CTNF 18/977,022 CTNF 82222 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This is the initial Office Action based on the application filed 12/11/2024. Claims 1-20 are presented for examination and have been considered below. Claim Objections 07-29-01 AIA Claim s 6, 13 and 19 are objected to because of the following informalities: The phrase “can not” should be replaced with cannot to ensure proper grammatical form . Appropriate correction is required. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim (s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 9,189,333 B2 to Wu et al. ("Wu"), and further in view of Cui et al ., "Improving LDPC Decoding Performance for 3D TLC NAND Flash by LLR Optimization Scheme for Hard and Soft Decision," ACM Transactions on Design Automation of Electronic Systems, Vol. 27, No. 1, Article 5, September 2021 ("Cui1”) . Claim 1: Wu teaches a method comprising: reading, by a processing device, a codeword from a memory device ( e.g., col. 5, ll. 6-13 shows that "read data from a flash memory using a plurality of different read voltages"; col. 6, ll. 10-18; Fig. 7 step 702. See also col. 1, ll. 57-60 ); determining whether the codeword contains errors ( e.g., As per col. 3, ll. 5-14, "a page read sometimes fails... ECC decoding is unable to correct all erroneous bits"; col. 6, ll. 18-25; Fig. 8 block 804 ); and responsive to the determining that the codeword contains errors, obtaining a first decoding parameter ( e.g., Wu teaches generating soft input information (LLR values) used as decoding parameters when errors are detected . As per col. 1, ll. 54-63, "log likelihood ratio or similar ECC decoder soft input information... generated from decision patterns"; col. 4, ll. 36-44 conceptually shows that logic elements including LLR generating logic 220. Upon error detection, Wu performs a read retry using adjusted voltages to generate new LLRs, in col. 12, ll. 40-45; Fig. 8 blocks 808-812 ), wherein the first decoding parameter includes a first likelihood set ( e.g., col. 11, ll. 48-60 shows generating numerical values/LLRs from decision patterns; col. 12, ll. 10-18 shows numerical values provided to soft-decoding ECC logic as LLRs. See also col. 7, ll. 39-50 shows computing weighted sums of decision values to generate LLRs ); performing a decoding operation with the first decoding parameter ( e.g., Wu teaches providing generated LLR values to soft-decoding ECC logic to attempt decoding. Col. 4, ll. 35-44 shows that LLR generating logic provides numerical values to soft-decoding ECC logic; Fig. 7 step 708 shows "input numerical values to soft-decoding ECC logic"; col. 13, ll. 5-8 ); responsive to determining that the decoding operation did not correct the errors of the codeword ( e.g., Wu teaches read retry when decoding fails. Col. 12, ll. 1-15 shows that "if a read error occurs... read retry logic initiates another read"; Fig. 8 decision block 806 shows that if read error occurred, proceed to block 808; col. 3, ll. 5-14 shows that common method for responding to page read failure is "retry" or "read retry" ), obtaining a second decoding parameter, wherein the second decoding parameter includes a second likelihood set (e.g., Wu generates new LLR values after performing another read with a different reference voltage. As per col. 6, ll. 20-35, using multiple reference voltages to read same cell generates different decision patterns and corresponding LLRs; Fig. 8 steps 808-814, shows (change reference voltage, read again, compute numerical values from decision patterns. Wu explicitly shows different LLR sets for different numbers of reads. See Fig. 9 shows LLR sets for 2 reads {2,0,-2} vs. 3 reads {3,1,-1,-3} ); and performing the decoding operation with the second decoding parameter (e.g. Wu performs ECC decoding again after generating new LLR values. Fig. 8 step 814 shows that after generating new LLRs, the method proceeds to block 804 to perform soft decoding; col. 12, ll. 10-20 ). Wu generates LLR values via weighted sums (col. 7, ll. 39-50) but does not explicitly disclose transforming likelihood values using quantization/scaling parameters as a distinct "transformation set." However, Cui teaches transforming LLR values through quantization and scaling using parameters β (scalar) and γ (offset). See Section 4 ("floating-point LLRs are quantized by two steps... q(x) = |β × x / min(x) + γ|"); Eq. (5) (quantization