Detailed Action
The instant application having Application No. 18/809,413 has a total of 20 claims pending in the application; there are 3 independent claims and 17 dependent claims, all of which are ready for examination by the examiner. This Office action is in response to the claims filed 6/24/26. Claims 1-20 are pending.
REJECTIONS BASED ON PRIOR ART
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 9-11 and 17-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mesnier et al. (U.S. Patent Application Publication No. 2022/0188028) herein referred to as Mesnier et al.
Referring to claim 1, Mesnier et al. disclose as claimed, a method, comprising: receiving a write operation to write data to a storage device of a storage system (see para. 450, where a write command is received from a host processor); determining, using a trained model, an estimated write lifetime for the data, wherein the trained model is trained based on observed write lifetimes for data previously written to one or more storage systems (see para. 429-437, where a model is trained to predict the lifetime of data, which is based on observed lifetimes. See para. 436 which states “we can train a machine learning model to learn and predict the lifetime. For example, we may discover that all data classified as “metadata” is short-lived and data classified as “archive” is long-lived.”); and writing the data to a memory location of the storage device based on the estimated write lifetime for the data (see para. 453-454, where the data object is written to a certain location based on the estimated lifetime of the data).
Claims 9 and 17 recite similar limitations to claim 1 and would be rejected using the same rationale.
As to claim 2, Mesnier et al. also disclose the method of claim 1, wherein the trained model is trained based on training data that correlates the observed determining the estimated write lifetimes with one or more signals associated with write operations for the data previously written, the one or more signals comprising at least one of: a logical block address for a write operation, a time at which a write operation was received, a volume to which data is to be written, a size of data to be written, or an alignment of data to be written (see para. 436-437, where a machine learning model may observe write lifetimes and correlate them with classification information in order to predict data lifetimes. para. 439-442 and tables 1-3, showing different feature vectors which may be used to determine an estimated write lifetime, including an LBA and a timestamp).
Claims 10 and 18 recite similar limitations to claim 2 and would be rejected using the same rationale.
As to claim 3, Mesnier et al. also disclose the method of claim 1, wherein determining the estimated write lifetime for the data is based on one or more contextual attributes of the write operation (see para. 440-442 and tables 1-3, where a process name or application may be used to determine the estimated write lifetime).
Claims 11 and 19 recite similar limitations to claim 3 and would be rejected using the same rationale.
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 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 of this title, 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.
Claims 4-8, 12-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mesnier et al. and in view of Therene et al. (U.S. Patent Application Publication No. 2020/0133898) herein referred to as Therene et al.
As to claim 4, Mesnier et al. disclose the claimed invention except for the method of claim 1, further comprising training the trained model by a storage controller of the storage system.
However, Therene et al. disclose training the trained model by a storage controller of the storage system (see para. 89-90, where the training is executed on the same hardware used to run inference steps. See para. 123, where inferences are run via hardware of an AI engine and see fig. 20, where the AI engine is on the storage controller, which would therefore train the trained model).
Mesnier et al. and Therene et al. are analogous art because they are from the same field of endeavor of storage systems (see Mesnier et al., abstract and Therene et al., abstract, regarding storage systems).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mesnier et al. to comprise training the trained model by a storage controller of the storage system, as taught by Therene et al., in order to more accurately predict behavior of data or storage and therefore increase performance allow for a compact and efficient system where the data has less distance to travel as the controller may process data that is nearby.
Claims 12 and 20 recite similar limitations to claim 4 and would be rejected using the same rationale.
As to claim 5, Mesnier et al. disclose the claimed invention except for providing training data to a remotely disposed computing system; and receiving, from the remotely disposed computing system, the trained model, wherein the trained model is trained based on the training data.
However, Therene discloses providing training data to a remotely disposed computing system; and receiving, from the remotely disposed computing system, the trained model, wherein the trained model is trained based on the training data (see Therene, fig. 4, where AI models may be stored separately and see para. 46, where an AI engine may also be implemented remotely. Therefore as both model and engine are remote, they would receive training data remotely and provide the model remotely).
