DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in response to communications filed 01/26/2026.
Claims 1-2, 7-8, and 17 have been amended.
Claims 1-20 are pending.
Claims 1-20 are rejected.
The Examiner notes the current action does not include prior art rejections over the current presentation of the claims. The cited relevant prior art references made of record below are considered as pertinent to the claims and disclosed details provided in the Specification.
The claims are subject to the rejections provided herein which must be addressed accordingly.
Miscellaneous
The Examiner notes, as discussed in the Interview Summary dated 01/16/2026, that the properly executed inventor’s oath or declaration has been filed for: Carlos ROLO, which therefore addresses the previous issue.
Response to Arguments
In Remarks filed on 01/26/2026, Applicant substantially argues:
On Pages 8-9, the amendments to claim 1, and similarly amended claims 8 and 17, overcome the prior art rejections of record including the teachings of Sun and Rocamora wherein the compression specifications taught therein do not teach or render obvious in combination the claimed limitations of the data value characteristic based compression of segments. Applicant’s arguments filed have been fully considered and they are found to be persuasive. The Examiner therefore withdraws the prior art rejections made in the Office action dated 12/12/2025. The Examiner notes the newly presented rejections made herein in response to Applicant’s amendments.
On Page 10, the Applicant has filed a terminal disclaimer to address the non-statutory double patenting rejections over claims 1, 5, 8-9, 13, and 15. Applicant’s arguments filed have been fully considered but they are not found to be persuasive. The Examiner notes that despite Applicant Remarks, no Terminal Disclaimer has been filed. Furthermore, the Examiner has attempted to contact the undersigned representative Mark Watson (Reg. no. 46,322) at the phone number listed in the Remarks and failed to receive a response to the number being identified as no longer being in service. Additionally, an attempt was made to the phone number listed in the Attorney of Record file for Mark Watson with no response received. Therefore, the corresponding rejections are maintained.
All arguments by the applicant are believed to be covered in the body of the office action; thus, this action constitutes a complete response to the issues raised in the remarks dated 01/26/2026.
Information Disclosure Statement
As required by M.P.E.P. 609(C), the applicant’s submission of the Information Disclosure Statement dated 01/26/2026 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 has been amended to recite “generating a compressed segment by applying the segment to the machine learning model to determine a compression technique applied to the segment based on characteristics of data values included in the segment”. In this manner, it appears that active step of applying the compression technique to the segment has been removed which the claim previously indicated in a positive manner and was discussed to be necessary to the representation of the invention as discussed in the Interview Summary dated 01/16/2026. It is therefore unclear regarding the current recitation that generating the compressed segment through “determin[ing] a compression technique applied to the segment” thereby includes the act of performing the compression step to achieve the compressed segment. The Examiner therefore suggests further amendment to restore the active step of performing the compression which is similarly presented in the recitation of claim 17 as recited by “generating a compressed segment by applying the segment to the machine learning model to determine a compression technique that is applied to the segment based on characteristics of data values included in the segment”. Respective dependent claims 2-7 do not resolve the issue.
Claim 8 recites the similar issue as identified for claim 1 above wherein the positively recited step of performing the compression is removed. Additionally, the limitations lack proper antecedent basis wherein recitation of “segment” should be correspondingly amended to refer to “channel segment” instead as is previously recited in the claim. The Examiner suggests the following amendment: “generating a compressed channel segment by applying the channel segment to the at least one machine learning model to determine a compression technique that is applied to the channel segment based on characteristics of data values included in the channel segment”. Respective dependent claims 9-16 do not resolve the issue.
Claim 17 recites “receiving a query from a system, the query specifying a portion of the storage system monitoring data”. Herein the recitation of “a system” lacks proper antecedent basis with respect to the prior recitation of “when executed by at least one processor of a system, cause the system to perform monitoring operations…” Respective dependent claims 18-20 do not resolve the issue.
Appropriate correction is required.
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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1, 5, 8-9, 13, and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5, 9, 12-13, and 18 of copending Application 18/646,559. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the copending application are a narrower recitation of those in the instant application as demonstrated by the comparison below.
Instant Application
US Application 18/646,559
A storage system monitoring method, comprising: obtaining storage system monitoring data; generating a segment by applying the storage system monitoring data to a machine learning model trained to segment the storage system monitoring data; generating a compressed segment by applying the segment to the machine learning model to determine a compression technique applied to the segment based on characteristics of data values included in the segment; receiving a query from a user system specifying a portion of the storage system monitoring data; in response to the query, performing at least one of: reconstructing and providing the portion using the compressed segment; or providing the compressed segment for reconstruction of the portion.
