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 .
Claims 2-17 are pending.
Response to Amendment
Applicant presented sufficient showing that 112(f) was not invoked in the response filed 22 July 2026. Consequently, the rejection under 35 U.S.C. 112 is withdrawn.
Response to Arguments
Applicant's arguments filed 22 July 2026 regarding the rejection under 35 U.S.C. 102 over Luo have been fully considered but they are not persuasive.
Applicant argues at page 5 under heading A:
“Luo is directed to a touchscreen user interface for delivered virtual desktops: a client device receives virtual-desktop graphics over a first communications channel and resource-characteristic data over a second channel for presentation on a touchscreen. (Luo, Abstract.) It is not directed to operation-level metrics collection for secondary storage devices in a cloud storage environment”.
In response the examiner is not persuaded. Applicant seems to focus on the touch screen of Luo while ignoring the other teachings of Luo invention at least at paragraphs [0096]-[0098] discussing for example performance monitoring service or agent, dedicated servers, dedicated service in cloud-based environment, data collection, aggregation, analysis, management and reporting, shared computing resources, cache usage, memory usage, performance conditions and metrics, periodic adjustment, agent action frequency.
Applicant argues at page 5 under heading B:
“Claim 2 requires logging I/O and operation-related metrics in a media-agent cache; transferring that data to media-agent local storage; storing it in a management database; correlating it with job-related data maintained in that database; storing processed metrics in a specialized metrics database; and generating a report via a reporting module. Luo discloses none of these elements arranged as claimed”.
In response the examiner is not persuaded. Applicant merely stated “Luo discloses none of these elements arranged as claimed”. The examiner maintains as written, the claimed elements are met by the teachings of Luo as shown in the previous Office action herein repeated in this Final Office action.
Applicant further argues:
“Secondary storage devices. The Office Action maps Luo’s clients 202 and servers 204 to the claimed secondary storage devices, but these are end-user devices and back-end servers for delivering virtual desktops and applications—not secondary storage devices for backup, disaster recovery, or data-protection copies as described in Applicant’s Specification”.
In response the examiner is not persuaded. The examiner’s mapping of the claimed secondary storage devices is consistent with applicant’ s description of secondary storage at least at paragraph [0113].
Furthermore claims are interpreted in light of the specification however the specification cannot be read into the claims. As written, no claim requires a backup secondary storage device for disaster recovery or data protection as argued by the applicant.
Applicant further argues:
“Media agent. Luo’s agents are Citrix Virtual Delivery Agents (VDAs) installed on physical or virtual machines to deliver applications or desktops to users (Luo, ¶¶[0079], [0085])—not media agents that manage payload data to or from secondary storage devices and that include a cache/local-storage metrics pipeline”.
In response the examiner is not persuaded. Applicant seems to argue limitations not reflected in the claim language. Note the claimed media agent is not defined in any particular manner thus is met by the agents taught in Luo throughout the entire specification.
Applicant further argues:
“Specified intervals / cache-to-local-storage transfer. The cited Luo ¶[0158] concerns the touchscreen UI engine deciding whether to send updated touchscreen UI data (e.g., every 30 seconds, or upon an application-notification, new-application, or new-file event). That is a trigger for UI graphics updates—not a transfer of metrics data from a media-agent cache to media-agent local storage”.
In response the examiner clarifies paragraph [0158] was cited to show the teaching of performing an action at time intervals, not to show the transfer of metrics data taught by Luo at least at paragraphs [0096], [0097], [0098]. Furthermore, paragraph [0158] of Luo is not exhaustive of the teaching of time interval.
The monitoring agent of Luo is implemented as components of Citrix Analytics ™ shown at paragraph [0096] and performs activities at predetermined frequency as shown in Luo at least at paragraph [0097] reproduced below:
[0097] The monitoring agents may, for example, monitor, measure, collect, and/or analyze data on a frequency (e.g., a predetermined frequency), based upon an occurrence of given event(s), or in real time during operation of the resource delivery system 500. The monitoring agents may, for example, monitor resource consumption and/or performance of hardware, software, and/or communications resources of the clients 202, the gateway 508 (and/or any other components in the DMZ 528), and/or the resource delivery controller(s) 512, the shared computing resources 502, the resource delivery agents 504, or any other components shown in FIG. 5A.
