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 .
This is the initial Office Action based on the application filed 05/09/2025. Claims 1-20 are presented for examination and have been considered below.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over HALPERIN (US 10,861,561 B2) in view of ALHUSSIEN (US 10,276,247 B2) and MOTWANI (US 2017/0093439 A1).Claim 1: A method comprising:
responsive to initiating an auto-read calibration operation comprising a plurality of read voltage level adjustments, performing a plurality of read operations on encoded host data stored in a memory device using the plurality of read voltage level adjustments to obtain current sensed data (e.g., Halperin teaches reading a page with a default threshold and, if hard decoding fails, performing two additional reads with a predetermined offset voltage . See col. 9, ll. 45-50; col. 10, ll. 1-5. This constitutes an auto-read calibration operation with multiple read voltage level adjustments);
obtaining, from a previous one of the plurality of read operations performed on the encoded host data using a previous read voltage level adjustment, a previous sensed data (e.g., Halperin's method inherently requires storing or using data from the initial default threshold read to compare with subsequent reads. The default read provides the "previous sensed data." See Fig. 1, steps 105-110).
Not explicitly taught by Halperin is:
a) identifying, based on the previous sensed data and the current sensed data, one or more flipped bits of the current sensed data;
b) assigning a likelihood value to the one or more flipped bits; and
c) responsive to completion of the auto-read calibration, performing, using the assigned likelihood value, soft-decision decoding on the encoded host data.
As per item (a), Halperin identifies that the initial hard decoding fails, which inherently indicates that some bits were flipped relative to the intended stored data. The Gaussian distribution approximation of successfully decoded bits relies on identifying which bits are in error. See col. 10, ll. 1-15). Besides, Alhussien directly teaches identifying bit errors by comparing the estimated or actual written data (genie data) to the data that was read. The calculation of cumulative distribution functions involves counting errors between reads. See col. 23, ll. 40-50; col. 24, ll. 5-15. And Motwani explicitly teaches identifying a "low confidence portion" of the ECC encoded data that has the "lowest probability of error free bits" by comparing multiple media reads. See ¶ [0030]; Fig. 4. Therefore, identifying flipped bits is a direct and necessary step in determining that hard decoding has failed. The comparison of previous and current sensed data to find differences is a straightforward technique that would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, as taught by Alhussien and Motwani.
As per item (b), Halperin assigns LLR values by approximating the empirical distribution of successfully decoded bits with a Gaussian distribution. See col. 10, ll. 15-20; col. 11, ll. 1-10. Besides, Alhussien explicitly teaches assigning LLR values based on a likelihood value assignment that changes sign at a read reference voltage. CDFs generated from bit error rates are used to calculate LLRs. See col. 17, ll. 30-45; col. 23, ll. 40-55. And Motwani assigns likelihood values by mapping a quantisation interval (voltage bin) to an LLR value. See ¶ [0030]-[0031]; Fig. 4. Therefore, the use of LLRs in soft-decision decoding is a well-established technique. A person of ordinary skill, before the effective filing date of the claimed invention, would have found it obvious to apply the LLR assignment techniques of Halperin, Alhussien, or Motwani to the flipped bits identified in the claim.
As per item (c), Halperin's method culminates in setting a new reading threshold and reading the page again, which is followed by soft decoding. See Fig. 1, steps 115-130; col. 10, ll. 25-30. Besides, Alhussien directly states that the LLRs and CDFs are used for "soft LDPC retry" or "soft decision decoding of LDPC." See col. 16, ll. 50-55; col. 27, ll. 40-45. And Motwani's entire disclosure is about soft decision decoding following the identification of a low confidence portion and flipping bits. See Abstract; ¶ [0031]. Therefore, before the effective filing date of the claimed invention, a person of ordinary skill would have found it obvious to use the improved LLR values to perform soft-decision decoding upon completion of the calibration process, as explicitly taught by all three references.
