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
The information disclosure statements (IDS) submitted on 02/21/2025 and 04/16/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The NPL references listed in the IDS dated 02/21/2025 are found in file wrapper of the parent application 18/646559.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-6 and 8-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 9, and 12-13 of copending Application No. 18/646559 (hereinafter “the ‘559 application”) in view of More (U.S. Patent Application Publication No. 2020/0272605, hereinafter “More”).
Regarding claim 1, the ‘559 application discloses a storage system monitoring method (Claim 1, Line 1), comprising:
obtaining a segment of storage system monitoring data (Claim 1, Line 2);
generating a compressed segment from the segment (Claim 1, Lines 3-4) and a reconstructed segment from the compressed segment (Claim 1, Lines 15-18);
receiving a user query from a user system (Claim 1, Line 11), the user query indicating a portion of the segment (Claim 1, Lines 12-13);
in response to the user query (Claim 1, Line 11), performing at least one of:
reconstructing and providing the portion using the compressed segment (Claim 1, Lines 15-18); or
providing the compressed segment for reconstruction of the portion (Claim 1, Lines 19-21).
The ‘559 application does not appear to disclose:
identifying locations in the segment based on a comparison of the segment and the reconstructed segment;
determining values for the identified locations;
wherein the identified locations and the determined values for the identified locations are used to reconstruct and provide the portion or are provided for reconstruction of the portion.
More discloses embodiments for the compression and decompression of data comprising:
identifying locations in the segment based on a comparison of the segment (“data block 402”) and the reconstructed segment (“decoding 412”) (§ 0081, Lines 1-13; Concurrently with generating the set of bits 410, a set of one or more actual errors in the decoding 412 may be determined based on performing a bitwise “XOR” operation between the decoding 412 and the data block 402. One or more locations of unpredicted error may then be determined based on performing a bitwise “XOR” operation between the set of one or more actual errors and the error prediction index 408. The result may be a data structure 414 that has the same size as the data block 402 and that indicates one or more bits set to indicate one or more locations of unpredicted errors);
determining values for the identified locations (§ 0081, Lines 14-16; The data structure 414 may be used to generate a data structure, such as a set of bits 416 that stores one or more offsets corresponding to unpredicted error);
wherein the identified locations and the determined values for the identified locations are used to reconstruct and provide the portion or are provided for reconstruction of the portion (§ 0108, Lines 1-7; To correct for unpredicted error, a bitwise “XOR” operation may be performed between the data structure 414 and the union 526. For example, the data structure 414 may be implemented as a bitmask storing the bit value “1” at offsets corresponding to unpredicted error, thereby enabling the bitwise “XOR” operation to correct unpredicted error based on flipping bit values).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the method of the ‘559 application by integrating More’s approach for achieving lossless data compression in order to correct both predicted and unpredicted errors in the compression process (More, § 0018, Lines 3-10).
Regarding claim 2, the ‘559 application in view of More further discloses wherein the locations are identified based on a difference between:
the values, first derivative, or second derivative of the segment and corresponding values, first derivative, or second derivative of the reconstructed segment (More, § 0081, Lines 1-13; Concurrently with generating the set of bits 410, a set of one or more actual errors in the decoding 412 may be determined based on performing a bitwise “XOR” operation between the decoding 412 and the data block 402. One or more locations of unpredicted error may then be determined based on performing a bitwise “XOR” operation between the set of one or more actual errors and the error prediction index 408. The result may be a data structure 414 that has the same size as the data block 402 and that indicates one or more bits set to indicate one or more locations of unpredicted errors).
Regarding claim 3, the ‘559 application in view of More further discloses wherein the locations are identified based on a position-dependent threshold (“error prediction index 408”) (More, § 0081, Lines 7-10; One or more locations of unpredicted error may then be determined based on performing a bitwise “XOR” operation between the set of one or more actual errors and the error prediction index 408).