Applicant further argues:
“Job-related data. The cited Luo ¶[0075] describes only generic cloud resource pooling and multi-tenancy. It discloses no job-related data, no management database maintaining such data, and no correlation of metrics data with it”.
In response the examiner is not persuaded. The claimed job-related data is not defined in any specific manner thus is met by multi-tenancy pooling data of Luo.
Applicant further argues:
“Distinct management and specialized metrics databases. Claim 2 recites separate management and specialized metrics databases. Luo’s database(s) 520 is a Microsoft SQL Server for configuration and session information, and AD 522 is a directory service (Luo, ¶¶[0081], [0084]); neither is a specialized metrics database storing processed metrics produced by correlation with job-related data, and a single Active Directory element cannot satisfy two distinct database limitations. See Becton, Dickinson, 616 F.3d at 1254”.
In response the examiner is not persuaded. Applicant argues limitations not reflected in the claim language. As written no “separate” database is required. Claim 2 merely recites:
“storing the processed metrics data in a specialized metrics database configured for efficient querying an retrieval of metrics information”.
The examiner maintains the claimed limitation is met by item 522 active directory shown in Fig.5A of Luo. This interpretation is consistent with applicant’s specification paragraph [0120] that mentions “As one example, for large file systems where the amount of data stored in management database 146 is relatively large, database 146 may be migrated to or may otherwise reside on a specialized database server (e.g., an SQL server) separate from a server that implements the other functions of storage manager 140”.
Applicant further argues under heading C:
“Claim 8 separately requires a metrics database configured specifically for operation-related metrics, retrieving job-related data from a management database, and processing metrics in conjunction with that data to generate usage and performance reports—none of which Luo discloses, for the reasons above”.
In response the examiner is not persuaded and maintains Lou teaches all the claimed limitations as discussed in the previous Office action and repeated in this final Office action below.
Applicant further argues:
“Claim 12 recites a corresponding system (media agent, centralized storage manager, management database, specialized metrics database, and reporting module) implementing the claimed metrics architecture. Luo’s virtual-desktop delivery system—Citrix Delivery Controller components, VDAs, Active Directory, and SQL configuration/session databases—is not that architecture”.
In response the examiner is not persuaded for the same reasons discussed in claim 2 above.
Applicant further argues under heading D:
“The dependent claims are patentable at least by virtue of their dependence. In addition, claims 6 and 16 recite consolidation with job-related data, claims 7 and 17 recite storage usage reports categorized by tenant, workload type, storage location, and geographical region, and claim 9 recites counters corresponding to specific operations on each of the secondary storage mount paths. The Office Action does not identify corresponding disclosures in Luo”.
In response the examiner is not persuaded. The reports categorized as claimed are clearly suggested by Luo at least at paragraph [0075].
Applicant presents no specific arguments regarding other claims. For all the reasons discussed above, the rejection of all pending claims is maintained using the Luo et al reference of record.
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) of record.
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 [0097], [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 intervals for transferring the metrics data from the cache memory to the local storage component are based on at least one of elapsed time and amount of the metrics data accumulated in the cache memory (see at least the following paragraphs:
[0097] The monitoring agents may, for example, monitor, measure, collect, and/or analyze data on a frequency (e.g., a predetermined frequency), based upon an occurrence of given event(s), or in real time during operation of the resource delivery system 500. The monitoring agents may, for example, monitor resource consumption and/or performance of hardware, software, and/or communications resources of the clients 202, the gateway 508 (and/or any other components in the DMZ 528), and/or the resource delivery controller(s) 512, the shared computing resources 502, the resource delivery agents 504, or any other components shown in FIG. 5A.
[0158]: a trigger event may include the elapsing of a regular time interval, such as every "30" seconds).
Regarding claim 6, Luo further teaches the computer-implemented method of claim 2, wherein processing the metrics data includes consolidating the metrics data with the job-related data for organized storage within the specialized 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 environment (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 of the secondary storage mount paths 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 data stored in the metrics database includes 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 usage and performance reports 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.
