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 1-20 are currently pending for examination.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 11, 12, 16, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Meriac (US 20190213038 A1) in view of Norton (US 7701946 B1) in further view of Seigel (US 20170230324 A1) in further view of Colon (US 12511182 B1).
As per claim 1, Meriac discloses:
The system for determining and prioritizing interruption events to improve computer processing and performance in an electronic network, the system comprising: a memory device with computer-readable program code stored thereon; at least one processing device operatively coupled to the at least one memory device and the at least one communication device, wherein executing the computer-readable code is configured to cause the at least one processing device to: identify at least one interruption event (“According to a fifth technique, there is provided a computer readable medium having computer readable program code embodied thereon for performing the methods described herein.”, 0007 ; “According to a third technique, there is provided a data processor for prioritizing system interrupts in a processing system”, 0005 ; “As illustrated in FIG. 6, a system comprises APP0 100 and APP1 110. Both applications require the TLS module and the Crypto API module in order to perform secure communications”, 0065 ; "determining, at a supervisor module, for each interrupt, a relative interrupt priority in accordance with at least one interrupt parameter for said interrupt; prioritizing, at said supervisor module, each said interrupt with respect to other interrupts of said system in compliance with said determined relative interrupt priority", 0003; “In addition, the assigned priority parameter associated with the interrupts issued from the module 14 is “important”. A “critical” interrupt is deemed to be higher in the hierarchy of assigned priorities than an “important” interrupt.”, 0022)
Meriac discloses the above limitations of claim 1, but does not disclose applying the interruption events to a diffusion engine (SDS) to determine a number of computing agents.
However, Norton discloses:
apply the at least one interruption event to a stochastic diffusion search (SDS) engine; determine, by the SDS engine, a number of computing agents (“The apparatus includes a local cache and a data diffusion engine coupled to the local cache. The data diffusion engine is further to create one or more random connections to transmit event data between the apparatus and agents in a data management network, the apparatus and the agents to maintain the event data for the data management network without utilizing a central management system”, col.2, lines 8-13; Examiner Note: using a diffusion engine to create connections between agents equates to determining a number of computing agents, and event data corresponds to an interruption event )
It would have been obvious to one of ordinary skill in the art to combine the teachings of Meriac with those of Norton in order to provide a system of agents which interact with each other to cause event data to quickly diffuse through the network (Norton, [col.3, lines 46-48]).
Meriac in view of Norton discloses the above limitations of claim 1, but does not explicitly disclose the analysis of interruption events by agents to update priority states.
However, Seigel discloses:
analyze the at least one interruption event by the number of computing agents; update, by the number of computing agents, at least one agent priority state for the at least one interruption event (“The deputized agent may analyze the event logs received from peer agents in the group, identify high priority event logs, and forward the high priority event logs to the coordinator. Thus, the coordinator may continue to receive high priority event logs from the group”, 0016 ; Examiner Note: prioritizing events (corresponding to interrupts) by the deputized agent, which receives events from other agents, equates to analyzing the interrupt event by a number of agents)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Meriac in view of Norton with those of Seigel in order to provide a means to avoid the central server becoming overloaded by event logs through the use of a deputized agent which best meets various criteria (Seigel, [0065]).
Meriac in view of Norton in further view of Seigel discloses the above limitations of claim 1, but does not explicitly disclose use of a diagnostic inference model to determine whether an event is critical, non-critical, or a false event.
However, Colon discloses:
apply the at least one agent priority state to a diagnostic inference model (DIM); and determine, by the DIM, whether the at least one interruption event is a critical event, a non-critical event, or a false event. (“ A detection and enrichment system can use Bayesian inference to model the likelihood that a co-occurrence of a detection event and an enriched detection event indicate an actual attack.”, col.2, lines 43-46 ; Examiner Note: a Bayesian inference model corresponds to diagnostic inference model, and the lack of an occurrence of an attack corresponds to a false interruption event.)
The combination of Meriac in view of Norton in further view of Seigel in further view of Colon would be capable of analyzing an interruption event, by an inference model, in order to determine whether it is critical, non-critical, [Meriac: "an interrupt received from the periodic system timer module 12, which has a “critical” assigned priority, is prioritised over an interrupt received from the module 14, which has an “important” assigned priority by the interrupt controller.", 0023 ; Examiner Note: a priority state of important, not being critical, equates to “non-critical”] or false. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Meriac in view of Norton in further view of Seigel with those of Colon in order to provide a system which advantageously considers priority risk indicators in the determination of a false interrupt, or false detection (Colon, [col.12, lines 18-28]).
As per claim 2, Meriac in view of Norton in further view of Seigel in further view of Colon fully discloses the limitations of claim 1.
Furthermore, Meriac discloses:
determine a prioritization of the at least one interruption event based on whether the at least one interruption event is the critical event, the non-critical event, or the false event ( "an interrupt received from the periodic system timer module 12, which has a “critical” assigned priority, is prioritised over an interrupt received from the module 14, which has an “important” assigned priority by the interrupt controller.", 0023)
Meriac discloses the above limitations of claim 2, but does not disclose the application of an event to the event management system based upon the prioritization.
However, Seigel discloses:
apply the at least one interruption event to an event management system based on the prioritization.("The deputized agent may analyze the event logs received from peer agents in the group, identify high priority event logs, and forward the high priority event logs to the coordinator. Thus, the coordinator may continue to receive high priority event logs from the group, without receiving a high volume of lower priority event logs from the group.", 0016; " The deputized agent may analyze the event logs generated by the peer agents and send (e.g., forward) event logs having a high priority", 0065)
As per claim 11, it is a computer program product claim comprising substantially the same limitations as claim 1. As such, it is rejected for substantially the same reasons.
