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 .
Claim 1 has been canceled by a preliminary amendment filed 31 March 2025.
Claims 2-17 are pending.
Claim Interpretation
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Claim limitations “media agent”, “centralized database manager”, “reporting module” have been evaluated under the three-prong test set forth in MPEP § 2181, subsection I, but the result is inconclusive. Thus, it is unclear whether these limitations should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The boundaries of this claim limitation are ambiguous; therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
In response to this rejection, applicant must clarify whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Mere assertion regarding applicant’s intent to invoke or not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph is insufficient. Applicant may:
(a) Amend the claim to clearly invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by reciting “means” or a generic placeholder for means, or by reciting “step.” The “means,” generic placeholder, or “step” must be modified by functional language, and must not be modified by sufficient structure, material, or acts for performing the claimed function;
(b) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, should apply because the claim limitation recites a function to be performed and does not recite sufficient structure, material, or acts to perform that function;
(c) Amend the claim to clearly avoid invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by deleting the function or by reciting sufficient structure, material or acts to perform the recited function; or
(d) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, does not apply because the limitation does not recite a function or does recite a function along with sufficient structure, material or acts to perform that function.
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 (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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 2-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Luo et al (US 20230236854 A1).
Regarding claim 2, Luo discloses, teaches or suggests a computer-implemented method for collecting and processing metrics data in a cloud storage environment (see at least Fig.4), comprising:
logging input/output (I/O) and operation-related metrics data associated with a plurality of secondary storage devices in a cache memory of a media agent (see at least Fig. 2, note the claimed plurality of secondary storage devices is met by clients 202, servers 204; [0097]:other performance conditions and metrics may be monitored, [0105]: logging data). Note the claimed media agent is not formally defined in any specific manner thus reads on the agents performing the monitoring, logging, processing of the gathered data in the method of Luo.
transferring, at specified intervals, the metrics data from the cache memory to a local storage component within the media agent (see at least [0098], [0158]);
transmitting the metrics data stored in the local storage component to a centralized storage manager (see at least Fig.5A items 512-522);
storing the transmitted metrics data in a management database accessible by the centralized storage manager (see at least Fig.5A items 520, 522);
processing the metrics data stored in the management database by correlating the metrics data with job-related data maintained in the management database to generate processed metrics data (see at least [0075]: resource pooling, multitenant environment);
storing the processed metrics data in a specialized metrics database configured for efficient querying and retrieval of metrics information (see at least Fig.5A item 522 active directory); and
generating, using a reporting module, a report based on the processed metrics data stored in the specialized metrics database (see at least [0075]: generate reports corresponding to the provided shared services and resources).
Regarding claim 3, Luo further teaches the computer-implemented method of claim 2, wherein the metrics data includes details related to at least one of operation types, geographical regions, tenant information, application programming interface (API) call data, and cost implications (see at least [0097]: other performance conditions and metrics may be monitored).
Regarding claim 4, Luo further teaches the computer-implemented method of claim 2, further comprising transmitting the report to the centralized storage manager for facilitating informed resource allocation and system management decisions (see at least Fig.5D,
[0107]: Within the resource delivery controller 512, the broker service 532 may report session data for every session on the machine providing real-time data. The monitor service 560 may also track the real-time data and store it as historical data in the database(s) 520. In some implementations, the resource manager 514 may communicate with the broker service 532 and may access real-time data. The resource director 516 may communicate with the broker service 532 to access the database(s) 520.
[0096]:the resource delivery system 500 may additionally include a performance monitoring service or agent. In some embodiments, one or more dedicated servers (or a dedicated service in a cloud-based environment) may be employed to perform performance monitoring. Performance monitoring may be performed using data collection, aggregation, analysis, management and reporting, for example by software, hardware or a combination thereof. Performance monitoring may include one or more agents for performing monitoring, measurement and data collection activities on one or more clients 202 (e.g., as a part of the resource access application 524), one or more servers 204, or one or more other system components).
