DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/13/2026 has been entered.
Response to Arguments
On page 11 of the Remarks, Applicant unreasonably argues “Examiner’s analysis consists of observing that both claims mention “Haar coefficients” and “2x2 quads of pixels,” which…fails to consider the claims in their entirety as a whole….” Examiner finds such an argument unreasonable in view of the significant analysis provided on this record. See double patenting rejections, infra.
On pages 11–17 of the Remarks, Applicant contends the cited prior art is deficient for failing to teach or suggest the features of the claims. Examiner disagrees. First, Mavridis teaches utilizing RGB and RGBA formats for coding Haar coefficient information such that coding of such information would be compliant with system parameters. For example, Mavridis teaches DXTC and BC7 methods of coding data wherein those formats can support 8-bits per pixel. Second, the rejection principally relies on the teachings of Gamito to teach the features of the data structure for carrying such information. Gamito teaches the skilled artisan would be led to Applicant’s purported invention of coding data in a data structure compliant with system parameters by teaching floating point data coded in compliance with IEEE 754 wherein the floating point representation comprises three parameters: a sign, an exponent, and a mantissa. Indeed, Applicant correctly pointed out on page 7 of the previous set of Remarks that Gamito specifically teaches splitting sign, mantissa, and exponent fields into separate components of the datastream. Therefore, in combination, Mavridis and Gamito together teach or suggest to the skilled artisan considering the coding of Haar coefficient data in accordance with system parameters as recited in Applicant’s claims. In addition to the features taught by Mavridis and Gamito, the rejection now additionally relies on the further teachings of Karkkainen to teach or suggest the obviousness of having configurable and adaptable system parameters of conventional FPGAs doing the work of wavelet compression. See rejections under 35 U.S.C. 103, infra. Examiner also draws Applicant’s attention to the teachings of Sarin George Mathen, cited under the Conclusion Section of this Office Action, which also teaches or suggests Applicant’s adaptable system parameters for wavelet compression. Thus, in view of the teachings of the prior art, Examiner is unpersuaded that “in accordance with adaptable system parameters,” is a non-obvious feature under 35 U.S.C. 103. Accordingly, the rejection is sustained.
On page 7 of the Remarks, Applicant contends one must have a reason to turn to Gamito to solve an identified problem of Mavridis. Obviousness analysis does not require such a reason. Under an obviousness analysis, the prior art is viewed as a whole to determine what knowledge the skilled artisan possessed prior to Applicant’s disclosure. The cited prior art evidences that Applicant’s claimed features are obvious in view of the teachings of the prior art and level of skill in the art. Applicant insists that when the prior art teaches an n-bit exponent field or an m-bit mantissa field, the prior art somehow means “a singular bit,” and that, therefore, there is no evidence in the prior art that the skilled artisan was in possession of determining a “set of bits.” Such an argument is unreasonable on its face and unreasonably demotes the level of skill in this art. Applicant insists determining a number of bits in a set in accordance with parameters of a system is a non-obvious feature. Contrary to Applicant’s unreasonable argument, setting bit depth in accordance with a computing system is the most fundamental of concepts in digital processing arts. It is simply unreasonable for Applicant to argue in the same paragraph acknowledging the IEEE 754 standard that the prior art does not teach something so fundamental as bit length according to system parameters. According to IEEE 754 32-bit floating point notation, there is a one-bit sign bit, then an 8-bit exponent component (scientific notation 1.XXX × 2^±y; exponent bias is 127 for single precision, wherein positive is above 127, so 127±y is coded in the 8-bit part), then 23 fraction bits (mantissa, i.e. the bits following the 1.XXX in scientific notation). Therefore, packing values according to three components wherein the components comprise sign, exponent, and mantissa is obvious in view of these teachings. Regarding the packing of data to include a sum coefficient, Mavridis teaches the sum coefficient as part of the data of the Haar coefficients. Thus, Applicant’s argument against the references individually, rather than what their combination would teach or suggest to one of ordinary skill in the art, is unpersuasive of error. Accordingly, the rejection under 35 U.S.C. 103 is sustained.