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 .
In response to Applicant’s claims filed on January 29, 2026, claims 1-20 are now pending for examination in the application.
Response to Arguments
The objection set forth in the 10/29/25 office action is hereby withdrawn.
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Upadhyay et al. (US Pub. No. 20220414103) and Kucherov et al. (US Pub. No. 20210286726) in further view of Krasner et al. (US Pub. No. 20200364516).
With respect to claim 1, Upadhyay et al. discloses a computer-implemented method, executed on a computing device, comprising:
tracing a variable set of deduplication metrics for one or more deduplication data sets (Paragraphs 227-228 discloses deduplication information (e.g., hashes, data blocks, deduplication block size, deduplication efficiency or other metrics); an estimated or historic usage or cost associated with different components (e.g., with secondary storage devices). Upadhyay et al. does not disclose applying a regression-based machine learning (ML) model to the variable set of deduplication metrics in order to generate one or more of a weighted deduplication score and one or more of a weighted compression score for each data set of the one or more deduplication data sets.
However, Kucherov et al. teaches applying a regression-based machine learning (ML) model to the variable set of deduplication metrics in order to generate one or more of a weighted deduplication score and one or more of a weighted compression score for each data set of the one or more deduplication data sets (Paragraph 9 discloses each of the plurality of page scores associated with one of the plurality of pages of data stored in the cache may be determined using a function that takes a plurality of inputs for the one page of data, wherein the plurality of inputs may include the neighbor score, the deduplication score, the compression score, and the access score);
selecting one or more of a compression algorithm and a deduplication parameter based on one or more of the weighted deduplication score and one or more of the weighted compression score (Paragraph 192 discloses the data storage system may perform data reduction services or operations including data deduplication and compression with respect to pages of data. In at least one embodiment, data may be stored in the cache in compressed or uncompressed form).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Upadhyay et al. with Kucherov et al. to perform in-line deduplication. This would have facilitated read and write operations. See Kucherov et al. Paragraph(s) 4-9.
Upadhyay et al. as modified by Kucherov et al. does not disclose using a flushing manager to perform in-line deduplication.
However, Krasner et al. discloses receiving front-end IO write data (Paragraph 74 discloses write data received at the data storage system from a host or other client may be initially written to cache memory);
using a flushing manager to perform in-line deduplication on one or more selections of front-end IO write data and to apply one or more of the selected compression algorithm and the selected deduplication parameter to produce in-line deduplicated data (Paragraph 44 discloses In embodiments, performed on thousands of random files, doing statistics based clustering, and other machine learning experiments and then compressing them, we have shown substantial size reductions);
writing, by the flushing manager, the in-line deduplicated data to one or more storage targets (Paragraph 13 discloses Deduplication can also be performed at the source or target, level. In post-process deduplication, identical data portions are determined after the data is stored to disk. In in-line deduplication, identical data portions are determined before the data, including the identical portion, is moved from memory to storage on disk).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Upadhyay et al. and Kucherov et al. with Krasner et al. to perform in-line deduplication. This would have facilitated read and write operations. See Krasner et al. Paragraph(s) 3.
The Upadhyay et al. reference as modified by Kucherov et al. and Krasner et al. teaches all the limitations of claim 1. With respect to claim 2, Upadhyay et al. teaches the computer-implemented method of claim 1, wherein the regression-based ML model is configured to generate one or more of the weighted deduplication score and the weighted compression score for each data set of the one or more deduplication data sets (Paragraph 202 discloses Network administrators may assign priority values or “weights” to certain data and/or applications corresponding to the relative importance).
The Upadhyay et al. reference as modified by Kucherov et al. and Krasner et al. teaches all the limitations of claim 1. With respect to claim 3, Upadhyay et al. teaches the computer-implemented method of claim 1, wherein tracing occurs one or more of at the end of an active flushing cycle, and at the end of a background deduplication cycle (Paragraphs 224-234 discloses Information management policies 148 can additionally specify or depend on historical or current criteria that may be used to determine which rules to apply to a particular data object, system component, or information management operation, such as: frequency with which primary data 112 or a secondary copy 116 of a data object or metadata has been or is predicted to be used, accessed, or modified; time-related factors (e.g., aging information such as time since the creation or modification of a data object); deduplication information (e.g., hashes, data blocks, deduplication block size, deduplication efficiency or other metrics); an estimated or historic usage or cost associated with different components (e.g., with secondary storage devices 108); the identity of users, applications 110, client computing devices 102 and/or other computing devices that created, accessed, modified, or otherwise utilized primary data 112 or secondary copies 116; a relative sensitivity (e.g., confidentiality, importance) of a data object, e.g., as determined by its content and/or metadata; the current or historical storage capacity of various storage devices; the current or historical network capacity of network pathways connecting various components within the storage operation cell; access control lists or other security information; and the content of a particular data object (e.g., its textual content) or of metadata associated with the data object).