Toomey ET AL (US 20250078008 A1) teach mechanisms for providing a single source for a wide range of data and predictive analyses, integrating data describing corporations, finances, stock performance, competition, educational institutions, and/or job markets, in any suitable combination. Various embodiments provide mechanisms for integrating any or all of such data to generate predictive analysis yield expected corporate and/or investment outcomes. In addition, the system and method described herein are able to create and answer questions in an outcome format that can be used to make financial decisions for investors, business executives, boards of directors, and/or the like. In particular, according to various embodiments, the system and method described herein are able to aggregate data from many different sources, such as for example nationwide jobs data, company information, stock portfolios, and/or educational data, and to provide accurate analytical forecasting based on such aggregated data.
Bushnaq et al (US 8010660 B2) teach a server system for supplying differing amounts of data into a database, wherein the system comprises the database and a data tracker device, wherein the data tracker receives data sent by a plurality of functional entities, preferably office machines and in particular devices with a printer, wherein the data tracker device is prepared to transmit data asynchronously to an intermediate buffer storage device with a queue structure, the intermediate buffer storage device storing the data sequentially and transmitting the data sequentially to the database to be stored.
Sen, Tanmoy, and Haiying Shen. "Context-aware Data Operation Strategies in Edge Systems for High Application Performance." Proceedings of the 50th International Conference on Parallel Processing. 2021.
ABSTRACT
Applications running in edge computing system seamlessly collect data, process data and take actions accordingly. In many cases, the applications need to assist people in real time, and even have life or-death consequences such as heart attack detection in healthcare and object detection in driving, which requires low job latency. Moreover, power and bandwidth are constrained resources in edge Data sharing and placement Fog Redundancy elimination computing systems. Therefore, a challenge is how to handle data efficiently to reduce job latency, and meanwhile reduce power and bandwidth consumption. Previous works mainly focus on where to store collected source data to reduce the communication latency for source data sharing. Noticing that intermediate and final processing results may be shared by many applications, we propose to store intermediate and final results for sharing to avoid the duplicated computation. We also propose data collection that reduces data collection frequency based on context-related factors to achieve an optimal tradeoff between the overhead and decision making accuracy. We further propose data redundancy elimination to reduce the redundant data transmitted between edge and fog nodes. Our combined data operation strategies show significant improvement over the state-of-the-art methods in terms job latency, power and bandwidth consumption for experiments on both simulated and real edge environment.
Qadeer A, Lee MJ. Deep-deterministic policy gradient based multi-resource allocation in edge-cloud system: A distributed approach. IEEE Access. 2023 Feb 27;11:20381-98.
ABSTRACT
Edge Cloud (EC) empowers the beyond 5G (B5G) wireless networks to cope with large-scale and real-time traffics of Internet-of-Things (IoT) by minimizing the latency and providing compute power at the edge of the network. Due to a limited amount of resources at the EC compared to the back end cloud (BC), intelligent resource management techniques become imperative. This paper studies the problem of multi-resource allocation (MRA) in terms of compute and wireless resources in an integrated EC and BC environment. Machine learning-based approaches are emerging to solve such optimization problems. However, it is challenging to adopt traditional discrete action space-based methods due to their high dimensionality issue. To this end, we propose a deep-deterministic policy gradient (DDPG) based temporal feature learning attentional network (TFLAN) model to address the MRA problem. TFLAN combines convolution, gated recurrent unit and attention layers together to mine local and long term temporal information from the task sequences for excellent function approximation. A novel heuristic-based priority experience replay (hPER) method is formulated to accelerate the convergence speed. Further, a pruning principle helps the TFLAN agent to significantly reduce the computational complexity and balance the load among base stations and servers to minimize the rejection-rate. Lastly, data parallelism technique is adopted for distributed training to meet the needs of a high-volume of IoT traffic in the EC environment. Experimental results demonstrate that the distributed training approach suites well to the problem scale and can magnify the speed of the learning process. We validate the proposed framework by comparing with five state-of-the art RL agents. Our proposed agent converges fast and achieves up to 28% and 72% reduction in operational cost and rejection-rate, and achieves up to 32% gain in the quality of experience on average, compared to the most advanced DDPG agent.
THIS ACTION IS MADE FINAL. 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 UYEN T LE whose telephone number is (571)272-4021. The examiner can normally be reached M-F 9-5.
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, 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/UYEN T LE/Primary Examiner, Art Unit 2156 1 October 2026