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1–20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–20 of U.S. Patent No. 12,073,593 in view of Mavridis et al., “Texture Compression using Wavelet Decomposition,” Computer Graphics Forum Vol. 31, Issue 7 (September 2012). Although the claims at issue are not identical, they are not patentably distinct from each other because they represent substantially overlapping subject matter regarding Haar Wavelet coefficients. Applicant contends the instant claims are different from reference patent ‘593 in that the reference patent recites determining a result of a weighted sum of differential coefficients and an average coefficient for a 2x2 quad of pixels. This result utilizes signs and exponents for the differential coefficients and weights. As claim 1 of the reference patent explains, these are Haar coefficients for a 2x2 quad of pixels. Likewise, pending claim 1 is also drawn to a 2x2 quad of Haar coefficients (see pending claim 8, elucidating the block of pixels is a 2x2 quad of pixels) wherein the differential coefficients are represented by signs and exponents. Also like the reference patent, in determining a sum coefficient, pending claim 1 determines an average coefficient (Applicant’s published paragraph [0120] explains, “The sum coefficient may also be referred to as an ‘average’ coefficient’.”). Also like the reference patent, because pending claim 1 encodes (and thus one would obviously decode) differential coefficients and a sum coefficient, the skilled artisan would understand the utility of that combination would be to “determin[e] the decoded value by determining the result of the weighted sum of the differential coefficients and the sum coefficient,” as explained in Applicant’s published paragraph [0084]. Applicant’s published paragraph [0120] explains a Level of Detail (LOD) can be achieved using such weights utilized in a weighted sum determination. In view of the teachings of Mavridis, e.g. Section 3.3 regarding decoding the Haar coefficients using a weighted sum and weights of 1 or -1 (Applicant’s weights are likewise 1 and -1; see explanation in paragraph [0120]), the pending claimed subject matter is obvious in view of the reference patent’s claims. Applicant also contends the instant claims are different from reference patent ‘593 in that the instant claims require packing in a compressed data stream (i) exponents; (ii) sign bits; and (iii) sum bits into first, second, and third portions, respectively. Likewise, the reference patent packs Haar coefficient data into a compressed data structure that includes a first portion made up of two sub-portions and a second portion. The first two sub-portions are allocated to (i) magnitudes of differential coefficients; and (ii) signs and exponents, while the second portion is allocated to an average (sum) coefficient. Therefore, both the reference patent and the pending claims utilize a compressed data structure having portions that include, in substantially identical order (although that would not matter), exponents, signs, and sum (average) bits. Therefore, the reference patent contains subject matter substantially identical to that of the pending claims when viewed in light of the teachings of Mavridis.
Claims 1–20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–20 of U.S. Patent No. 11,863,765 in view of Mavridis et al., “Texture Compression using Wavelet Decomposition,” Computer Graphics Forum Vol. 31, Issue 7 (September 2012). Although the claims at issue are not identical, they are not patentably distinct from each other because they represent substantially overlapping subject matter regarding Haar Wavelet coefficients. Applicant contends the instant claims are different from the reference patent ‘765 for reasons similar to those presented against reference patent ‘593. Examiner disagrees for substantially similar reasons to those addressed, supra. Particularly, both the reference claims and pending claims cover substantially overlapping subject matter drawn to a data structure storing Haar coefficient information for a 2x2 quad of pixels wherein the coefficient information comprises differential coefficients, an average coefficient, magnitudes, signs, exponents, and a second portion. Other claims in the claim sets overlap in terms of mantissa bits and RGBA channels in view of Mavridis. Therefore, the reference patent contains subject matter substantially identical to that of the pending claims when viewed in light of the teachings of Mavridis.
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 of this title, 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.
Claims 1–20 are rejected under 35 U.S.C. 103 as being unpatentable over Mavridis et al., “Texture Compression using Wavelet Decomposition,” Computer Graphics Forum Vol. 31, Issue 7 (September 2012) (herein “Mavridis”), Gamito et al., “Lossless coding of floating point data with JPEG 2000 Part 10,” In Applications of Digital Image Processing XXVII, Vol. 5558. SPIE, 276–287, November 2004 (herein “Gamito”), and Karkkainen (US 2017/0041021 A1).