Regarding claim 4, the ‘559 application in view of More further discloses wherein:
the values for the identified locations are the values of the segment at the locations, or
the values for the identified locations are based on the values of the segment at the locations and corresponding values of the reconstructed segment at the locations (More, § 0081, Lines 14-16; The data structure 414 may be used to generate a data structure, such as a set of bits 416 that stores one or more offsets corresponding to unpredicted error. The offsets are used to correct unpredicted errors).
Regarding claim 5, the ‘559 application in view of More further discloses wherein:
the compressed segment is generated using a lossy compression method (More, § 0072, Lines 3-5; The encoder layers of the autoencoder 403 may apply an appropriate compressor model to yield an encoding 404 that is a lossy compression of the data block 402).
Regarding claim 6, the ‘559 application in view of More further discloses wherein:
the compressed segment is generated using at least one of polynomial approximation, frequency domain transformation, or audio encoding (The ‘559 application, Claim 9).
Regarding claim 8, the ‘559 application in view of More further discloses wherein:
the identification of the locations occurs during the generation of the compressed segment (More, § 0018, Lines 3-6; The process may include leveraging machine learning techniques to yield (a) a lossy compression of the data and (b) a prediction as to which of the data (i.e., locations) will be lost through the lossy compression (hereinafter “predicted error”)).
Claim 9 is disclosed by the ‘559 application (Claim 12) in view of More similar to how claim 1 is disclosed by the ’559 application (Claim 1) in view of More.
Claim 10 is disclosed by the ‘559 application in view of More similar to how claim 2 is disclosed by the ‘559 application in view of More.
Claim 11 is disclosed by the ‘559 application in view of More similar to how claim 3 is disclosed by the ‘559 application in view of More.
Claim 12 is disclosed by the ‘559 application in view of More similar to how claim 4 is disclosed by the ‘559 application in view of More.
Claim 13 is disclosed by the ‘559 application in view of More similar to how claim 5 is disclosed by the ‘559 application in view of More.
Regarding claim 14, the ‘559 application in view of More further discloses wherein:
the compressed segment is generated using at least one of polynomial approximation, frequency domain transformation, or audio encoding (The ‘559 application, Claim 9).
Regarding claim 15, the ‘559 application in view of More further discloses wherein:
the storage system monitoring data includes a table metric channel, a message metric channel, a streaming metric channel, a compaction metric channel, a commit log metric channel, a storage metric channel, a hint metric channel, an index metric channel, a buffer pool metric channel, a client management metric channel, a batch metric channel, or a virtual machine metric channel (The ‘559 application, Claim 13).
Claim 16 is disclosed by the ‘559 application in view of More similar to how claim 8 is disclosed by the ‘559 application in view of More.
Claim 17 is disclosed by the ‘559 application (Claim 12, which comprises at least one non-transitory computer-readable medium containing instructions) in view of More similar to how claim 1 is disclosed by the ’559 application (Claim 1) in view of More.
Regarding claim 18, the ‘559 application in view of More further discloses wherein:
the compressed segment is generated using at least one of polynomial approximation, frequency domain transformation, or audio encoding (The ‘559 application, Claim 9).
Claim 19 is disclosed by the ‘559 application in view of More similar to how claim 15 is disclosed by the ‘559 application in view of More.
Claim 20 is disclosed by the ‘559 application in view of More similar to how claim 8 is disclosed by the ‘559 application in view of More.
Claim 7 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/646559 (hereinafter “the ‘559 application”) in view of More (U.S. Patent Application Publication No. 2020/0272605, hereinafter “More”); further in view of Prado et al. (U.S. Patent Application Publication No. 2021/0223982, hereinafter “Prado”).
Regarding claim 7, the ‘559 application in view of More discloses the method as recited in claim 1.
The ‘559 application in view of More does not appear to disclose wherein:
the storage system monitoring data includes CPU I/O wait time, CPU Guest Usage, CPU usage, System Status, number of connected clients, network usage, memory usage, disk usage, read latency, write latency, or operating system load.
