DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
2. Claims 1, 3-8, 10-15, and 17-20 are presented for examination. Claims 2, 9, and 16 are canceled.
Request for Continued Examination
3. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/04/2026 has been entered.
Response to Arguments
4. Applicant’s argument filed on 08/13/2026 with respect claims 1, 8, and 15 have been fully considered but they are not persuasive.
The applicant contends that the office action fails to teach or suggest the limitation of " generating feedback information based at least in part on the classification value, the first set of the plurality of error recovery operations, and the error recovery result." As recited in claims 1, 8, and 15.
The Examiner respectfully disagrees and asserts that Jeong et al. (US 2017/0249206 A1) in paragraphs [0038], [0041] and Fig. 5 teaches the such limitation. For example, the routine 500 begins at step 502, where a particular error condition is simulated in the storage device 200. For example, to simulate an STE/ATE error, the controller 220 may perform a number of writes of data to a test data track 204 on the recording surface of a disk 202 of the storage device while gradually encroaching from a single adjacent track. Similarly, a WW error may be simulated by writing data to the data track 204 while gradually reducing an adaptive flying height (“AFH”) value, while an ATI error may be simulated by gradually increasing the number of writes on an adjacent track. The bit-error rate (“BER”) is measured through the read channel over the repeated writes until the BER reaches a threshold value, such as −1.8. See paragraph [0038].
From step 506, the routine 500 proceeds to step 508, where the controller 220 decides whether more channel parameter sets are to be generated for additional error types. If additional error types are desired, then the routine 500 returns to step 502 where the process is repeated for the additional error types. According to some embodiments, the routine 500 may include simulating errors for each error type on a test data track 204 on a recording surface associated with each read/write head 206 in the storage device and/or in each storage zone on the recording surface(s). As a result, the channel parameters lookup table 232 may contain a separate channel parameter set 302 for each unique combination of error type, read/write head, and zone. Once all channel parameter sets 302 have been generated for all desired error types, read/write heads, and zones and stored in the channel parameters lookup table 232, the routine 500 ends. See paragraph [0041].
Also, the applicant contends that the office action fails to teach or suggest the limitation of "training, based on the feedback information, the classifier function." As recited in claims 1, 8, and 15.
The Examiner respectfully disagrees and asserts that Jeong et al. (US 2017/0249206 A1) in paragraphs [0002], [0039], [0043], [0048], and [0050] teaches the such limitation. For example, according to further embodiments, an adaptive read channel system comprises a read channel including at least one adaptive component, a memory storing a plurality of predetermined channel parameter sets, each predetermined channel parameter set associated with an error type, and a processor operably connected to the memory and the read channel. The processor is configured to determine whether a read error has occurred in the read channel, and if an error has occurred, perform at least one read-retry while training current channel parameters for the read channel to the error environment. After the at least one read-retry, the processors compares the current channel parameters with the plurality of predetermined channel parameter sets to determine a most probable error type, and an error recovery sequence for recovering from the read error is selected based on the determined most probable error type. See paragraph [0002].
After simulating the error condition on the recording surface, the routine 500 proceeds from step 502 to step 504, where the controller 220 performs a number of reads of the test data track 204 while the current channel parameters 414 are trained in the adaptive read channel module 228 for the simulated error condition. For example, the controller may make 100 reads of the test data track 204 while the adaptation algorithms 416 attempts to optimize the current channel parameters 414 for the error environment. In further embodiments, the controller may simulate the error environment on other data tracks 204 on the recording surface and then read the data using the optimized current channel parameters 414 in order to verify their effectiveness for the error environment before storing the current channel parameters to the channel parameters lookup table 232 associated with the error. See paragraph [0039].
The routine 600 begins at steps 602A-602R, where the data error recovery module 240 performs a number of read retries of the target data area (e.g., data track(s) 204 or sector(s)) where the UDE occurred while the current channel parameters 414 in the adaptive read channel module 228 are trained for the error environment. For example, the adaptive read channel module 228 may initially attempt R=5 read retries. If the read retries do not result in a successful read of the target data area, then the routine 600 proceeds from step 602R to step 604, where the data error recovery module 240 compares the adapted current channel parameters 414 to each of the channel parameter sets 302 associated with an error type in the channel parameters lookup table 232. For example, a minimum distance A(r), B(r), . . . N(r) between the parameter values 304 A.sub.0-A.sub.K, B.sub.0-B.sub.K, . . . N.sub.0-N.sub.K of each channel parameter set 302A-302N and the corresponding parameter value R.sub.0-R.sub.K in the current channel parameters 414 trained for the current error environment. See paragraph [0043].