Regarding claim 1, the combination of Mavridis, Gamito, and Karkkainen teaches or suggests a method of encoding a block of pixels into a compressed data structure in accordance with adaptable system parameters in a computer system, the method comprising: receiving the block of pixels (Mavridis, Title, Section 3, and Section 3.1.1: teaches a 2x2 block of texels being compressed using Haar wavelets); determining a set of Haar coefficients for the block of pixels, wherein the set of Haar coefficients comprises a plurality of differential coefficients and a sum coefficient (Examiner notes this is simply a recitation of the inherent properties of Haar wavelet coding, which includes a bias value representing a sum; Mavridis, Section 3.3: teaches a weighted sum of transform coefficients; Mavridis, Section 3.2: teaches Haar subband wavelet coefficients, said subbands represent Haar Transform differences from an average at a level of decomposition); determining a set of exponent bits representing exponents for the differential coefficients, wherein the number of exponent bits in the set of exponent bits is defined based on the adaptable system parameters (Gamito, Abstract and Section 2: explains the lossless compression of Haar transform coefficients can be accomplished using floating point data compliant with IEEE 754 wherein the floating point representation comprises three parameters: a sign, an exponent, and a mantissa; See also Gamito, Table 1; Examiner notes floating point precision is defined by a number of bits dedicated to coding the floating point representation; Examiner interprets the amended language drawn to “adaptable system parameters” consistent with Applicant’s published paragraph [0117], in which Applicant’s Specification explains the adaptable system parameters can be a compression ratio determined by the difference between the number of input bits and output bits; Neither Gamito nor Mavridis appears to explicitly teach the adaptability of the system parameters in the manner described by Karkkainen; Karkkainen, ¶ 0147: teaches that conventional encoders are capable of adaptively varying system parameters, such as compression ratio, between the input data and the encoded output data); determining a set of sign bits representing signs for one or more of the differential coefficients, wherein the number of sign bits in the set of sign bits is defined based on the adaptable system parameters (Gamito, Abstract and Section 2: explains the lossless compression of Haar transform coefficient can be accomplished using floating point data compliant with IEEE 754 wherein the floating point representation comprises three parameters: a sign, an exponent, and a mantissa; Examiner notes floating point precision is defined by a number of bits dedicated to coding the floating point representation; Examiner interprets the amended language drawn to “adaptable system parameters” consistent with Applicant’s published paragraph [0117], in which Applicant’s Specification explains the adaptable system parameters can be a compression ratio determined by the difference between the number of input bits and output bits; Neither Gamito nor Mavridis appears to explicitly teach the adaptability of the system parameters in the manner described by Karkkainen; Karkkainen, ¶ 0147: teaches that conventional encoders are capable of adaptively varying system parameters, such as compression ratio, between the input data and the encoded output data); determining a set of sum bits representing the sum coefficient, wherein the number of sum bits in the set of sum bits is defined based on the adaptable system parameters (Examiner notes this feature is interpreted, consistent with Applicant’s Specification, to be a weighted sum of coefficients; Mavridis, Section 3.3: teaches a weighted sum of transform coefficients; Examiner notes floating point precision is defined by a number of bits dedicated to coding the floating point representation; Examiner interprets the amended language drawn to “adaptable system parameters” consistent with Applicant’s published paragraph [0117], in which Applicant’s Specification explains the adaptable system parameters can be a compression ratio determined by the difference between the number of input bits and output bits; Neither Gamito nor Mavridis appears to explicitly teach the adaptability of the system parameters in the manner described by Karkkainen; Karkkainen, ¶ 0147: teaches that conventional encoders are capable of adaptively varying system parameters, such as compression ratio, between the input data and the encoded output data); packing: (i) the determined set of exponent bits into a first portion of the compressed data structure, (ii) the determined set of sign bits into a second portion of the compressed data structure, and (iii) the determined set of sum bits into a third portion of the compressed data structure; and storing the compressed data structure (Mavridis, Section 3.1 and Section 3.1.1: teaches packing the 2x2 blocks of texture information into a bit format wherein the 2x2 blocks are packed to effectively represent an 8x8 block in 128 bits; see also Mavridis, Section 6: explaining the wavelet coefficients are packed according to a data format and quantized).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Mavridis, with those of Gamito, because both references are drawn to the same field of endeavor such that one wishing to practice in the art of Haar wavelet transformation and compression of texture data would be led to their relevant teachings and because both Mavridis and Gamito demonstrate the desirability of using floating point representation of Haar coefficients rather than integers and because Gamito explains it would be desirable to format the floating point data according to the well-established IEEE 754 standard supported in JPEG 2000 Part 10. Therefore, the combination is a mere combination of prior art elements, according to known methods, to yield a predictable result. This rationale applies to all combinations of Mavridis and Gamito used in this Office Action unless otherwise noted.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Mavridis and Gamito, with those of Karkkainen, because all three references are drawn to the same field of endeavor such that one wishing to practice in the art of Haar wavelet transformation and compression of texture data would be led to their relevant teachings and because both Mavridis and Gamito demonstrate the desirability of using floating point representation of Haar coefficients rather than integers and because Karkkainen explains it would be desirable to support compression ratio adaptability in modern codecs. Therefore, the combination is a mere combination of prior art elements, according to known methods, to yield a predictable result. This rationale applies to all combinations of Mavridis, Gamito, and Karkkainen used in this Office Action unless otherwise noted.