transformation formula) and Page 10 (LLR quantization discussion). Cui further teaches that β amplifies small LLRs to preserve precision and γ ensures non-zero LLRs during decoding (Section 4, Fig. 6). These constitute a "transformation set." Furthermore, Wu recomputes LLR values but does not explicitly disclose transforming likelihood values using quantization/scaling parameters as a distinct "transformation set" that varies between decoding attempts. However, Cui teaches LLR transformations including quantization, amplification of small LLRs, and lookup tables that can be optimized differently for different decoding scenarios. See Page 1-2 introduction (LLR optimization schemes for hard and soft decision); Page 10 (LLR quantization); Section 6.3 (optimizing β for soft-decision decoding, different from hard-decision parameters); Section 5.2 (different numbers of reference voltages require different parameter optimization). Also Cui explicitly teaches that transformation parameters can and should be varied based on the decoding context. Therefore, a person having ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine Wu 's iterative read retry framework with Cui 's LLR optimization and quantization techniques to improve decoding performance. Both references address the identical technical problem: inaccurate LLRs degrading LDPC decoding performance in NAND flash memory ( Wu , col. 1, ll. 62-67; Cui , Section 1). Wu provides the framework for iterative reads and LLR generation upon error detection. And Cui provides the specific mechanism for optimizing those LLRs via quantization parameters that can be tailored to different decoding stages. Applying known optimization techniques (Cui) to a known iterative process (Wu) to achieve predictable improvements in error correction would have been obvious to a PHOSITA. The combination represents the integration of prior art elements according to known methods to yield predictable results, which is precisely the situation where obviousness is found under KSR International Co. v. Teleflex Inc., 550 U.S. 398 (2007). As per claims 8 and 15, the claimed limitations are obvious for the same reasons as claim 1. Claim 2: Wu and Cui teach t he method of claim 1, wherein the first likelihood set of the first decoding parameter is different from the second likelihood set of the second decoding parameter or the first transformation set of the first decoding parameter is different from the second transformation set of the second decoding parameter. For instance, Wu teaches different LLR sets for different read attempts. Fig. 9 shows different LLR sets for 2 reads (first attempt after error) versus 3 reads (second attempt after error). See also col. 6, ll. 20-35 (different reference voltages produce different decision patterns and corresponding LLRs). And Cui teaches different quantization parameters for different decoding scenarios. Section 6.3 discusses optimizing β for soft-decision decoding, which would differ from hard-decision parameters. Section 5.2 and Fig. 10 show that different numbers of reference voltages constitute different parameters. As per claims 9 and 16, the claimed limitations are obvious for the same reasons as claim 2. Claim 3: Wu and Cui teach t he method of claim 2, but fail to teach that the first transformation set and the second transformation set includes a scalar value and an offset value and corresponds to a range of high reliability error rate (HRER) values. However, Cu i explicitly teaches this limitation. Formulas (5) and (6) disclose transformation parameters β (beta) (scalar value) and γ (gamma) (offset value) used to convert floating-point LLRs to fixed-point LLRs. Section 4 states: "β and γ are two constants. In general, β is set to 1, and γ is to ensure that LLR ≠ 0 during the decoding process." Section 6.3 discusses optimizing β for different decoding scenarios. Figure 6 shows the impact of different β values on performance. Regarding "HRER values", Cui teaches that LLR optimization corresponds to reliability characteristics. Section 4 discusses preserving precision of small LLRs because the decoder is sensitive to them. Section 2.1 and Figure 2 show that different bits (MSB, CSB, LSB) have different reliability characteristics. A PHOSITA would understand that transformation parameters inherently correspond to reliability ranges. As per claim 10, the claimed