Claim 10: “The method of Claim 1, wherein the unbiased latent representation is composed of a plurality of components, and wherein the quantization is performed only on a filtered component among the plurality of components of the unbiased latent representation.” (Han indicates that coded values may be first filtered, such as by performing interpolation. See Han, Paragraphs 52, 77 and statement of motivation in Clam 1.)
Claim 11: “The method of Claim 1, wherein: the entropy encoding is performed based on a quantized Probability Mass Function (PMF) approximate value for each of the quantization intervals of the current layer, … wherein the PMF-approximate value for a quantization interval represents a probability that the unbiased latent representation belongs to the quantization interval .” (Under the broadest reasonable interpretation consistent with the specification and ordinary skill in the art, the entropy coding can be performed on data in the current layer that is quantized using the same (or probably the same) parameters as the data in the previous layer. See Specification, Page 15. Prior art notes that these parameters can be set to be the same, and thus representing a probability of the coded interval belonging to the coded layer and the previous layer: “the number of LOD levels (levelOfDetailCount), the distance threshold for dividing each layer of the LOD (dist2), the quantization step size of each layer of the LOD ( quantizationSteps ), and the size of the dead zone of each layer of the LOD ( quantizedDeadZoneSize) (that is, the residual interval to quantized the residual to 0) can be selected,” thus the total number of layers is relevant to determining the number of levels which is relevant to determining the quantization steps and step sizes. See Li, Paragraph 139. See statement of motivation in Claim 1.)
Claim 12: “A latent representation decoding method based on a hierarchical quantization,” is rejected for reasons stated for Claim 1, and because prior art applies such features to decoding in the same manner as to encoding: “The 3D) data point set processing method includes encoding or decoding” Li, Paragraph 4.
Claim 13: “A non-transitory computer readable recording medium storing instructions that, when executed, cause a computer to carry out …” is rejected for reasons stated for Claim 1, and because prior art applies such features to decoding in the same manner as to encoding: “The computer storage medium stores program code, and the program code may be configured to perform the method for processing the 3D data point set described in foregoing embodiments of the present disclosure.” Li, Paragraph 207.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20080002767 Schwarz (“Schwarz”), as cited in the previous Office Actions.
Roodaki, Hoda, Hamid R. Rabiee, and Mohammad Ghanbari. "Rate-distortion optimization of scalable video codecs." Signal Processing: Image Communication 25.4 (2010): 276-286. This reference is relevant for explaining quantization processing in image compression in plain terms.
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/MIKHAIL ITSKOVICH/Primary Examiner, Art Unit 2483
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