Regarding claim 2, the combination of Mavridis, Gamito, and Karkkainen teaches or suggests the method of claim 1, wherein one or more of the adaptable system parameters define the number of exponent bits in the set of exponent bits (Gamito, Table 1: defines a standardized number of exponent and mantissa bits).
Regarding claim 3, the combination of Mavridis, Gamito, and Karkkainen teaches or suggests the method of claim 1, further comprising determining the number of sign bits in the set of sign bits and the number of sum bits in the set of sum bits in accordance with one or more of the adaptable system parameters and one or more of the Haar coefficients in the determined set of Haar coefficients for the block of pixels (Mavridis, Section 3.3: teaches a weighted sum of transform coefficients; Gamito, Abstract and Section 2: explains the lossless compression of Haar transform coefficient can be accomplished using floating point data compliant with IEEE 754 wherein the floating point representation comprises three parameters: a sign, an exponent, and a mantissa; See also Gamito, Table 1).
Regarding claim 4, the combination of Mavridis and Gamito teaches or suggests the method of claim 1, further comprising: determining a set of mantissa bits representing mantissas for one or more of the differential coefficients, wherein the number of mantissa bits in the set of mantissa bits is in accordance with the adaptable system parameters (Gamito, Abstract and Section 2: explains the lossless compression of Haar transform coefficient can be accomplished using floating point data compliant with IEEE 754 wherein the floating point representation comprises three parameters: a sign, an exponent, and a mantissa); and packing the determined set of mantissa bits into a fourth portion of the compressed data structure (Mavridis, Section 3.1 and Section 3.1.1: teaches packing the 2x2 blocks of texture information into a bit format wherein the 2x2 blocks are packed to effectively represent an 8x8 block in 128 bits; see also Mavridis, Section 6: explaining the wavelet coefficients are packed according to a data format and quantized).
Regarding claim 5, the combination of Mavridis and Gamito teaches or suggests the method of claim 4, further comprising determining the number of mantissa bits in the set of mantissa bits in accordance with one or more of the adaptable system parameters and one or more of the Haar coefficients in the determined set of Haar coefficients for the block of pixels (Gamito, Table 1: defines a standardized number of exponent and mantissa bits).
Regarding claim 6, the combination of Mavridis and Gamito teaches or suggests the method of claim 4, wherein one of the adaptable system parameters is a minimum exponent, ei,min, for a differential coefficient, δi, and wherein the method comprises: in response to determining that the differential coefficient, δi, is in a range 0<δi<2ei,min, rounding the value of δi to either 0 or 2ei,min prior to determining the set of mantissa bits (Mavridis, Section 3.1: teaches the differential coefficients (not those in the LL band) are close to zero and are represented as gray, i.e. quantized to zero or otherwise quantized to some minimum higher frequency using harsher quantization, i.e. less precision due to lower visual perceptibility).
Regarding claim 7, the combination of Mavridis and Gamito teaches or suggests the method of claim 4 wherein, for encoding the block of pixels, the compressed data structure includes only the determined set of exponent bits, the determined set of sign bits, the determined set of sum bits and the determined set of mantissa bits, such that the number of bits in the compressed data structure, N, equals a sum of the number of exponent bits in the set of exponent bits, the number of sign bits in the set of sign bits, the number of sum bits in the set of sum bits, and the number of mantissa bits in the set of mantissa bits (Gamito, Abstract and Section 2: explains the lossless compression of Haar transform coefficient can be accomplished using floating point data compliant with IEEE 754 wherein the floating point representation comprises three parameters: a sign, an exponent, and a mantissa; Mavridis, Section 3.3: teaches a weighted sum of transform coefficients).
