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 .
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on April 9, 2026, June 7, 2026 and July 12, 2026. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the Examiner.
Applicant(s) Response to Official Action
The response filed on June 25, 2026 has been entered and made of record. Claims 1 – 20 are pending in the application.
Response to Arguments
Applicant’s arguments see pages 7 – 8 with respect to the rejection of Claims 1-20 under 35 U.S.C. 102(a)(1) as being anticipated by SCHWARZ et al., (US 2021/0136385 A1) have been fully considered and are not persuasive. Examiner’s response to the presented arguments follows below:
Applicant argues on page 8 that “Based on these claim mappings, for the above limitations of claim 1 to map properly to Schwarz, the reference would have to teach or suggest determining whether the chosen one of the non-zero transform coefficient levels is less than the preceding transform coefficient levels. Plainly, setting the preceding transform coefficient levels to a defined number is not the same as performing a comparison against the preceding transform coefficient levels. In view of at least these distinctions, Applicant submits that Schwarz cannot be properly interpreted as teaching or suggesting the above limitations of claim 1”. Examiner respectfully disagrees. The claim merely requires determining whether a second cost function value is less than a first cost function value. SCHWARZ clearly discloses Fig. 17, Par. [0106], the quantization indexes q.sub.k for a transform block should be determined in a way so that the following cost measure is minimized and Par. [0189] Compare the costs of the 4 final nodes (for the last coefficient in coding order) and chose the node with minimum cost. SCHWARZ further teaches in Par. [0188] that the problem of determining the vector/block of quantization indexes that minimizes the overall rate-distortion cost D+λ. R is equivalent to finding the path with minimum cost path through the trellis (from the left to the right in FIG. 17), Par. [0192] a. For each subset A, B, C, D, determine the quantization index that minimizes the distortion for the given original transform coefficient, Par. [0194] i. Calculate the rate-distortion costs for the two paths that connect a state for the preceding transform coefficient with the current state. The costs are given as the sum of the cost for the preceding state and the D.sub.k+λ.Math.R.sub.k, where D.sub.k and R.sub.k represent the distortion and rate for choosing the quantization index of the subset (A, B, C, D) that is associated with the considered connection, Par. [0195] ii. Assign the minimum of the calculated costs to the current node and prune the connection to the state of the previous transform coefficient that does not represent the minimum cost path.. Therefore, SCHWARZ clearly discloses the claim limitations.
Accordingly, the rejection is maintained.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 - 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SCHWARZ et al., (US 2021/0136385 A1) referred to as SCHWARZ hereinafter.
Regarding Claim 1, SCHWARZ discloses a computer-implemented method for encoding video data (Fig. 1, Par. [0025], a method for encoding a block of transform coefficients), the method comprising:
performing one or more scalar quantization operations on a plurality of transform coefficients of prediction residues to generate a plurality of first indices (Fig. 2a, Scalar quantization, Par. [0063], The resulting transform blocks are coded using transform coding: A 2d transform is applied (i.e. performing) to the block of residual samples, the resulting transform coefficients are quantized (i.e. quantization operation) using independent scalar quantization, and the resulting transform coefficient levels (quantization indexes) (i.e. first indices) are entropy coded);
performing one or more trellis coded quantization operations on a first vector that includes the plurality of transform coefficients to generate a second vector that includes a plurality of second indices (Fig. 5B, dependent scalar quantization, Par. [0150] The state transition in dependent quantization can also be represented using a trellis structure, as is illustrated in FIG. 12. The trellis shown in this figure corresponds to the state transitions specified in Table 1. For each state (i.e. second vector), there are two paths that connect the state for a current transform coefficient with two possible states for the next transform coefficient in reconstruction order. Given an initial state (the state 0) (i.e. first vector), the path through the trellis (i.e. trellis quantization operation) is uniquely specified by the transmitted quantization indexes (i.e. second indices));
determining a first cost function value based on a third vector that includes the plurality of first indices and a second cost function value based on the second vector that includes the plurality of second indices (Par. [0108], The location of the first non-zero transform coefficient levels (i.e. third vector) is determined by comparing the Lagrangian costs that are obtained by choosing one of the non-zero transform coefficient levels as first non-zero transform coefficient levels (i.e. second cost function value) in coding order (the preceding transform coefficient levels are set equal to zero (i.e. first cost function value));