Regarding claim 5, Luo further teaches the computer-implemented method of claim 2, wherein the specified interval for transferring metrics data from the cache memory to local storage is based on at least one of elapsed time and size of accumulated metrics data (see at least [0158]: a trigger event may include the elapsing of a regular time interval, such as every “30” seconds. In other implementations, a trigger event may additionally or alternatively include receipt or detection of a particular type of data).
Regarding claim 6, Luo further teaches the computer-implemented method of claim 2, wherein processing the metrics data includes consolidating the metrics data with associated job identifiers for organized storage within the metrics database (see at least [0075]: resource pooling to serve multiple users via clients 202 through a multi-tenant environment or multi-tenant model with different physical and virtual resources dynamically assigned and reassigned responsive to different demands within the respective environment).
Regarding claim 7, Luo further teaches the computer-implemented method of claim 2, wherein generating the report includes detailing storage system usage patterns at a granular level, categorized by at least tenant, workload type, storage location, and geographical region (see at least [0075]: The cloud computing environment 400 can provide an elasticity to dynamically scale out or scale in response to different demands from one or more clients 202.).
Regarding claim 8, Luo discloses, teaches or suggests a computer-implemented method for managing operation-related metrics in a cloud storage system (see at least Fig.4), comprising:
logging operation-related metrics data from multiple secondary storage mount paths into a cache associated with a media agent (see at least Fig. 2, note the claimed plurality of secondary storage devices is met by clients 202, servers 204; [0097]:other performance conditions and metrics may be monitored, [0105]: logging data). Note the claimed media agent is not formally defined in any specific manner thus reads on the agents performing the monitoring, logging, processing of the gathered data in the method of Luo;
periodically moving the logged metrics data from the cache to local storage of the media agent (see at least [0098], [0158]);
transmitting the metrics data from the local storage to a storage manager component (see at least Fig.5A items 512-522);
storing the transmitted metrics data in a metrics database configured specifically for storing operation-related metrics (see at least Fig.5A items 520, 522);
retrieving job-related data from a management database (see at least Fig.5A item 522 active directory);
processing the metrics data stored in the metrics database in conjunction with the retrieved job-related data to generate usage and performance reports (see at least [0075]: resource pooling, multitenant environment, generate reports corresponding to the provided shared services and resources); and
outputting the reports to facilitate analysis of cloud storage performance and resource utilization (see at least [0075]: resource pooling, multitenant environment, Fig.6 item 608 physical device including output device).
Regarding claim 9, Luo further teaches the computer-implemented method of claim 8, wherein logging operation-related metrics comprises tracking and updating counters corresponding to specific operations on each mount path managed by the media agent (see at least [0106]: FIG. 5D shows examples of paths through which the resource manager 514 and the resource director 516 may access such data in some embodiments. As indicated by the arrows 552 and 554, administrators may use the resource manager 514 to access real-time data from the broker agent 556 of a resource delivery agent 504 (via the broker service 532 of the resource delivery controller 512). The resource director 516 may access the same data, as indicated by arrows 558 and 554, plus any historical data the monitor service 560 of the resource delivery controller 512 stores in the database(s) 520, as indicated by arrows 558, 562 and 564. Further, as indicated by arrow 566, the resource director 516 may also access data from the gateway 508 for help desk support and troubleshooting.).
Regarding claim 10, Luo further teaches the computer-implemented method of claim 8, wherein the metrics database stores metrics data including operation type, tenant identity, geographical usage region, and API interaction details (see at least [0098] The monitoring agents may provide application performance management for the resource delivery system 500. For example, based upon one or more monitored performance conditions or metrics, the resource delivery system 500 may be dynamically adjusted, for example periodically or in real-time, to optimize application delivery by the resource delivery agents 504 to the clients 202 based upon network environment performance and conditions).
Regarding claim 11, Luo further teaches the computer-implemented method of claim 8, wherein the reports generated by the reporting module are configured to inform decisions regarding performance optimization, resource allocation, and cost management in the cloud storage environment (see at least [0098] The monitoring agents may provide application performance management for the resource delivery system 500. For example, based upon one or more monitored performance conditions or metrics, the resource delivery system 500 may be dynamically adjusted, for example periodically or in real-time, to optimize application delivery by the resource delivery agents 504 to the clients 202 based upon network environment performance and conditions).