Prado discloses:
the storage system monitoring data includes CPU I/O wait time, CPU Guest Usage, CPU usage, System Status, number of connected clients, network usage, memory usage, disk usage, read latency, write latency, or operating system load (§ 0006, Lines 1-5; The telemetry information can include one or more of: each storage device’s system configuration, each storage device’s input/output (I/O) workloads, and each storage device’s performance characteristics associated with the workload conditions).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to augment the telemetry data set of the ‘559 application and More with Prado’s telemetry information in order to facilitate prediction of storage device performance having different system configurations (Prado, § 0003, Lines 5-8).
This is a provisional nonstatutory double patenting rejection.
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.
Claim(s) 1-5, 8-13, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (U.S. Patent Application Publication No. 2020/0366315, hereinafter “Sun”) in view of More (U.S. Patent Application Publication No. 2020/0272605, hereinafter “More”).
Claims 1, 9, and 17:
Sun discloses a monitoring system (§ 0055, Lines 3-7; The management of one or more computer systems over one or more networks 2024 may require the collection of telemetry data from one or more computer systems and the transmission of telemetry data to a central computer), comprising:
at least one processor (§ 0089, Lines 6-7; Processor 1002); and
at least one computer-readable medium containing instruction that, when executed by the at least one processor (§ 0089, Lines 28-31; The executing application program 1042 may include instruction that cause the processor 1002 to control the sequence and timing of operation of the system memory 1004), cause the monitoring system to perform operations comprising:
obtaining a segment of storage system monitoring data (§ 0079, Lines 6-7; The working stage software may read the telemetry data set 2050);
generating a compressed segment from the segment (§ 0079, Lines 6-9; The working stage software may compress the telemetry data set 2050 to produce a compressed telemetry data set 2055) and a reconstructed segment from the compressed segment (§ 0085, Lines 5-13; The consumption stage software may open the compressed telemetry data set 2055, decompress the compressed data payload 16022 of each of the compressed data blocks contained within the compressed telemetry data set 2055, and write the decompressed data blocks to an output file which becomes a copy of the telemetry data set 2050);
receiving a user query from a user system, the user query indicating a portion of the segment (§ 0068; The transmission stage may be performed by centralized management software on a management console or management server that is in communication with the system management software product on the managed system and may result in the compressed telemetry data set 2055 being sent to the management console or management server);
in response to the user query (See citation above), performing at least one of:
reconstructing and providing the portion using the compressed segment (§ 0085, Lines 5-13; The consumption stage software may open the compressed telemetry data set 2055, decompress the compressed data payload 16022 of each of the compressed data blocks contained within the compressed telemetry data set 2055, and write the decompressed data blocks to an output file which becomes a copy of the telemetry data set 2050); or
providing the compressed segment for reconstruction of the portion (§ 0084, Lines 1-4; A goal of the transmission stage is to move the compressed telemetry data set 2055 from the managed computer system where it may have been created to a central computer where the telemetry data is needed) (See citation above. The compressed data set 2055 is provided to a central computer to be decompressed (i.e., reconstructed) by the consumption stage software).
Sun does not appear to disclose:
identifying locations in the segment based on a comparison of the segment and the reconstructed segment;
determining values for the identified locations;
wherein the identified locations and the determined values for the identified locations are used to reconstruct and provide the portion or are provided for reconstruction of the portion.
More discloses embodiments for the compression and decompression of data comprising:
identifying locations in the segment based on a comparison of the segment (“data block 402”) and the reconstructed segment (“decoding 412”) (§ 0081, Lines 1-13; Concurrently with generating the set of bits 410, a set of one or more actual errors in the decoding 412 may be determined based on performing a bitwise “XOR” operation between the decoding 412 and the data block 402. One or more locations of unpredicted error may then be determined based on performing a bitwise “XOR” operation between the set of one or more actual errors and the error prediction index 408. The result may be a data structure 414 that has the same size as the data block 402 and that indicates one or more bits set to indicate one or more locations of unpredicted errors);
determining values for the identified locations (§ 0081, Lines 14-16; The data structure 414 may be used to generate a data structure, such as a set of bits 416 that stores one or more offsets corresponding to unpredicted error);
wherein the identified locations and the determined values for the identified locations are used to reconstruct and provide the portion or are provided for reconstruction of the portion (§ 0108, Lines 1-7; To correct for unpredicted error, a bitwise “XOR” operation may be performed between the data structure 414 and the union 526. For example, the data structure 414 may be implemented as a bitmask storing the bit value “1” at offsets corresponding to unpredicted error, thereby enabling the bitwise “XOR” operation to correct unpredicted error based on flipping bit values).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Sun’s dynamic data compression method by integrating More’s approach for achieving lossless data compression in order to correct both predicted and unpredicted errors in the compression process (More, § 0018, Lines 3-10).
