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
Acknowledgment is made of applicant's Information Disclosure Statement (IDS) Form PTO-1449, filed 12/30/2025. The information disclosed therein was considered.
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, 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.
Claim(s) 1, 8, 14 & 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jung et al (US20190050159) in view of Li et al (US20220057962).
Regarding claim 1, Jung discloses an apparatus comprising: a read/write amplifier configured to retrieve read data from a memory array (FIG 2; [0042-0043] discloses 120 operating as write amplifier and retrieving read data from 160); a characteristic of a read data transmission is configured the read data signal based on the characteristic of the read data transmission path to provide a modified read data signal; and an output driver configured to transmit the modified read data signal path (FIG 2; [0037] ; path check circuit 170 checking a state of a signal transmission path and generating a result indicating whether or not a re-training operation is required based on a result of comparison).
However, Jung does not disclose a preconditioning circuit configured to receive a read data signal based on the read data, wherein a machine learning model of the preconditioning circuit trained based on a characteristic of a read data transmission path is configured to precondition the read data signal based on the characteristic of the read data transmission path to provide a modified read data signal; and an output driver configured to transmit the modified read data signal.
In the same field of endeavor, Li discloses a preconditioning circuit configured to receive a read data signal based on the read data, wherein a machine learning model of the preconditioning circuit trained based on a characteristic of a read data transmission path is configured to precondition the read data signal based on the characteristic of the read data transmission path to provide a modified read data signal; and an output driver configured to transmit the modified read data signal (FIG 2-4; [ 0042 & 0050] claim 1, discloses implementing machine learning based on I/O) information and predicting the I/O) information in the first time period e.g., I/O predictions results of the first future time period e.g., eight adjacent I/Os into one data path).
Jung and Li are analogous art because they are all directed to a data processing apparatus comprising a buffer/write driver, and one of ordinary skill in the art would have had a reasonable expectation of success by modify Jung to include Li because they are from the same field of endeavor.
Therefore, it would be obvious to include the teachings of Li in the teachings of Jung for the benefits improving data operations and implement wear balance of the apparatus [0004 Li].
Regarding claim 8, Jung in view of Li discloses wherein the characteristic of the read data transmission path includes a capacitance of signal lines of the read data transmission path (Jung FIG 2; [0031] discloses hardware block having capacitors).
Regarding claim 14, Jung discloses a method comprising: retrieving, from a memory array of a memory, read data(FIG 2; [0042-0043] discloses 120 operating as write amplifier and retrieving read data from 160 a characteristic of a read data transmission path, a read data signal corresponding to the read data based on the characteristic of the read data transmission path to provide a modified read data signal; and transmitting, via an output driver of the memory, the read data based on the modified read data signal(FIG 2; [0037] ; path check circuit 170 checking a state of a signal transmission path and generating a result indicating whether or not a re-training operation is required based on a result of comparison).
However, Jung does not disclose preconditioning, via a machine learning model of a preconditioning circuit of the memory trained
In the same field of endeavor, Li discloses preconditioning, via a machine learning model of a preconditioning circuit of the memory trained (FIG 2-4; [ 0042 & 0050] claim 1, discloses implementing machine learning based on I/O) information and predicting the I/O) information in the first time period e.g., I/O predictions results of the first future time period e.g., eight adjacent I/Os into one data path).
Jung and Li are analogous art because they are all directed to a data processing apparatus comprising a buffer/write driver, and one of ordinary skill in the art would have had a reasonable expectation of success by modify Jung to include Li because they are from the same field of endeavor.
Therefore, it would be obvious to include the teachings of Li in the teachings of Jung for the benefits improving data operations and implement wear balance of the apparatus [0004 Li].
Regarding claim 19, Jung in view of Li discloses wherein the characteristic of the read data transmission path includes a capacitance of signal lines of the read data transmission path (Jung FIG 2; [0031] discloses hardware block having capacitors).
Allowable Subject Matter
Claims 10-13 are allowed.
Claim 2-7, 9, 15-18 & 20 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 01/02/2026 have been fully considered but they are not persuasive. The applicant has stated that in page 7 “I/0 information, therefore, refers to metadata descriptive of an IO operation and, contrary to the contentions of the Office Action, not to "a characteristic of a read data transmission path", Li therefore fails to teach, disclose, or suggest: (a) a machine learning model trained based on a characteristic of a read data transmission path; and (b) a preconditioning circuit configured to precondition a read data signal based on the characteristic of the read data transmission path to provide a modified read data signal. As such, Li fails to teach, disclose, or suggest at least the above recitation of independent claim 1”.
In response to the arguments presented above by the applicant, the examiner respectfully disagreed. For example, the examiner clearly has distinguished in the rejection of claim 1 that, Li discloses a preconditioning circuit configured to receive a read data signal based on the read data, wherein a machine learning model of the preconditioning circuit trained based on a characteristic of a read data transmission path is configured to precondition the read data signal based on the characteristic of the read data transmission path to provide a modified read data signal; and an output driver configured to transmit the modified read data signal in FIG 2-4; [ 0042 & 0050] claim 1, discloses implementing machine learning based on I/O) information and predicting the I/O) information in the first time period e.g., I/O predictions results of the first future time period e.g., eight adjacent I/Os into one data path). For further explanation, clearly FIG 6 discloses implementing the machining learning based on the I/O information and to predict the results based on the data that was read and fed in before. For example, new memory state e.g., past memory state e.g., data read prior in order to determine the new modified output e.g., asserting the prediction results by using a first formula to value the accuracy of the predictions results. Unless, a clear distinguish is made e.g., how the modification of the read data signal is based e.g., coefficient values, the interpretation of claim 1 is that now when one thinks that the words “based on the characteristic of the read data transmission path” as cited in claim 1, is treated as “I/O path characteristic of the read data based on predictions results or any characteristic information”. Therefore, the rejection Jung et al in view of Li et al is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lu et al (US20220399060 FIG 1-2; [0005] discloses data buffer, Memory array, neural networks, wherein data buffer has an optimal area e.g., having width of data buffer is less than the width of the memory array).
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/MUNA A TECHANE/Primary Examiner, Art Unit 2827