The routine 800 begins at steps 802A-802R, where the data error recovery module 240 performs the limited number of read retries of the target data area to train the current channel parameters 414 for the error environment, as described above in regard to steps 602A-602R shown in FIG. 6. From step 802R, the routine 800 proceeds to step 804, where the data error recovery module 240 determines if the channel has been adapted, i.e. whether the current channel parameters 414 are sufficiently different from the default set of channel parameters 308 to account for the error condition in the channel. If the channel has been adapted, the routine 800 proceeds from step 804 to step 810, where the routine proceeds as described above in regard to steps 604-620 shown in FIG. 6. See paragraph [0048].
From step 806, the routine 800 proceeds to step 808, where the data error recovery module 240 performs a number of reads on the adjacent sectors in error located in step 806 to further train the current channel parameters 414 for the error environment. From step 808, the routine 800 proceeds to steps 810-826, where the data error recovery module 240 compares the adapted current channel parameters 414 to each of the channel parameter sets 302 associated with an error type to determine an error type and perform the appropriate recovery procedure accordingly, as described in regard to steps 604-620 above. From step 826, the routine 800 ends. See paragraph [0050]. For the Applicant’s convenience, see Fig. 5 is reproduced below.
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Further, the applicant contends that the office action fails to teach or suggest the limitation of "wherein the training comprises adjusting one or more weights associated with the classifier function based on the error recovery result." As recited in claims 1, 8, and 15.
Examiner notes that the applicant’s arguments regarding the above limitation with respect have been considered but are moot in view of the new ground(s) of rejection. In addition to, the Examiner maintained the references Hwang et al. (US 2020/0192759 A1) and Jeong et al. (US 2017/0249206 A1) since there is no further argument/s regarding to these references.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
5. Claims 1, 3-8, 10-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per claim 1:
At Step 1, is the claim directed to a processor, machine, manufacture or composition of matter? Yes, see MPEP 2106.03. The claim recites a system and, therefore, is a machine/manufacture, and thus directed to a statutory category. At step 2A Prong One, Does the claim recite an abstract idea law of nature or natural phenomenon? Yes, see MPEP 2106.04. The claim recites “identifying, using a classifier function, a classification value corresponding to a set of errors associated with the memory device; selecting, based on the classification value, a first set of a plurality of error recovery operations from a plurality of sets of error recovery operations; executing the first set of the plurality of error recovery operations to generate an error recovery result;---; and training, based on the feedback information, the classifier function, wherein the training comprises adjusting one or more weights associated with the classifier function based on the error recovery result,” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. At step 2A Prong Two, Does the claim recite additional elements that integrate the judicial exception into a practical application? NO, see MPEP 2106.04(d). The claim recites additional element/s of “generating feedback information based at least in part on the classification value, the first set of the plurality of error recovery operations, and the error recovery result” does not integrate the abstract idea into a practical application because is generic computer function of data mere data generating. These extra-solution activities do not provide practical application. At step 2B, Does the claim recite additional elements that amount to significantly more than judicial exception? NO, see MPEP 2106.05. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element/s “a memory device” and “a processing device” are generic components that are well understood, routine and conventional and do not result in the claim as a whole amounting to significantly more than the abstract idea. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. See the prior arts Shen et al. Hwang et al. (US 2020/0192759 A1) and Jeong et al. (US 2017/0249206 A1) teach well known elements. Therefore, the claim is not patent eligible.