A storage system monitoring method, comprising: obtaining storage system monitoring data; generating a plurality of compressed storage system monitoring data segments, at least by: segmenting the storage system monitoring data into a plurality of storage system monitoring data segments; and selecting a compression technique for each of the plurality of storage system monitoring data segments; compressing the plurality of storage system monitoring data segments using at least two compression techniques; and in response to a query: identifying at least one of the plurality of compressed storage system monitoring data segments based on time information associated with the compressed storage system monitoring data segment; and performing at least one of: reconstructing and providing a portion of the storage system monitoring data using the at least one compressed storage system monitoring data segment; or providing the at least one compressed storage system monitoring data segment for reconstruction of the portion of the storage system monitoring data.
The storage system monitoring method of claim 1, wherein: the machine learning model is additionally trained to specify a first compression technique applied to the segment upon determining the segment has first characteristics and specify a second compression technique applied to the segment upon determining the segment has second characteristics.
The storage system monitoring method of claim 1, wherein: the storage system monitoring data comprises at least one of a table metric, a message metric, a streaming metric, a compaction metric, a commit log metric, a storage metric, a hint metric, an index metric, a buffer pool metric, a client management metric, a batch metric, or a virtual machine metric.
The storage system monitoring method of claim 1, wherein: the machine learning model is trained to indicate segment boundaries between segments.
The storage system monitoring method of claim 1, wherein: segmenting the storage system monitoring data comprises: segmenting the storage system monitoring data based on at least one of segment size, number of data points, or segment duration.
The storage system monitoring method of claim 1, wherein: the machine learning model comprises at least one neural network model; gradient boosting, decision tree, or random-forest model; or naive Bayes model.
The storage system monitoring method of claim 1, wherein: segmenting the storage system monitoring data comprises: determining at least one of statistics of the storage system monitoring data, a frequency domain representation of the storage system monitoring data, or a wavelet domain representation of the storage system monitoring data.
The storage system monitoring method of claim 1, wherein: the specified compression technique includes at least one of polynomial approximation, frequency domain transformation, or audio encoding.
The storage system monitoring method of claim 1, wherein: segmenting the storage system monitoring data comprises: applying the storage system monitoring data to a machine learning model trained to segment the storage system monitoring data.
The storage system monitoring method of claim 1, wherein: the storage system monitoring data includes CPU 1/0 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.
The storage system monitoring method of claim 1, wherein: the method further comprises selecting one of the at least two compression techniques for one of the storage system monitoring data segments based on at least one of a range, standard deviation, distribution, number of zero-crossings, entropy, or mutual information of the one of the storage system monitoring data segments.
The storage system monitoring method of claim 1, wherein: the method further comprises: obtaining second storage system monitoring data; and generating a reference to the compressed segment by applying the second storage system monitoring data to the machine learning model; and receiving a second query, the second query specifying a portion of the second storage system monitoring data; in response to the second query, performing at least one of: reconstructing and providing the portion of the second storage system monitoring data using reference and the compressed segment; or providing the compressed segment for reconstruction of the portion of the second storage system monitoring data.
The storage system monitoring method of claim 1, wherein: the method further comprises: generating, for one of the storage system monitoring data segments, the plurality of compressed storage system monitoring data segments using different ones of the at least two compression techniques; and selecting one of the multiple compressed storage system monitoring data segments for storage in a data store.
A monitoring system, comprising: at least one processor; and at least one computer-readable medium containing instructions that, when executed by the at least one processor, cause the monitoring system to perform operations, comprising: obtaining multiple channels of storage system monitoring data for a storage system; generating a channel segment by applying at least one of the multiple channels to at least one machine learning model trained to segment the storage system monitoring data; generating a compressed channel segment by applying the segment to the at least one machine learning model to determine a compression technique to the channel segment based on characteristics of data values included in the segment; in response to a query: identifying the compressed channel segment; and performing at least one of: reconstructing and providing a portion of one of the multiple channels of the storage system monitoring data using the compressed channel segment; or providing the compressed channel segment for reconstruction of the portion of the one of the multiple channels of the storage system monitoring data.