determining whether the second cost function value is less than the first cost function value (Fig. 17, Par. [0103] Since the selection of transform coefficient levels determines both the distortion (or reconstruction/approximation quality) and the bit rate, the quantization algorithm used has a substantial impact on the rate-distortion performance of the produced bitstream. Par. [0106], the quantization indexes q.sub.k for a transform block should be determined in a way so that the following cost measure is minimized. Par. [0189] Compare the costs of the 4 final nodes (for the last coefficient in coding order) and chose the node with minimum cost (i.e. less than first));
setting a fourth vector that includes a plurality of quantization indices equal to either the second vector or the third vector based on whether the second cost function value is less than the first cost function value (Par. [0189] Follow the chosen path (specified by the final node) (i.e. fourth vector) is reverse order and collect the quantization indexes that are associated with the connections between the trellis nodes); and
performing one or more entropy coding operations on the fourth vector that includes the plurality of quantization indices to generate encoded video data (Fig. 1, entropy coding, Par. [0113], dependent scalar quantization is combined with a modified entropy coding, in which the probability model selection (or, alternatively, the codeword table selection) for a transform coefficient depends on the set of admissible reconstruction levels).
Regarding Claim 2, SCHWARZ discloses claim 1. SCHWARZ further discloses wherein first metadata also is generated when the one or more scalar quantization operations are performed on the plurality of transform coefficients of prediction residues (Par. [0056] The binarization of the transform coefficient levels and the distribution of the binary decisions (also referred to as bins) over the multiple passes is chosen in a way that the data coded (i.e. metadata) in the first pass uniquely determine the set of admissible reconstruction levels for the next scan position).
Regarding Claim 3, SCHWARZ discloses claim 2. SCHWARZ further discloses wherein the second cost function value is not less than the first cost function value (Par. [0114], the set of admissible values for the second transform coefficient t′.sub.1 does not depend on the chosen value for the first reconstructed transform coefficient t′.sub.0.), and further comprising determining quantization metadata based on the first metadata (Par. [0056] The binarization of the transform coefficient levels and the distribution of the binary decisions (also referred to as bins) over the multiple passes is chosen in a way that the data coded (i.e. metadata) in the first pass uniquely determine the set of admissible reconstruction levels for the next scan position), and storing at least a portion of the quantization metadata in memory (Par. [0064], The reconstructed prediction error signal for a block (obtained by reconstructing the transform coefficients given the quantization indexes and an inverse transform) is added to the corresponding prediction signal and the result is written to a buffer for the current picture. After all blocks of a picture are reconstructed, one or more in-loop filters can be applied (for example, a deblocking filter and a sample adaptive offset filter). The final reconstructed picture is then stored in a decoded picture buffer).
Regarding Claim 4, SCHWARZ discloses claim 3. SCHWARZ further discloses wherein the quantization metadata is further determined based on the second cost function value (Par. [0056] The binarization of the transform coefficient levels and the distribution of the binary decisions (also referred to as bins) over the multiple passes is chosen in a way that the data coded (i.e. metadata) in the first pass uniquely determine the set of admissible reconstruction levels for the next scan position. Par. [0103] Since the selection of transform coefficient levels determines both the distortion (or reconstruction/approximation quality) and the bit rate, the quantization algorithm used has a substantial impact on the rate-distortion performance of the produced bitstream).
Regarding Claim 5, SCHWARZ discloses claim 1. SCHWARZ further discloses wherein second metadata also is generated when the one or more trellis coded quantization operations are performed on the first vector that includes the plurality of transform coefficients (Par. [0056] The binarization of the transform coefficient levels and the distribution of the binary decisions (also referred to as bins) over the multiple passes is chosen in a way that the data coded (i.e. second metadata) in the first pass uniquely determine the set of admissible reconstruction levels for the next scan position) Fig. 5B, dependent scalar quantization, Par. [0150] The state transition in dependent quantization can also be represented using a trellis structure, as is illustrated in FIG. 12. The trellis shown in this figure corresponds to the state transitions specified in Table 1. For each state (i.e. second vector), there are two paths that connect the state for a current transform coefficient with two possible states for the next transform coefficient in reconstruction order. Given an initial state (the state 0) (i.e. first vector), the path through the trellis (i.e. trellis quantization operation) is uniquely specified by the transmitted quantization indexes).