limitations are obvious for the same reasons as claim 3. Claim 4: Wu and Cui teach t he method of claim 2, wherein each likelihood set includes a plurality of likelihood values and corresponds to a range of high reliability error rate (HRER) values. For instance, Wu teaches LLR sets comprising multiple values corresponding to different voltage regions (Figs. 3-6) and different reliability conditions. See col. 7-8 (decision patterns and corresponding LLR values for different voltage regions). And Cui teaches that different pages (MSB, CSB, LSB) have different reliability characteristics (Fig. 2) and that LLR values correspond to different reliability ranges. Section 2.1 discusses reliability variations among bit positions. As per claim 11, the claimed limitations are obvious for the same reasons as claim 4. As per claim 17, the claimed limitations are obvious for the same reasons as claims 2 and 4. Claim 5: Wu and Cui teach t he method of claim 1, further comprising: responsive to determining that the decoding operation did not correct the errors of the codeword, determining whether a subsequent decoding parameter can be obtained; responsive to determining that a subsequent decoding parameter can be obtained, obtaining a third decoding parameter, wherein the third decoding parameter includes a third likelihood set and a third transformation set; and performing the decoding operation with the third decoding parameter. For instance, Wu explicitly teaches this iterative process. Wu teaches reading data in an nth instance using an nth reference voltage, determining (n+1)th decision patterns, and generating (n+1)th plurality of numerical values (col. 12, ll. 45-65; Fig. 8 iterative loop). Wu states: "if it is determined a read error occurred in the nth instance, then the step of determining a plurality of decision patterns includes determining an (n+1)th decision pattern" (col. 12, ll. 55-60). See also col. 13, ll. 1-5 (continuing until either no error occurs or maximum reads performed). And Cui teaches that different numbers of reference voltages (3, 5, 7) yield different LLR sets and may require different quantization parameters (Section 5.2, Fig. 10). As per claims 12 and 18, the claimed limitations are obvious for the same reasons as claim 5. Claim 6: Wu and Cui teach t he method of claim 1, but fail comprising: responsive to determining that the decoding operation did not correct the errors of the codeword, determining whether a subsequent decoding parameter can be obtained; responsive to determining that a subsequent decoding parameter can not cannot be obtained, flagging the codeword as unreliable. However, Wu teaches read retry continues until either no error occurs or a maximum number of reads is performed (col. 13, ll. 1-5). Therefore, a PHOSITA would understand that when maximum retries are exhausted without success, the data would be flagged as unreliable. As per claims 13 and 19, the claimed limitations are obvious for the same reasons as claim 6. Claim 7: Wu and Cui teach t he method of claim 1, further comprising: responsive to determining that the decoding operation corrected the errors of the codeword, returning the codeword. For instance, Wu teaches that if no error occurs, the controller continues normal operation (col. 12, ll. 25-30; Fig. 8, if no read error occurred, continue to operate in conventional manner). As per claims 14 and 20, the claimed limitations are obvious for the same reasons as claim 7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to GUERRIER MERANT whose telephone number is (571)270-1066. The examiner can normally be reached Monday-Friday 8:00 Am - 5:00 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mark Featherstone can be reached at 571-270-3750. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /GUERRIER MERANT/Primary Examiner, Art Unit 2111 3/18/2026 Application/Control Number: 18/977,022 Page 2 Art Unit: 2111 Application/Control Number: 18/977,022 Page 3 Art Unit: 2111 Application/Control Number: 18/977,022 Page 4 Art Unit: 2111 Application/Control Number: 18/977,022 Page 5 Art Unit: 2111 Application/Control Number: 18/977,022 Page 6 Art Unit: 2111 Application/Control Number: 18/977,022 Page 7 Art Unit: 2111 Application/Control Number: 18/977,022 Page 8 Art Unit: 2111 Application/Control Number: 18/977,022 Page 9 Art Unit: 2111
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