The Upadhyay et al. reference as modified by Kucherov et al. and Krasner et al. teaches all the limitations of claim 3. With respect to claim 4, Upadhyay et al. teaches the computer-implemented method of claim 3, further comprising:
in response to generating one or more of the weighted deduplication score and the weighted compression score, persisting one or more of the generated weighted deduplication score and the generated weighted compression score in a metadata cache following at least one of the active flushing cycle or the background deduplication cycle (Paragraph 137 discloses edia agent 144 can act as a local cache of recently-copied data and/or metadata stored to secondary storage device(s) 108, thus improving restore capabilities and performance for the cached data).
The Upadhyay et al. reference as modified by Kucherov et al. and Krasner et al. teaches all the limitations of claim 1. With respect to claim 5, Krasner et al. teaches the computer-implemented method of claim 1, wherein the regression-based ML model is configured to integrate with the tracing process by recording one or more of a deduplication hit ratio, a compression yield ratio, and an overwrite rate (Paragraph 69 discloses type of communication connection used may vary with certain system parameters and requirements, such as those related to bandwidth and throughput required in accordance with a rate of I/O requests as may be issued by the host computer systems). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Upadhyay et al. reference and the Krasner et al. reference is applicable to dependent claim 5.
The Upadhyay et al. reference as modified by Kucherov et al. and Krasner et al. teaches all the limitations of claim 1. With respect to claim 6, Upadhyay et al. teaches the computer-implemented method of claim 1, wherein the variable set of deduplication metrics are related to one or more extents of back-end IO write data flushed from front-end IO write data (Paragraph 80 discloses rimary data 112 can include files, directories, file system volumes, data blocks, extents, or any other hierarchies or organizations of data objects. As used herein, a “data object” can refer to (i) any file that is currently addressable by a file system or that was previously addressable by the file system (e.g., an archive file), and/or to (ii) a subset of such a file (e.g., a data block, an extent, etc.)).
The Upadhyay et al. reference as modified by Kucherov et al. and Krasner et al. teaches all the limitations of claim 1. With respect to claim 7, Krasner et al. teaches the computer-implemented method of claim 1, wherein the variable set of deduplication metrics include one or more of a deduplication ratio and a compression ratio (Paragraph 29 discloses locality sensitive hashing with hamming distance metric, or one or more of the following algorithms: k-means, k-medoids, mean shift, generalized method of moment (GMM), or density based spatial clustering of applications with noise (DBSCAN) considering various statistical attributes of the data). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Upadhyay et al. reference and the Lewis et al. reference is applicable to dependent claim 7.
The Upadhyay et al. reference as modified by Kucherov et al. and Krasner et al. teaches all the limitations of claim 1. With respect to claim 8, Upadhyay et al. teaches the computer-implemented method of claim 1, wherein the variable set of deduplication metrics includes one or more of:
timestamps, metadata addresses, a number of flushes executed within the predefined time period, a metadata retention rate, a data integrity validation rate, and a recovery time (Paragraph 86 discloses retention and pruning policies).
With respect to claim 9, Upadhyay et al. discloses a computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising:
tracing a variable set of deduplication metrics for one or more deduplication data sets (Paragraphs 227-228 discloses deduplication information (e.g., hashes, data blocks, deduplication block size, deduplication efficiency or other metrics); an estimated or historic usage or cost associated with different components (e.g., with secondary storage devices). Upadhyay et al. does not disclose applying a regression-based machine learning (ML) model to the variable set of deduplication metrics in order to generate one or more of a weighted deduplication score and one or more of a weighted compression score for each data set of the one or more deduplication data sets.
However, Kucherov et al. teaches applying a regression-based machine learning (ML) model to the variable set of deduplication metrics in order to generate one or more of a weighted deduplication score and one or more of a weighted compression score for each data set of the one or more deduplication data sets (Paragraph 9 discloses each of the plurality of page scores associated with one of the plurality of pages of data stored in the cache may be determined using a function that takes a plurality of inputs for the one page of data, wherein the plurality of inputs may include the neighbor score, the deduplication score, the compression score, and the access score);
selecting one or more of a compression algorithm and a deduplication parameter based on one or more of the weighted deduplication score and one or more of the weighted compression score (Paragraph 192 discloses the data storage system may perform data reduction services or operations including data deduplication and compression with respect to pages of data. In at least one embodiment, data may be stored in the cache in compressed or uncompressed form).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Upadhyay et al. with Kucherov et al. to perform in-line deduplication. This would have facilitated read and write operations. See Kucherov et al. Paragraph(s) 4-9.
Upadhyay et al. as modified by Kucherov et al. does not disclose using a flushing manager to perform in-line deduplication.
However, Krasner et al. discloses receiving front-end IO write data (Paragraph 74 discloses write data received at the data storage system from a host or other client may be initially written to cache memory);
using a flushing manager to perform in-line deduplication on one or more selections of front-end IO write data and to apply one or more of the selected compression algorithm and the selected deduplication parameter to produce in-line deduplicated data (Paragraph 44 discloses In embodiments, performed on thousands of random files, doing statistics based clustering, and other machine learning experiments and then compressing them, we have shown substantial size reductions);
writing, by the flushing manager, the in-line deduplicated data to one or more storage targets (Paragraph 13 discloses Deduplication can also be performed at the source or target, level. In post-process deduplication, identical data portions are determined after the data is stored to disk. In in-line deduplication, identical data portions are determined before the data, including the identical portion, is moved from memory to storage on disk).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Upadhyay et al. and Kucherov et al. with Krasner et al. to perform in-line deduplication. This would have facilitated read and write operations. See Krasner et al. Paragraph(s) 3.