The storage system monitoring method of claim 1, wherein: generating the plurality of compressed storage system monitoring data segments further comprises: determining a second storage system monitoring data segment matches a first storage system monitoring data segment; and associating the second storage system monitoring data segment with a compressed version of the first storage system monitoring data segment; identifying the at least one compressed storage system monitoring data segment comprises identifying the second storage system monitoring data segment based on a time associated with the user query and a time associated with the second storage system monitoring data segment; and reconstructing and providing the portion of the storage system monitoring data or providing the at least one compressed storage system monitoring data segment for reconstruction of the portion of the storage system monitoring data comprises: reconstructing, based on the association between the second storage system monitoring data segment and the compressed version of the first storage system monitoring data segment, the portion of the storage system monitoring data using the compressed version of the first storage system monitoring data segment.
The monitoring system of claim 8, wherein: the multiple channels of the storage system monitoring data comprise at least one of 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 storage system monitoring method of claim 1, wherein: the at least two compression techniques include at least one of polynomial approximation, frequency domain transformation, or audio encoding.
The monitoring system of claim 8, wherein: generating the channel segment by applying the at least one of the multiple channels to the at least one machine learning model trained to segment the storage system monitoring data comprises: generating a set of channel segments by applying each one of the multiple channels to a corresponding machine learning model trained to indicate segment boundaries between segments of the one of the multiple channels.
The storage system monitoring method of claim 1, wherein: compressing the plurality of storage system monitoring data segments into the compressed storage system monitoring data segments using the at least two compression techniques comprises determining, and using, differing sets of compression techniques for different storage system monitoring data segments based on differing characteristics of the different storage system monitoring data segments.
The monitoring system of claim 8, wherein: generating the channel segment by applying the at least one of the multiple channels to the at least one machine learning model trained to segment the storage system monitoring data, comprises: generating the channel segment by applying the multiple channels to a machine learning model trained to indicate segment boundaries between segments of the multiple channels based on values of the multiple channels.
The storage system monitoring method of claim 1, wherein: compressing the storage system monitoring data segments into the plurality of compressed storage system monitoring data segments using the at least two compression techniques comprises compressing one of the storage system monitoring data segments into a plurality of first compressed storage system monitoring data segments; and reconstructing and providing the portion of the storage system monitoring data or providing the at least one compressed storage system monitoring data segment for reconstruction of the portion of the storage system monitoring data comprises: reconstructing the first compressed storage system monitoring data segments; and recreating the one of the storage system monitoring data segments by combining the reconstructed first compressed storage system monitoring data segments.
The monitoring system of claim 8, wherein: the channel segment includes a first channel and remainder channels; and generating the channel segment by applying the at least one of the multiple channels to the at least one machine learning model trained to segment the storage system monitoring data, comprises: generating a segment boundary by applying the first channel to a machine learning model trained to indicate segment boundaries between segments of the first channel; and generating the channel segment by segmenting the first channel and the remainder channels using the segment boundary.
A system, comprising: at least one processor; and at least one non-transitory computer-readable medium containing instructions that, when executed by the at least one processor, cause the system to perform operations for monitoring a storage system comprising: obtaining a plurality of channels of storage system monitoring data for the storage system; generating compressed channel segments, at least by: segmenting the channels of the storage system monitoring data into channel segments; and compressing the channel segments using at least two compression techniques; and in response to a query: identifying at least one compressed channel segment; and performing at least one of: reconstructing and providing a portion of one of the multiple channels of the storage system monitoring data using the at least one compressed channel segment; or providing the at least one compressed channel segment for reconstruction of the portion of the one of the multiple channels of the storage system monitoring data.
The monitoring system of claim 8, wherein: the multiple channels of the storage system monitoring data are independently segmented.
The system of claim 12, wherein: the plurality of channels of the storage system monitoring data comprise at least one of 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 monitoring system of claim 8, wherein: the at least one machine learning model comprises at least one neural network model; gradient boosting, decision tree, or random-forest model; or naive Bayes model.
The system of claim 12, wherein: a channel of the channels of the storage system monitoring data is segmented based on at least one of segment size, number of data points, or segment duration.
The monitoring system of claim 8, wherein: the specified compression technique includes at least one of polynomial approximation, frequency domain transformation, or audio encoding.
The system of claim 12, wherein: segmenting the channels of the storage system monitoring data comprises: determining, for a one of the channels of the storage system monitoring data, at least one of statistics of the one of the channels, a frequency domain representation of the one of the channels, or a wavelet domain representation of the one of the channels.