Regarding claim 8, the combination of Mavridis and Gamito teaches or suggests the method of claim 1, wherein the block of pixels is a 2×2 quad of pixels (Mavridis, Title, Section 3, and Section 3.1.1: teaches a 2x2 block of texels being compressed using Haar wavelets).
Regarding claim 9, the combination of Mavridis and Gamito teaches or suggests the method of claim 1, wherein each pixel in the block of pixels is represented with an n-bit pixel value, wherein n is one of the adaptable system parameters; and wherein the compressed data structure has N bits, wherein N is one of the adaptable system parameters (Gamito, Table 1: defines a standardized number of exponent and mantissa bits according to IEEE 754; Mavridis teaches other system parameters have bit structures with larger numbers of bits).
Regarding claim 10, the combination of Mavridis and Gamito teaches or suggests the method of claim 1, wherein the adaptable system parameters comprise a plurality of configurable system parameters and a plurality of dependent system parameters which are determined based on one or more of the configurable system parameters, wherein the configurable system parameters are: the number of bits, n, representing the pixel value of each of the pixels in the block of pixels; the number of bits, N, in the compressed data structure; a minimum and a maximum number of sum bits in the set of sum bits, Dmin and Dmax; a minimum and a maximum exponent value for a first of the differential coefficients ex,min and ex,max; a minimum and a maximum exponent value for a second of the differential coefficients ey,min and ey,max; and a minimum and a maximum exponent value for a third of the differential coefficients exy,min and exy,max (Gamito, Table 1: defines a standardized number of exponent and mantissa bits according to IEEE 754; Mavridis teaches other system parameters have bit structures with larger numbers of bits).
Regarding claim 11, the combination of Mavridis and Gamito teaches or suggests the method of claim 10, wherein the dependent system parameters are: a number of exponent bits in the set of exponent bits, E, if exponent compaction is not applied; a number of exponent bits in the set of exponent bits, E′, if exponent compaction is applied; a difference, ΔE, between E and E′; a number of bits, F, of the compressed data structure that can be allocated after the exponent bits and the minimum number of sum bits have been allocated, if exponent compaction is not applied; and a number of bits, F′, of the compressed data structure that can be allocated after the exponent bits and the minimum number of sum bits have been allocated, if exponent compaction is applied (Gamito, Table 1: defines a standardized number of exponent and mantissa bits according to IEEE 754; Mavridis teaches other system parameters have bit structures with larger numbers of bits).
Regarding claim 12, the combination of Mavridis and Gamito teaches or suggests the method of claim 1, further comprising, subsequent to receiving the block of pixels: determining a plurality of block-specific parameters in dependence on one or more of the adaptable system parameters and one or more of the Haar coefficients in the determined set of Haar coefficients, wherein the block-specific parameters comprise: the number of sign bits in the set of sign bits, S; the number of sum bits in the set of sum bits, D; and a number of mantissa bits in a set of mantissa bits, M, to be packed into the compressed data structure for the block of pixels (Gamito, Section 3: teaches IEEE 754 includes sign, exponent, and mantissa data).
Regarding claim 13, the combination of Mavridis and Gamito teaches or suggests the method of claim 1, wherein the number of sign bits in the set of sign bits is the number of the differential coefficients which are non-zero, and wherein said determining a set of sign bits comprises, for each of the differential coefficients which is non-zero, setting a respective sign bit to a first value if the differential coefficient is positive and setting the respective sign bit to a second value if the differential coefficient is negative (Examiner notes this is how conventional sign coding works; Gamito, Section 3: teaches IEEE 754 includes sign, exponent, and mantissa data).
Regarding claim 14, the combination of Mavridis and Gamito teaches or suggests the method of claim 1, wherein the pixel values of the pixels of the block are in an unsigned format, and wherein said determining a set of sum bits comprises operating in an odd mode in which there is an odd number of steps between consecutive representable sum values from the smallest representable sum value to the largest representable sum value (Examiner notes that mapping from signed to unsigned often entails converting negative numbers into positive even numbers and positive numbers into positive odd numbers).
Regarding claim 15, the combination of Mavridis and Gamito teaches or suggests the method of claim 1, wherein the pixel values of the pixels of the block are in a signed format, and wherein said determining a set of sum bits comprises operating in an even mode in which there is an even number of steps between consecutive representable sum values from the smallest representable sum value to the largest representable sum value (Examiner notes that mapping from signed to unsigned often entails converting negative numbers into positive even numbers and positive numbers into positive odd numbers).