Regarding Claim 6, SCHWARZ discloses claim 5. SCHWARZ further discloses wherein the second cost function value is less than the first cost function value (Par. [0051], an example trellis structure that can be exploited for determining sequences (or blocks) of quantization indexes that minimize a cost measures (such as an Lagrangian cost measure D+λ.Math.R). The trellis is shown for 8 transform coefficients (or quantization indexes). The first state (at the very left) represents an initial state, which is set equal to 0), and further comprising determining quantization metadata based on the second metadata (Par. [0056] The binarization of the transform coefficient levels and the distribution of the binary decisions (also referred to as bins) over the multiple passes is chosen in a way that the data coded (i.e. metadata) in the first pass uniquely determine the set of admissible reconstruction levels for the next scan position), and storing at least a portion of the quantization metadata in memory (Par. [0064], The reconstructed prediction error signal for a block (obtained by reconstructing the transform coefficients given the quantization indexes and an inverse transform) is added to the corresponding prediction signal and the result is written to a buffer for the current picture. After all blocks of a picture are reconstructed, one or more in-loop filters can be applied (for example, a deblocking filter and a sample adaptive offset filter). The final reconstructed picture is then stored in a decoded picture buffer).
Regarding Claim 7, SCHWARZ discloses claim 6. SCHWARZ further discloses wherein the quantization metadata is further determined based on the first cost function value (Par. [0056] The binarization of the transform coefficient levels and the distribution of the binary decisions (also referred to as bins) over the multiple passes is chosen in a way that the data coded (i.e. metadata) in the first pass uniquely determine the set of admissible reconstruction levels for the next scan position. Par. [0103] Since the selection of transform coefficient levels determines both the distortion (or reconstruction/approximation quality) and the bit rate, the quantization algorithm used has a substantial impact on the rate-distortion performance of the produced bitstream).
Regarding Claim 8, SCHWARZ discloses claim 1. SCHWARZ further discloses wherein the second cost function value is less than the first cost function value (Par. [0108], The location of the first non-zero transform coefficient levels (i.e. third vector) is determined by comparing the Lagrangian costs that are obtained by choosing one of the non-zero transform coefficient levels as first non-zero transform coefficient levels (i.e. second cost function value) in coding order (the preceding transform coefficient levels are set equal to zero (i.e. first cost function value)), and the fourth vector that includes the plurality of quantization indices is set equal to the second vector (Par. [0189] Follow the chosen path (specified by the final node) (i.e. fourth vector) is reverse order and collect the quantization indexes that are associated with the connections between the trellis nodes).
Regarding Claim 9, SCHWARZ discloses claim 1. SCHWARZ further discloses wherein the second cost function value is not less than the first cost function value (Par. [0114], the set of admissible values for the second transform coefficient t′.sub.1 does not depend on the chosen value for the first reconstructed transform coefficient t′.sub.0.), and the fourth vector that includes the plurality of quantization indices is set equal to the third vector (Par. [0189] Follow the chosen path (specified by the final node) (i.e. third vector) is reverse order and collect the quantization indexes that are associated with the connections between the trellis nodes).
Regarding Claim 10, SCHWARZ discloses claim 1. SCHWARZ further discloses wherein a cost function is used to compute the first cost function value and the second cost function value (Par. [0106] Quantization algorithms that aim to minimize a Lagrange function (i.e. cost function) D+λ.Math.R of distortion and rate are also referred to as rate-distortion optimized quantization (RDOQ). If we measure the distortion using the MSE or a weighted MSE, the quantization indexes q.sub.k for a transform block (i.e. first and second cost functions) should be determined in a way so that the following cost measure is minimized), wherein the cost function incorporates a tradeoff between an estimated number of bits needed by an entropy encoder (Fig. 1) to encode a sequence of transform coefficients (Par. [0187] a very good trade-off between distortion (reconstruction quality) and bit rate (i.e. estimated number of bits), the quantization indexes should be selected in a way that a Lagrangian cost measure is minimized) and a distortion corresponding to the sequence of transform coefficients (Par. [0051] FIG. 17 shows a schematic diagram of an example trellis structure that can be exploited for determining sequences (or blocks) of quantization indexes that minimize a cost measures (such as an Lagrangian cost measure D+λ.Math.R)).