The method of claim 1 is implemented by the system of claim 9 and is therefore rejected with the same rationale.
Regarding the “non-transitory computer-readable medium” of claim 17, Sun discloses a server that comprises one or more processors and one or more non-transitory computer-readable storage media to store instructions executable by the one or more processors to perform the disclosed operations (Claim 1, Lines 2-5).
Claims 2 and 10:
Sun in view of More further discloses wherein the locations are identified based on a difference between:
the values, first derivative, or second derivative of the segment and corresponding values, first derivative, or second derivative of the reconstructed segment (More, § 0081, Lines 1-13; Concurrently with generating the set of bits 410, a set of one or more actual errors in the decoding 412 may be determined based on performing a bitwise “XOR” operation between the decoding 412 and the data block 402. One or more locations of unpredicted error may then be determined based on performing a bitwise “XOR” operation between the set of one or more actual errors and the error prediction index 408. The result may be a data structure 414 that has the same size as the data block 402 and that indicates one or more bits set to indicate one or more locations of unpredicted errors).
Claims 3 and 11:
Sun in view of More further discloses wherein the locations are identified based on a position-dependent threshold (“error prediction index 408”) (More, § 0081, Lines 7-10; One or more locations of unpredicted error may then be determined based on performing a bitwise “XOR” operation between the set of one or more actual errors and the error prediction index 408).
Claims 4 and 12:
Sun in view of More further discloses wherein:
the values for the identified locations are the values of the segment at the locations, or
the values for the identified locations are based on the values of the segment at the locations and corresponding values of the reconstructed segment at the locations (More, § 0081, Lines 14-16; The data structure 414 may be used to generate a data structure, such as a set of bits 416 that stores one or more offsets corresponding to unpredicted error. The offsets are used to correct unpredicted errors).
Claims 5 and 13:
Sun in view of More further discloses wherein:
the compressed segment is generated using a lossy compression method (Sun, § 0004, Lines 7-9; Lossy compression may be desirable if the imperfections introduced by compression are acceptable and result in an additional size reduction) (Sun, § 0060, Lines 1-6; An individual data block within the telemetry data set 2050 is compressed using an individual data compression technique that is optimally selected) (More, § 0072, Lines 3-5; The encoder layers of the autoencoder 403 may apply an appropriate compressor model to yield an encoding 404 that is a lossy compression of the data block 402).
Claims 8, 16, and 20:
Sun in view of More further discloses wherein:
the identification of the locations occurs during the generation of the compressed segment (More, § 0018, Lines 3-6; The process may include leveraging machine learning techniques to yield (a) a lossy compression of the data and (b) a prediction as to which of the data (i.e., locations) will be lost through the lossy compression (hereinafter “predicted error”)).
Claim(s) 6, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (U.S. Patent Application Publication No. 2020/0366315, hereinafter “Sun”) in view of More (U.S. Patent Application Publication No. 2020/0272605, hereinafter “More”); further in view of Nag et al. (U.S. Patent Application Publication No. 2020/0091930, hereinafter “Nag”).
Claims 6, 14, and 18:
Sun in view of More discloses the method as recited in claim 1, the system as recited in claim 9, and the medium as recited in claim 17.
Sun in view of More does not appear to disclose wherein:
the compressed segment is generated using at least one of polynomial approximation, frequency domain transformation, or audio encoding.