Regarding claim 16, the combination of Mavridis and Gamito teaches or suggests the method of claim 1, wherein said determining a set of exponent bits comprises, for each of the differential coefficients, δi: if the differential coefficient, δi, is zero, setting the exponent value for the differential coefficient to be equal to ei,min − 1; if the differential coefficient, δi, is non-zero, setting the exponent value for the differential coefficient to be equal to [Symbol font/0xEB]log2(| δi |)[Symbol font/0xFB]; and shifting the exponent values of the differential coefficients with a bias of ei,min − 1 such that the shifted exponent values start from zero, wherein the set of exponent bits represent the shifted exponent values for the differential coefficients; wherein ei,min is one of the adaptable system parameters and indicates a minimum exponent value for the differential coefficient δi (Examiner notes this feature is a feature of Haar Wavelet transforms).
Regarding claim 17, the combination of Mavridis and Gamito teaches or suggests the method of claim 1, wherein said determining a set of exponent bits representing exponents for the differential coefficients comprises: determining a plurality of exponent values for the respective plurality of differential coefficients; and compacting representations of the plurality of determined exponent values into the set of exponent bits, such that the number of exponent bits in the set of exponent bits is less than a sum of the numbers of bits needed to represent each of the individual determined exponent values (Gamito, Table 1: defines a standardized number of exponent and mantissa bits according to IEEE 754; Mavridis teaches other system parameters have bit structures with larger numbers of bits).
Regarding claim 18, the combination of Mavridis and Gamito teaches or suggests the method of claim 1, wherein each pixel in the block of pixels has a channel value in a plurality of channels, and wherein the method comprises performing channel decorrelation on the channel values of the pixels in the block of pixels prior to determining the set of Haar coefficients for the block of pixels (Mavridis, Section 3.5: teaches decorrelating the input texture color channels by converting RGB to a YUV (YCoCg) color space).
Claim 19 lists the same elements as claim 1, but in apparatus form rather than method form. Therefore, the rationale for the rejection of claim 1 applies to the instant claim.
Claim 20 lists the same elements as claim 1, but in CRM form rather than method form. Therefore, the rationale for the rejection of claim 1 applies to the instant claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Farghaly et al., “Floating-point discrete wavelet transform-based image compression on FPGA,” Int. J. Electron. Commun. (2020).
Trott et al., “Wavelets Applied to Lossless Compression and Progressive Transmission of Floating Point Data in 3-D Curvilinear Grids,” Proceedings of the 7th IEEE Visualization Conference, 1996.
Beer-Gingold (US 2014/0133552 A1) teaches using Haar wavelet compression of image data and storing exponents and mantissas (e.g. Abstract).
Mallat (US 8,559,499 B2) teaches frame buffer compression using wavelets.
Shih et al., “Memory Reduction by Haar Wavelet Transform for MPEG Decoder,” IEEE Transactions on Consumer Electronics, vol. 45, No. 3, Aug. 1, 1999, pp. 867–873.
Barnes (US 6,912,319 B1) teaches, for wavelet transformation, specific parameters adapted to provide desired compression ratios (col. 18., ll. 3–15).
Hou (US 2006/0228031 A1) teaches for wavelets used in JPEG2000, a fast adaptive lifting scheme for adaptive compression ratio and adaptive bit-depths (e.g. ¶¶ 0028 and 0089).
Colton, “Real-Time Wavelet Compression for High Speed Video,” accessed at https://scolton.blogspot.com/2019/10/real-time-wavelet-compression-for-high.html on May, 2, 2026, published October 27, 2019. This publication teaches several claimed features of the present invention including (1) truncating the sum turns it into an average and a sum/average component of the Haar wavelet; (2) a difference component; (3) a lifting scheme that allows for bit-depth adaptation; and (4) quantization that allows for compression ratio adaptation.
Sarin George Mathen, “Wavelet transform based Adaptive Image Compression on FPGA,” Masters Thesis, M.S. Computer Engineering, University of Kansas, June 23, 2000. The publication teaches benefits of Haar wavelet transforms on FPGAs is configurability and adaptable levels of compression (e.g. Slides 3, 4, 10, and 11).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael J Hess whose telephone number is (571)270-7933. The examiner can normally be reached on Mon - Fri 9:00am-5:30pm.
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 571-273-8300.
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/MICHAEL J HESS/Primary Examiner, Art Unit 2481
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