The monitoring system of claim 8, wherein: generating the compressed channel segment further comprises generating at least one updated channel, the updated channel being a function of ones of the multiple channels and being compressed in place of one of the multiple channels.
The system of claim 12, wherein: segmenting the channels of the storage system monitoring data comprises: applying the channels of the storage system monitoring data to a machine learning model trained to segment the channels of the storage system monitoring data.
A non-transitory, computer-readable medium containing instructions that, when executed by at least one processor of a system, cause the system to perform monitoring operations, comprising: obtaining storage system monitoring data; generating a segment by applying the storage system monitoring data to a machine learning model trained to indicate segment boundaries between segments, the machine learning model including at least one of a neural network model; gradient boosting, decision tree, or random-forest model; or naive Bayes model; generating a compressed segment by applying the segment to the machine learning model to determine a compression technique that is applied to the segment based on characteristics of data values included in the segment; receiving a query from a system, the query specifying a portion of the storage system monitoring data; in response to the query, performing at least one of: reconstructing and providing the portion using the compressed segment; or providing the compressed segment for reconstruction of the portion.
The system of claim 12, wherein: segmenting the channels of the storage system monitoring data comprises: applying respective channels of the storage system monitoring data to corresponding machine learning models trained to segment the respective channels of the storage system monitoring data.
The non-transitory, computer-readable medium of claim 17, wherein: the machine learning model is additionally trained to specify the at least one of the polynomial approximation, the frequency domain transformation, or the audio encoding technique applied to the segment.
The system of claim 12, wherein: the channels of the storage system monitoring data are independently segmented.
The non-transitory, computer-readable medium of claim 17, wherein: the storage system monitoring data includes CPU 1/0 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.
The system of claim 12, wherein: generating the compressed channel segments further comprises selecting one of the at least two compression techniques for compressing one of the channel segments based on at least one of a range, standard deviation, distribution, number of zero-crossings, entropy, or mutual information of the one of the channel segments.
The non-transitory, computer-readable medium of claim 17, wherein: the storage system monitoring data includes multiple channels of metric data; and the segment includes the multiple channels and is segmented based on values of one or more of the channels.
The system of claim 12, wherein: generating the compressed channel segments further comprises independently selecting ones of the at least two compression techniques for the channel segments.
The system of claim 12, wherein: generating the compressed channel segments further comprises generating an additional channel segment using one of the channel segments and at least one remaining channel segment of the channel segments; compressing the channel segments into the compressed channel segments comprises compressing the additional channel segment in place of the one of the channel segments; and reconstructing and providing the portion of the one of the plurality of channels of the storage system monitoring data or providing the at least one compressed channel segment comprises: reconstructing the additional channel segment and the at least one remaining channel segment; and recreating the one of the channel segments using the reconstructed additional channel segment and the reconstructed at least one remaining channel segment.
Regarding claim 1, the claim of the instant application is substantially similar to that of Claim 1 and claim 5 of the copending Application as noted by the unbolded portions of each claim in the table above. The bolded portions of claim 1 of the copending application notes the differences and the copending application thereby presenting a narrower scope establishes that the copending application would otherwise anticipate the limitations of the instant application.
Regarding claim 5 of the instant application, the limitations are substantially identical to claim 9 of the copending Application.
Regarding claim 8 of the instant application, the limitations are substantially identical to claim 12 and claim 17 of the copending Application for similar reasons as identified for claim 1.
Regarding claim 9 of the instant application, the limitations are substantially identical to claim 13 of the copending Application.
Regarding claim 13 of the instant application, the limitations are substantially identical to claim 18 of the copending application.
Regarding claim 15 of the instant application, the limitations are substantially identical to claim 9 of the copending application as while claim 9 recites steps directed to a method claim, one of ordinary skill in the art would recognize the system of claim 15 as being capable of executing the method steps.
This is a provisional nonstatutory double patenting rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Cooper et al. (US 2024/0411725) – Paragraphs [0094-95] wherein performing preprocessing and dividing data into segments prior to machine learning based compression is discussed.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER J YOON whose telephone number is (408)918-7629. The examiner can normally be reached on Monday-Friday 8am-3pm ET. The examiner’s email is alexander.yoon2@uspto.gov.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jared Rutz can be reached on 571-272-5535. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALEXANDER YOON/
Examiner, Art Unit 2135
/JARED I RUTZ/Supervisory Patent Examiner, Art Unit 2135
Prosecution Insights