With respect to claim 10, it is rejected on grounds corresponding to above rejected claim 2, because claim 10 is substantially equivalent to claim 2.
With respect to claim 11, it is rejected on grounds corresponding to above rejected claim 3, because claim 11 is substantially equivalent to claim 3.
With respect to claim 12, it is rejected on grounds corresponding to above rejected claim 4, because claim 12 is substantially equivalent to claim 4.
With respect to claim 13, it is rejected on grounds corresponding to above rejected claim 5, because claim 13 is substantially equivalent to claim 5.
With respect to claim 14, it is rejected on grounds corresponding to above rejected claim 6, because claim 14 is substantially equivalent to claim 6.
With respect to claim 15, it is rejected on grounds corresponding to above rejected claim 7, because claim 15 is substantially equivalent to claim 7.
With respect to claim 16, Upadhyay et al. discloses a computing system comprising:
a memory (See Fig. 4); and
a processor (See Fig. 4) configured to trace a variable set of deduplication metrics for one or more deduplication data sets (Paragraphs 227-228 discloses deduplication information (e.g., hashes, data blocks, deduplication block size, deduplication efficiency or other metrics); an estimated or historic usage or cost associated with different components (e.g., with secondary storage devices). Upadhyay et al. does not disclose applying a regression-based machine learning (ML) model to the variable set of deduplication metrics in order to generate one or more of a weighted deduplication score and one or more of a weighted compression score for each data set of the one or more deduplication data sets.
However, Kucherov et al. teaches apply a regression-based machine learning (ML) model to the variable set of deduplication metrics in order to generate one or more of a weighted deduplication score and one or more of a weighted compression score for each data set of the one or more deduplication data sets (Paragraph 9 discloses each of the plurality of page scores associated with one of the plurality of pages of data stored in the cache may be determined using a function that takes a plurality of inputs for the one page of data, wherein the plurality of inputs may include the neighbor score, the deduplication score, the compression score, and the access score);
select one or more of a compression algorithm and a deduplication parameter based on one or more of the weighted deduplication score and one or more of the weighted compression score (Paragraph 192 discloses the data storage system may perform data reduction services or operations including data deduplication and compression with respect to pages of data. In at least one embodiment, data may be stored in the cache in compressed or uncompressed form).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Upadhyay et al. with Kucherov et al. to perform in-line deduplication. This would have facilitated read and write operations. See Kucherov et al. Paragraph(s) 4-9.
Upadhyay et al. as modified by Kucherov et al. does not disclose using a flushing manager to perform in-line deduplication.
However, Krasner et al. discloses receiving front-end IO write data (Paragraph 74 discloses write data received at the data storage system from a host or other client may be initially written to cache memory);
using a flushing manager to perform in-line deduplication on one or more selections of front-end IO write data and to apply one or more of the selected compression algorithm and the selected deduplication parameter to produce in-line deduplicated data (Paragraph 44 discloses In embodiments, performed on thousands of random files, doing statistics based clustering, and other machine learning experiments and then compressing them, we have shown substantial size reductions);
writing, by the flushing manager, the in-line deduplicated data to one or more storage targets (Paragraph 13 discloses Deduplication can also be performed at the source or target, level. In post-process deduplication, identical data portions are determined after the data is stored to disk. In in-line deduplication, identical data portions are determined before the data, including the identical portion, is moved from memory to storage on disk).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify over Upadhyay et al. and Kucherov et al. with Krasner et al. to perform in-line deduplication. This would have facilitated read and write operations. See Krasner et al. Paragraph(s) 3.
With respect to claim 17, it is rejected on grounds corresponding to above rejected claim 2, because claim 17 is substantially equivalent to claim 2.
With respect to claim 18, it is rejected on grounds corresponding to above rejected claim 3, because claim 18 is substantially equivalent to claim 3.
With respect to claim 19, it is rejected on grounds corresponding to above rejected claim 4, because claim 19 is substantially equivalent to claim 4.
With respect to claim 20, it is rejected on grounds corresponding to above rejected claim 5, because claim 20 is substantially equivalent to claim 5.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-PUB 20210286726 is directed to TECHNIQUES FOR DETERMINING AND USING CACHING SCORES FOR CACHED DATA: [0004] cache management comprising: receiving a plurality of pages of data having a plurality of page scores, wherein each of the plurality of pages of data is associated with a corresponding one of the plurality of page scores, wherein the corresponding one of the plurality of page scores associated with said each page of data is determined in accordance with one or more criteria including one or more of a deduplication score, a compression score, and a neighbor score that uses a popularity metric based on deduplication related criteria of neighboring pages of data; and storing the plurality of pages of data in a cache in accordance with the plurality of page scores.
Conclusion
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/N.E.A/Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
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