As per claim 8:
At Step 1, is the claim directed to a processor, machine, manufacture or composition of matter? Yes, see MPEP 2106.03. The claim recites series steps, therefore, is a process, and thus directed to a statutory category. At step 2A Prong One, Does the claim recite an abstract idea law of nature or natural phenomenon? Yes, see MPEP 2106.04. The claim recites “identifying, using a classifier function, a classification value corresponding to a set of errors associated with a memory device; selecting, based on the classification value, a first set of a plurality of error recovery operations from a plurality of sets of error recovery operations; executing the first set of the plurality of error recovery operations to generate an error recovery result;--- and training, based on the feedback information, the classifier function, wherein the training comprises adjusting one or more weights associated with the classifier function based on the error recovery result,” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. At step 2A Prong Two, Does the claim recite additional elements that integrate the judicial exception into a practical application? NO, see MPEP 2106.04(d). The claim recites additional element/s of “generating, by a processing device, feedback information based at least in part on the classification value the first set of the plurality of error recovery operations and the error recovery result” does not integrate the abstract idea into a practical application because is generic computer function of data mere data generating. These extra-solution activities do not provide practical application. At step 2B, Does the claim recite additional elements that amount to significantly more than judicial exception? NO, see MPEP 2106.05. The claim does not recite any additional elements that amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
As per claim 15:
At Step 1, is the claim directed to a processor, machine, manufacture or composition of matter? Yes, see MPEP 2106.03. The claim directed to a non-transitory computer-readable storage medium, therefore, is a machine/manufacture, and thus directed to a statutory category. At step 2A Prong One, Does the claim recite an abstract idea law of nature or natural phenomenon? Yes, see MPEP 2106.04. The claim recites “identifying, using a classifier function, a classification value corresponding to a set of errors associated with a memory device; selecting, based on the classification value, a first set of a plurality of error recovery operations from a plurality of sets of error recovery operations; executing the first set of the plurality of error recovery operations to generate an error recovery result;--- and training, based on the feedback information, the classifier function, wherein the training comprises adjusting one or more weights associated with the classifier function based on the error recovery result,” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. At step 2A Prong Two, Does the claim recite additional elements that integrate the judicial exception into a practical application? NO, see MPEP 2106.04(d). The claim recites additional element/s of “generating feedback information based at least in part on the classification value the first set of the plurality of error recovery operations, and the error recovery result” does not integrate the abstract idea into a practical application because is generic computer function of data mere data generating. These extra-solution activities do not provide practical application. At step 2B, Does the claim recite additional elements that amount to significantly more than judicial exception? NO, see MPEP 2106.05. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element/s “a non-transitory computer-readable storage medium” and “a processing device” are generic components that are well understood, routine and conventional and do not result in the claim as a whole amounting to significantly more than the abstract idea. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. See the prior arts Shen et al. Hwang et al. (US 2020/0192759 A1) and Jeong et al. (US 2017/0249206 A1) teach well known elements. Therefore, the claim is not patent eligible.
Dependent claims 3-7, 10-14, and 17-20 are extended elements of the abstract idea of the independent claims and the claims are abstract in nature falling withing Mental Processes. The dependent claims do not add any meaningful limits to the abstract idea to improve the technology or the computer component and fails to add significantly more than the abstracts idea. Therefore, the dependent claims are not patent eligible.
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.
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.
6. Claims 1, 3-8, 10-15, and 17-20 are rejected under 35 U.S.C. 103 (a) as being unpatentable over Hwang et al. (US 2020/0192759 A1) “hereafter as Hwang” in view of Jeong et al. (US 2017/0249206 A1) "herein after as Jeong" in further view of Lu et al. (US 2017/0364149 A1) “herein after as Lu.”
As per claims 1, 8, and 15:
Hwang substantially teaches or discloses a system comprising (see Fig. 1): a memory device (see Fig. 1, memory device 1100); and a processing device, operatively coupled with the memory device, to perform operations comprising (see paragraph [0145], herein the processor 710 may control overall operations of the memory controller 1200, and perform a logical operation. The processor 710 may communicate with the external host 2000 through the host interface 740, and communicate with the memory device 1100 through the memory interface 760): identifying, using a classifier function, a classification value corresponding to a set of errors associated with the memory device (see paragraph [0239], herein when the read error occurs, the controller may determine occurrence possibilities for the plurality of different type of defects, respectively, based on status information 100500 on the storage region (for example, block or page) where the error occurred at step 101000, see Fig. 27); selecting, based on the classification value, a first set of a plurality of error recovery operations from a plurality of sets of error recovery operations (see paragraph [0041], herein the controller may select a read retry set among the plurality of read retry sets included in the read retry table, according to the sorted defect order; and paragraph [0219], herein the read retry unit 72400 may select a read retry set among the plurality of read retry sets included in the read retry table 73000, according to the sorted defect order); executing the first set of the plurality of error recovery operations to generate an error recovery result (see paragraph [0219] perform a read retry operation on the any one storage region using the selected read retry set, and paragraph[0241]).
Hwan does not explicitly teach generating feedback information based at least in part on the classification value, the first set of the plurality of error recovery operations, and the error recovery result; and training, based on the feedback information, the classifier function.
However, Jeong in the same the field of endeavor teaches generating feedback information based at least in part on the classification value, the first set of the plurality of error recovery operations, and the error recovery result (see paragraph [0041], herein the routine 500 proceeds to step 508, where the controller 220 decides whether more channel parameter sets are to be generated for additional error types. If additional error types are desired, then the routine 500 returns to step 502 where the process is repeated for the additional error types); and training, based on the feedback information, the classifier function (see paragraph [0039], herein After simulating the error condition on the recording surface, the routine 500 proceeds from step 502 to step 504, where the controller 220 performs a number of reads of the test data track 204 while the current channel parameters 414 are trained in the adaptive read channel module 228 for the simulated error condition, and paragraph [0043]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the system of Hwan with the teachings of Jeong by generating feedback information based at least in part on the classification value, the first set of the plurality of error recovery operations, and the error recovery result; and training, based on the feedback information, the classifier function.