Claims 12-17 essentially recite limitations similar to claims 2-7 in form of system thus are rejected for the same reasons discussed in claims 2-7 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
ROSIER (WO 2016028669 A1) teaches a system can correlate derived metrics for system activity to determine problems and recommend solutions. Using a hierarchy of derived metrics from a set of raw metrics, a system can identify a problem, correlate related metrics and determine a recommended solution. For example, raw metrics can be collected about computing resources. Analyzers can process the raw metrics and outputs from other analyzers to gather metrics that include metrics derived from other metrics. When a problem symptom is discovered, derived metrics (and other metrics) can be correlated with the symptom to help identify the problem. Using the correlated metrics, a system can recommend a solution to an identified problem.
Richter et al (US 20120317274 A1) teach distributed metering and monitoring service (DMMS) system provides a way to gather and maintain metrics data which remains distributed, until requested. The DMMS system uses messaging queues to scale the number of servers that may be monitored and metered to a hyperscale of greater than 10,000 servers. The DMMS system determines how many servers (nodes) to assign to a cluster, and uses a metric aggregator to collect and store metrics data for the nodes. The DMMS system creates message queues for the instances, injects instance identifiers into the cluster state data and metrics data, listens for request messages for metering information for instances, retrieves the metrics data for users identified by the instance identifiers stored locally at the nodes, and calculates the metering information for the instance.
WADEKAR et al (US 20230168929 A1) teach a method includes receiving a reservation request corresponding to resource requirements of an application. The reservation request including an amount of resources requested for the application. Determining an initial intra-tenant threshold based on the reservation request. Reserving an amount of intra-tenant resources. The amount of intra-tenant resources reserved being greater than the amount of resources requested. Monitoring tenant resource usage assigned to execute the application. The method further includes storing resource usage data periodically. The method further includes predicting future tenant resource usage based on the resource usage data. The method further includes responsive to the predicted future tenant resource usage, performing at least one of: determining a new intra-tenant threshold to be recommended in response to the initial intra-tenant threshold being set too high or too low, or generating an alert indicating that the initial intra-tenant threshold is insufficient to support the predicted future tenant resource usage.
Dilley et al (US 10791168 B1) teach a system is provided to manage operation of workloads over a workload placement network comprising: a user interface to receive workload placement specifications that indicate locations; a data storage device storing cluster location information; a workload placement manager to determine placement of workloads at clusters based at least in part upon cluster locations and cluster resource utilization; wherein the clusters include metrics collector instances to collect information indicating cluster resource utilization and to send the collected information over the workload placement network to the placement the orchestration manager.
Mouline et al (US 20130304904 A1) teach systems and methods for a cloud management system which utilizes both technical and business metrics to achieve operational efficiencies. The systems and methods can be used to provide an elastic infrastructure model for an emergency notifications system which delivers near infinite scale with guaranteed near 100% uptime. In an embodiment, a mass recipient emulator can be utilized for testing of the notifications system with actual phone call or message exchange.
Nimmagadda, Srikanth. "Intelligent Data Tiering with Access-Aware Storage Protocols: Architectures, Algorithms, and Applications." Int. J. Innov. Res. Sci. Eng. Technol 12.8 (August 2023).
ABSTRACT: The rapid growth of data in enterprise and cloud environments necessitates intelligent storage management techniques that can balance performance, cost, and scalability. This paper explores intelligent data tiering—the automated and dynamic placement of data across different storage classes (hot, warm, cold)—enabled by access-aware storage protocols. By leveraging AI/ML techniques and access pattern analytics, the proposed approach facilitates predictive tiering decisions that enhance storage efficiency and minimize operational costs. The study presents architectural models, protocol enhancements, and tiering algorithms applicable to file, block, and object storage systems. Experimental evaluations demonstrate significant improvements in latency optimization and cost performance trade-offs, validating the practical applicability of intelligent tiering in hybrid and cloud-native environments.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UYEN T LE whose telephone number is (571)272-4021. The examiner can normally be reached M-F 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay M Bhatia can be reached at 5712723906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/UYEN T LE/Primary Examiner, Art Unit 2156 16 April 2026
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