Nag discloses the compressed segment is generated using at least one of polynomial approximation, frequency domain transformation, or audio encoding (§ 0015, Lines 1-4 and 16-20; Compression algorithms for floating point (FP) data attempt to improve compression performance for compressing n-dimensional FP data grids by first decorrelating the data and then applying an encoding scheme. This spatial continuity may be exploited by using any of context-based predictors which maintain the context using a hash table, polynomial predictors which use polynomial approximation using adjacent points to the data point).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Sun and More’s compression technique by using a compression algorithm for floating point data in order to improve compression performance for compressing n-dimensional FP data grids (Nag, § 0015, Lines 2-3).
Claim(s) 7, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al. (U.S. Patent Application Publication No. 2020/0366315, hereinafter “Sun”) in view of More (U.S. Patent Application Publication No. 2020/0272605, hereinafter “More”); further in view of Prado et al. (U.S. Patent Application Publication No. 2021/0223982, hereinafter “Prado”).
Claim 7:
Sun in view of More discloses the method as recited in claim 1.
Sun in view of More does not appear to disclose wherein:
the storage system monitoring data includes CPU I/O wait time, CPU Guest Usage, CPU usage, System Status, number of connected clients, network usage, memory usage, disk usage, read latency, write latency, or operating system load.
Prado discloses:
the storage system monitoring data includes CPU I/O wait time, CPU Guest Usage, CPU usage, System Status, number of connected clients, network usage, memory usage, disk usage, read latency, write latency, or operating system load (§ 0006, Lines 1-5; The telemetry information can include one or more of: each storage device’s system configuration, each storage device’s input/output (I/O) workloads, and each storage device’s performance characteristics associated with the workload conditions).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to augment Sun and More’s telemetry data set with Prado’s telemetry information in order to facilitate prediction of storage device performance having different system configurations (Prado, § 0003, Lines 5-8).
Claims 15 and 19:
Sun in view of More discloses the system as recited in claim 9 and the medium as recited in claim 17.
Sun in view of More does not appear to disclose wherein:
the storage system monitoring data includes a table metric channel, a message metric channel, a streaming metric channel, a compaction metric channel, a commit log metric channel, a storage metric channel, a hint metric channel, an index metric channel, a buffer pool metric channel, a client management metric channel, a batch metric channel, or a virtual machine metric channel.
Prado discloses:
the storage system monitoring data includes a table metric channel, a message metric channel, a streaming metric channel, a compaction metric channel, a commit log metric channel, a storage metric channel (§ 0006, Lines 1-5; The telemetry information can include one or more of: each storage device’s system configuration, each storage device’s input/output (I/O) workloads, and each storage device’s performance characteristics associated with the workload conditions), a hint metric channel, an index metric channel, a buffer pool metric channel, a client management metric channel, a batch metric channel, or a virtual machine metric channel.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to augment Sun and More’s telemetry data set with Prado’s telemetry information in order to facilitate prediction of storage device performance having different system configurations (Prado, § 0003, Lines 5-8).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent Application Publication No. 2011/0314356 (Grube et al.) – A method for verifying the integrity of data stored in a dispersed storage memory.
U.S. Patent Application Publication No. 2019/0296963 (Bonnell) – Adaptive and efficient detection of anomalies within an environment by reconstructing metric data for a single application instance using tiles of metric values generated from metric data of other application instances.
U.S. Patent Application Publication No. 2020/0177443 (Asghar et al.) – Regenerative telemetry method for resource reduction where any data segments with errors are overlayed with error segments in order to reconstruct the original measurement data. This overlaying is only performed as needed by the user (i.e., if the reconstructed measurement data is not accurate enough without overlaying error segments) in order to reduce processing.
U.S. Patent No. 11138200 (Mantzouratos et al.) – Aggregating (compressing) time series data and upon receiving an aggregation search query, decompressing the matching compressed time series data, and returning the result in response to the search query.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAM T TRAN whose telephone number is (408)918-7553. The examiner can normally be reached Monday-Friday 7AM-3PM EST.
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/NAM T TRAN/Primary Examiner, Art Unit 2455