Claim 2: Halperin, Alhussien and Motwani teach the method of claim 1, wherein identifying, based on the previous sensed data and the current sensed data, the one or more flipped bits of the current sensed data comprises: performing an exclusive OR (XOR) operations on the previous sensed data and the current sensed data; and identifying, based on a result of the XOR operation, each bit of the current sensed data that were flipped as the one or more flipped bits of the current sensed data. For instance, Alhussien teaches comparing written data to read data using an XOR function to determine bit error rates. See col. 23, ll. 45-50; Fig. 11). The XOR operation between two pages is explicitly used to determine differences (errors). Thus, XOR is a fundamental logical operation for comparing two binary data sets. Alhussien explicitly teaches using XOR to identify differences between written and read data. It would be obvious to a person of ordinary skill to apply XOR to compare previous and current sensed data to identify flipped bits
Claim 3: Halperin, Alhussien and Motwani teach the method of claim 2, wherein each bit of the result having a bit value of 1 indicates that a corresponding bit of the current sensed data was flipped. For instance, Alhussien teaches comparing written data to read data using an XOR function to determine bit error rates. See col. 23, ll. 45-50; Fig. 11). The XOR operation between two pages is explicitly used to determine differences (errors). Thus, XOR is a fundamental logical operation for comparing two binary data sets. Alhussien explicitly teaches using XOR to identify differences between written and read data. It would be obvious to a person of ordinary skill to apply XOR to compare previous and current sensed data to identify flipped bits
Claim 4: Halperin, Alhussien and Motwani teach the method of claim 1, wherein assigning the likelihood value to the one or more flipped bits comprises: obtaining a likelihood value based on a position of a region between the current read voltage level and the previous read voltage level to an optimal read voltage level; identifying, based on the encoded host data, a likelihood assignment data structure associated with the encoded host data, wherein each entry of the likelihood assignment data structure corresponds to a position of a bit of the encoded host data; and for each bit of the flipped bits, storing, in an entry of the likelihood assignment data structure corresponding to a position associated with a respective bit of the flipped bits, the obtained likelihood value. For instance, Halperin teaches finding an intersection of Gaussian distributions and setting that as the optimal threshold. The position of a read relative to the optimal threshold determines the LLR value. See col. 11, ll. 15-30; col. 12, ll. 5-15. And Alhussien teaches using a lookup table (likelihood assignment data structure) to map read patterns to LLR values. See col. 17, ll. 20-35; Fig. 6. The LLR values change sign at a read reference voltage that is optimal for a given population. Furthermore, Motwani teaches assigning LLRs based on the quantisation interval in which a received signal is detected, with each interval having a specific LLR value. See ¶ [0030]-[0031]. Therefore, using a data structure (lookup table) to store LLR values based on voltage regions is well-known in the art. Alhussien explicitly teaches using a lookup table for LLR mapping. Halperin teaches finding the optimal threshold, and Alhussien teaches that LLR sign changes at the optimal threshold. A person of ordinary skill would find it obvious to combine these teachings.
Claim 5: Halperin, Alhussien and Motwani teach the method of claim 4, wherein the position of the region between the current read voltage level and the previous read voltage level to the optimal read voltage level indicates a magnitude of the likelihood value and a sign of the likelihood value (see the rejection of claim 4 above).
Claim 6: Halperin, Alhussien and Motwani teach the method of claim 1, wherein each read voltage adjustment of the plurality of read voltage adjustments of the auto-read calibration produces a read voltage level by applying a predetermined amount of adjustment to a read voltage level used to perform a previous read operation. For instance, Halperin teaches reading the page two more times with a predetermined offset voltage when the hard decoding fails. See col. 9, ll. 45-50; Fig. 1. And Alhussien teaches performing read retries with read reference voltages that are offset from the default or previous read voltage. See col. 14, ll. 45-55; Fig. 7. Therefore, applying a predetermined adjustment to the previous read voltage for subsequent reads is a fundamental aspect of read retry operations, as explicitly taught by both Halperin and Alhussien.
Claim 7: Halperin, Alhussien and Motwani teach the method of claim 1, wherein the completion of the auto-read calibration is determined by one of: performing a read operation using a read voltage level obtained by each read voltage adjustment of the plurality of read voltage adjustments of the auto-read calibration or obtaining a read voltage level between a pair of logical states. For instance, Halperin's process completes when the Gaussian distributions are approximated and their intersection is found (obtaining a read voltage level between a pair of logical states). See col. 10, ll. 15-20; col. 11, ll. 5-10. And Alhussien teaches that read retry operations continue until decoding succeeds or all entries in the read retry table (RRT) are exhausted (performing a read operation using each read voltage adjustment). See col. 15, ll. 30-45. Therefore, determining the completion of a read calibration process by either exhausting available voltage adjustments or finding an optimal voltage level between states is a well-known concept. Halperin teaches finding the intersection point, and Alhussien teaches exhausting all available entries.
Claims 8-20 are directed to system and computer-readable medium embodiments that correspond to the method claims of claims 1-7. Accordingly, claims 8-20 are rejected on same grounds as claims 1-7.
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
8/3/2026
Prosecution Insights