Mesnier et al. and Therene et al. are analogous art because they are from the same field of endeavor of storage systems (see Mesnier et al., abstract and Therene et al., abstract, regarding storage systems).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mesnier et al. to comprise providing training data to a remotely disposed computing system; and receiving, from the remotely disposed computing system, the trained model, wherein the trained model is trained based on the training data, as taught by Therene et al., in order to allow for training from any location and training at multiple distance locations. Being able to provide training data to remote locations also allows for faster and cheaper updates.
Claim 13 recites similar limitations to claim 5 and would be rejected using the same rationale.
As to claim 6, Mesnier et al. disclose the claimed invention except for periodically retraining the trained model.
However, Therene et al. disclose periodically retraining the trained model (see Therene et al., para. 89-90, where re-training or refining may take place on the trained model).
Mesnier et al. and Therene et al. are analogous art because they are from the same field of endeavor of storage systems (see Mesnier et al., abstract and Therene et al., abstract, regarding storage systems).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mesnier et al. to comprise periodically retraining the trained model, as taught by Therene et al., in order to allow for updates to fix errors or increase performance.
Claim 14 recites similar limitations to claim 6 and would be rejected using the same rationale.
As to claim 7, Mesnier et al. disclose the claimed invention except for determining an estimated write lifetime for live data included in a unit of data identified for garbage collection and rewriting the live data based on the estimated write lifetime for the live data.
However, Therene et al. disclose determining an estimated write lifetime for live data included in a unit of data identified for garbage collection (see para. 66, where a predictive garbage collection is implemented using AI engine, and see Pandurangan et al., para. 36, where garbage collection is performed. Also see Mesnier et al., para. 433, where the estimated lifetime of data may be used for garbage collection); and rewriting the live data based on the estimated write lifetime for the live data (see Therene et al., para. 68, where data migration is performed with garbage collection to free up space. Also see Mesnier et al., para. 433, where correct estimation of data improves garbage collection).
Mesnier et al. and Therene et al. are analogous art because they are from the same field of endeavor of storage systems (see Mesnier et al., abstract and Therene et al., abstract, regarding storage systems).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mesnier et al. to comprise determining an estimated write lifetime for live data included in a unit of data identified for garbage collection and rewriting the live data based on the estimated write lifetime for the live data, as taught by Therene et al., in order to allow for more efficient garbage collection or to make garbage collection easier (see Mesnier et al., para. 433).
Claim 15 recites similar limitations to claim 7 and would be rejected using the same rationale.
As to claim 8, Mesnier et al. disclose the claimed invention except for wherein the trained model is implemented in a storage controller of the storage system.
However, Therene et al. disclose wherein the trained model is implemented in a storage controller of the storage system (see fig. 20, showing an AI engine and AI models in a storage controller. see para. 89-90, where the training is executed on the same hardware used to run inference steps. See para. 123, where inferences are run via hardware of an AI engine and see fig. 20, where the AI engine is on the storage controller, which would therefore train the trained model).
Mesnier et al. and Therene et al. are analogous art because they are from the same field of endeavor of storage systems (see Mesnier et al., abstract and Therene et al., abstract, regarding storage systems).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mesnier et al. to comprise wherein the trained model is implemented in a storage controller of the storage system, as taught by Therene et al., in order to more accurately predict behavior of data or storage and therefore increase performance allow for a compact and efficient system where the data has less distance to travel as the controller may process data that is nearby.
Claim 16 recites similar limitations to claim 8 and would be rejected using the same rationale.
Response to Arguments
Applicant’s arguments, filed 6/24/26, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Mesnier et al.
CLOSING COMMENTS
Conclusion
a. STATUS OF CLAIMS IN THE APPLICATION
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i):
a(1) CLAIMS REJECTED IN THE APPLICATION
Per the instant office action, claims 1-20 stand rejected.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 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.
b. DIRECTION OF FUTURE CORRESPONDENCES
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/A.O/Examiner, Art Unit 2132
/HOSAIN T ALAM/Supervisory Patent Examiner, Art Unit 2132
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