This modification would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, because one of ordinary skill in the art would have recognized the generating feedback information based at least in part on the classification value, the first set of the plurality of error recovery operations, and the error recovery result; and training, based on the feedback information, the classifier function would have improved its data detection performance (see paragraph [0014] of Jeong). Hwan-Jeong as combined teaches all subject matter of claims 1 8, and 15 expect wherein the training comprises adjusting one or more weights associated with the classifier function based on the error recovery result.
However, Lu in the same the field of endeavor teaches wherein the training comprises adjusting one or more weights associated with the classifier function based on the error recovery result (see paragraph [0089], herein As each set of M error-corrected points are provided for the given noise calibration zone, the machine learning element will adjust its internal parameters (e.g. weights associated with nodes within a neural network) to provide an output that best matches the desired/correct output).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the system of Hwan-Jeong as combined with the teachings of Lu by adjusting one or more weights associated with the classifier function based on the error recovery result.
This modification would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, because one of ordinary skill in the art would have recognized the adjusting one or more weights associated with the classifier function based on the error recovery result would have improved calibration process, local accuracy compensation, and local gaze smoothing (see paragraph [0001] of Lu).
As per claims 3, 10, and 17:
Jeong teaches that wherein the training of the classifier function comprises generating an updated classifier function (see paragraph [0031], herein determine an error type for the current error by correlating adaptive channel parameter values with channel parameter sets associated with various error types, and then optimize the error recovery sequence accordingly).
As per claims 4, 11, and 18:
Jeong teaches that the operations further comprising identifying, using the updated classifier function, an additional classification value corresponding to an additional set of errors associated with the memory device (see paragraph [0042, herein determining an error type in a data error recovery procedure of a storage device by correlating adaptive channel parameter values with channel parameter sets associated with various error types, according to some embodiments).
As per claims 5 and 12:
Jeong teaches that the operations further comprising selecting, based on the additional classification value, a second set of the plurality of error recovery operations from the plurality of sets of error recovery operations (see paragraph [0046], herein from step 606, the routine 600 proceeds to steps 608-618, where the data error recovery module 240 selects the most effective error recovery procedure sequence based on the determined error type, and Fig. 6 steps 608-618).
As per claims 6 and 13:
Jeong teaches that the operations further comprising executing the second set of the plurality of error recovery operations (see Fig. 6 steps 612 & 614).
As per claims 7, 14, and 20:
Jeong teaches that the operations further comprising: generating additional feedback information based at least in part on the additional classification value and the first set of the plurality of error recovery operations; and training, based on the additional feedback information, the updated classifier function (see paragraph [0041], herein From step 506, the routine 500 proceeds to step 508, where the controller 220 decides whether more channel parameter sets are to be generated for additional error types. If additional error types are desired, then the routine 500 returns to step 502 where the process is repeated for the additional error types. According to some embodiments, the routine 500 may include simulating errors for each error type on a test data track 204 on a recording surface associated with each read/write head 206 in the storage device and/or in each storage zone on the recording surface(s). As a result, the channel parameters lookup table 232 may contain a separate channel parameter set 302 for each unique combination of error type, read/write head, and zone, and Fig. 5, step 508).
As per claim 19:
Jeong teaches that selecting, based on the additional classification value, a second set of the plurality of error recovery operations from the plurality of sets of error recovery operations (see paragraph [0046], herein from step 606, the routine 600 proceeds to steps 608-618, where the data error recovery module 240 selects the most effective error recovery procedure sequence based on the determined error type, and Fig. 6 steps 608-618); and executing the second set of the plurality of error recovery operations (see Fig. 6 steps 612 & 614).
Examiner Notes
7. When amending the claims, applicants are respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Prior Art
8. The prior art of record, considered pertinent to the applicant’s disclosure, is listed in the attached PTO-892 form.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to OSMAN ALSHACK whose telephone number is (571)272-2069. The examiner can normally be reached on MON-FRI 8:30 AM-5:00 PM EST, also please fax interview request to (571) 273- 2069. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ALBERT DECADY can be reached on 5712723819. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OSMAN M ALSHACK/Examiner, Art Unit 2112
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