Regarding Claim 11, it has limitations similar to those treated in the above rejection(s) of Claim 1, and is met by the references as discussed above. Claim 11 however also recites one or more non-transitory, computer-readable media storing instructions that, when executed by one or more processors (Par. [0032] a non-transitory digital storage medium may have a computer program stored thereon to perform the inventive methods, when said computer program is run by a computer).
Regarding Claim 12, SCHWARZ discloses claim 11. SCHWARZ further discloses setting a flag value to indicate that the second cost function value is less than the first cost function value (Par. [0108]-[0112] The flags coded_sub_block_flag for the 4×4 subblocks are determined by comparing the Lagrangian costs for the following two cases: (a) The transform coefficient levels selected in step 1 are used; (b) The syntax element coded_sub_block_flag is set equal to zero and, thus, all transform coefficient levels of the 4×4 subblock are set equal to zero. The coded_block_flag is determined by comparing the Lagrangian costs for the sequence of transform coefficient levels obtained after step 3 and the case that all transform coefficient levels inside the transform block are set equal to zero).
Regarding Claim 13, SCHWARZ discloses claim 12. SCHWARZ further discloses wherein both the fourth vector that includes the plurality of quantization indices and the flag value are transmitted to an entropy coding engine that performs the one or more entropy coding operations (Par. [0076] For simplifying the following entropy coding, the admissible reconstruction levels are represented by quantization indexes (also referred to as transform coefficient levels) (i.e. fourth vector) , which are transmitted as part of the bitstream) Par. [0090] A syntax element coded_block_flag is transmitted, which signals whether there are any non-zero transform coefficient levels in the transform block).
Regarding Claim 14, SCHWARZ discloses claim 11. SCHWARZ further discloses wherein each of the first cost function value and the second cost function value includes a coding cost and a distortion (Par. [0106] Quantization algorithms that aim to minimize a Lagrange function D+λ.Math.R of distortion and rate are also referred to as rate-distortion optimized quantization (RDOQ). If we measure the distortion using the MSE or a weighted MSE, the quantization indexes q.sub.k for a transform block should be determined in a way so that the following cost measure is minimized).
Regarding Claim 15, SCHWARZ discloses claim 11. SCHWARZ further discloses wherein the second cost function value is less than the first cost function value (Par. [0108], The location of the first non-zero transform coefficient levels (i.e. third vector) is determined by comparing the Lagrangian costs that are obtained by choosing one of the non-zero transform coefficient levels as first non-zero transform coefficient levels (i.e. second cost function value) in coding order (the preceding transform coefficient levels are set equal to zero (i.e. first cost function value)), and the fourth vector that includes the plurality of quantization indices is set equal to the second vector (Par. [0189] Follow the chosen path (specified by the final node) (i.e. fourth vector) is reverse order and collect the quantization indexes that are associated with the connections between the trellis nodes).
Regarding Claim 16, SCHWARZ discloses claim 11. SCHWARZ further discloses wherein the second cost function value is not less than the first cost function value (Par. [0114], the set of admissible values for the second transform coefficient t′.sub.1 does not depend on the chosen value for the first reconstructed transform coefficient t′.sub.0.), and the fourth vector that includes the plurality of quantization indices is set equal to the third vector (Par. [0189] Follow the chosen path (specified by the final node) (i.e. third vector) is reverse order and collect the quantization indexes that are associated with the connections between the trellis nodes).
Regarding Claim 17, SCHWARZ discloses claim 11. SCHWARZ further discloses wherein a cost function is used to compute the first cost function value and the second cost function value (Par. [0106] Quantization algorithms that aim to minimize a Lagrange function (i.e. cost function) D+λ.Math.R of distortion and rate are also referred to as rate-distortion optimized quantization (RDOQ). If we measure the distortion using the MSE or a weighted MSE, the quantization indexes q.sub.k for a transform block (i.e. first and second cost functions) should be determined in a way so that the following cost measure is minimized), wherein the cost function incorporates a tradeoff between an estimated number of bits needed by an entropy encoder (Fig. 1) to encode a sequence of transform coefficients (Par. [0187] a very good trade-off between distortion (reconstruction quality) and bit rate (i.e. estimated number of bits), the quantization indexes should be selected in a way that a Lagrangian cost measure is minimized) and a distortion corresponding to the sequence of transform coefficients (Par. [0051] FIG. 17 shows a schematic diagram of an example trellis structure that can be exploited for determining sequences (or blocks) of quantization indexes that minimize a cost measures (such as an Lagrangian cost measure D+λ.Math.R)).
Regarding Claim 18, SCHWARZ discloses claim 11. SCHWARZ further discloses wherein first metadata also is generated when the one or more scalar quantization operations are performed on the plurality of transform coefficients of prediction residues (Par. [0056] The binarization of the transform coefficient levels and the distribution of the binary decisions (also referred to as bins) over the multiple passes is chosen in a way that the data coded (i.e. metadata) in the first pass uniquely determine the set of admissible reconstruction levels for the next scan position), and further comprising determining quantization metadata based on the first metadata (Par. [0056] The binarization of the transform coefficient levels and the distribution of the binary decisions (also referred to as bins) over the multiple passes is chosen in a way that the data coded (i.e. metadata) in the first pass uniquely determine the set of admissible reconstruction levels for the next scan position).
Regarding Claim 19, SCHWARZ discloses claim 11. SCHWARZ further discloses wherein second metadata also is generated when the one or more trellis coded quantization operations are performed on the first vector that includes the plurality of transform coefficients (Par. [0056] The binarization of the transform coefficient levels and the distribution of the binary decisions (also referred to as bins) over the multiple passes is chosen in a way that the data coded (i.e. second metadata) in the first pass uniquely determine the set of admissible reconstruction levels for the next scan position) Fig. 5B, dependent scalar quantization, Par. [0150] The state transition in dependent quantization can also be represented using a trellis structure, as is illustrated in FIG. 12. The trellis shown in this figure corresponds to the state transitions specified in Table 1. For each state (i.e. second vector), there are two paths that connect the state for a current transform coefficient with two possible states for the next transform coefficient in reconstruction order. Given an initial state (the state 0) (i.e. first vector), the path through the trellis (i.e. trellis quantization operation) is uniquely specified by the transmitted quantization indexes), and further comprising determining quantization metadata based on the second metadata (Par. [0056] The binarization of the transform coefficient levels and the distribution of the binary decisions (also referred to as bins) over the multiple passes is chosen in a way that the data coded (i.e. metadata) in the first pass uniquely determine the set of admissible reconstruction levels for the next scan position).
Regarding Claim 20, it has limitations similar to those treated in the above rejection(s) of Claim 1, and is met by the references as discussed above. Claim 20 however also recites a computer system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps (Par. [0457] Some or all of the method steps may be executed by (or using) a hardware apparatus, like for example, a microprocessor (i.e. processor), a programmable computer or an electronic circuit. In some embodiments, one or more of the most important method steps may be executed by such an apparatus. Par. [0459] The invention can be implemented in hardware or in software. The implementation can be performed using a digital storage medium, for example a floppy disk, a DVD, a Blu-Ray, a CD, a ROM, a PROM, an EPROM, an EEPROM or a FLASH memory (i.e. memories), having electronically readable control signals stored thereon, which cooperate (or are capable of cooperating) with a programmable computer system (i.e. computer system) such that the respective method is performed. Therefore, the digital storage medium may be computer readable) Par. [0461] a computer program product with a program code (i.e. instructions), the program code being operative for performing one of the methods when the computer program product runs on a computer).
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to SUSAN E HODGES whose telephone number is (571)270-0498. The Examiner can normally be reached on M-F 8:00 am - 4:00 pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Brian T. Pendleton, can be reached on (571) 272-7527. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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, see 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 information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Susan E. Hodges/Primary Examiner, Art Unit 2425
Prosecution Insights