As per claim 12, it is a computer program product claim comprising substantially the same limitations as claim 2. As such, it is rejected for substantially the same reasons.
As per claim 16, it is a computer implemented method claim comprising substantially the same limitations as claim 1. As such, it is rejected for substantially the same reasons.
As per claim 17, it is a computer implemented method claim comprising substantially the same limitations as claim 2. As such, it is rejected for substantially the same reasons.
Claims 3-5, 13-14, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Meriac (US 20190213038 A1) in view of Norton (US 7701946 B1) in further view of Seigel (US 20170230324 A1) in further view of Colon (US 12511182 B1) in further view of Nyamwange (US 20250028621 A1) in further view of Ye (US 20180234493 A1).
As per claim 3, Meriac in view of Norton in further view of Seigel in further view of Colon fully discloses the limitations of claim 1, but does not disclose the use of an AI plugin to predict idle time or peak load time of a server.
However, Nyamwange discloses:
apply the at least one interruption event to an artificial intelligence (AI) plugin, wherein the AI plugin is trained to predict an idle time or a peak load time associated with a server; and determine, based on the idle time or the peak load time, a mode associated with the number of computing agents ("For example, the AI/ML model may predict that a resource is idle", 0063 ; "The method may include training the AI/ML model by using the historical telemetry data, the historical trace log data, and calculated activity levels, to predict an activity level of a resource based on recent telemetry data and recent trace log data.", 0014 ; “The method may include the computer processor running the AI/ML model to determine the activity level of the resource over a time spanning three or more months within six months of the current date. To be designated as an active server, an active hypervisor, and an active virtual machine, the resources may have an activity level of at least 10%. To be designated as an idle server, an idle hypervisor, and an idle virtual machine, these resources may have an activity level of less than 10%.”, 0016 ; Examiner Note: a state of active within a data center equates to a mode associated with a number of computing agents)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Meriac in view of Norton in further view of Seigel in further view of Colon with those of Nyamwange in order to provide the system with the ability to identify underutilized resources which may waste floor space, increase energy consumptions and costs, and introduce security risks (Nyamwange, [0002]).
Meriac in view of Norton in further view of Seigel in further view of Colon in further view of Nyamwange fully disclose the above limitations of claim 3, but they do not disclose a mode which dynamically scales the number of computing agents in order to analyze an interruption event.
However, Ye discloses:
a mode associated with the number of computing agents, wherein the mode determined dynamically scales the number of computing agents to analyze the at least one interruption event. ("a timing scale-up mode that is based on a time period and in which an elastic scale-up operation is regularly triggered; a service dynamic scale-up mode in which it is dynamically determined, based on a service monitoring performance indicator, whether to execute elastic scale-up on a service", 0031)
The combination of Meriac in view of Norton in further view of Seigel in further view of Colon in further view of Nyamwange in further view of Ye would be operate in modes which dynamically scale the computing agents used to analyze an interruption event (see Seigel [0065]). It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Meriac in view of Norton in further view of Seigel in further view of Colon in further view of Nyamwange with those of Ye in order to provide the system with elastic scaling technology which may be used to improve fault tolerance and availability (Ye, [0003]).
As per claim 4, Meriac in view of Norton in further view of Seigel in further view of Colon in further view of Nyamwange in further view of Ye fully discloses the limitations of claim 3.
Furthermore, Nyamwange discloses:
the AI plugin is pretrained based on a metrics database comprising historical data of the server, further comprising optimal performance time and optimal response time. ("The method may include training the AI/ML model by using the historical telemetry data, the historical trace log data, and calculated activity levels, to predict an activity level of a resource based on recent telemetry data and recent trace log data.", 0014)
As per claim 5, Meriac in view of Norton in further view of Seigel in further view of Colon in further view of Nyamwange in further view of Ye fully discloses the limitations of claim 3.
Furthermore, Nyamwange discloses:
the mode comprises at least one of an idle mode, a self-healing mode, a normal mode, or a high performance mode. (“The resource may include a server, a hypervisor, or a virtual machine. The resource pair may include an active server and an idle server”, 0012 ; Examiner Note: an active server equates to a server in normal mode, an idle server equates to a server in an idle mode)
As per claim 13, it is a computer program product claim comprising substantially the same limitations as claim 3. As such, it is rejected for substantially the same reasons.
As per claim 14, it is a computer program product claim comprising substantially the same limitations as claim 4. As such, it is rejected for substantially the same reasons.
As per claim 18, it is a computer implemented method claim comprising substantially the same limitations as claim 3. As such, it is rejected for substantially the same reasons.
As per claim 19, it is a computer implemented method claim comprising substantially the same limitations as claim 4. As such, it is rejected for substantially the same reasons.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Meriac (US 20190213038 A1) in view of Norton (US 7701946 B1) in further view of Seigel (US 20170230324 A1) in further view of Colon (US 12511182 B1) in further view of Nyamwange (US 20250028621 A1).
As per claim 6, Meriac in view of Norton in further view of Seigel in further view of Colon fully discloses the limitations of claim 1, but does not disclose applying the at least one interruption event to at least one bot trained with a metrics database.
However, Nyamwange discloses:
executing the computer-readable code is configured to cause the at least one processing device to: apply the at least one interruption event to at least one bot trained with a metrics database, wherein the metrics database comprises historical metric data associated with historical interruption events ("The method may include training the AI/ML model by using the historical telemetry data, the historical trace log data, and calculated activity levels, to predict an activity level of a resource based on recent telemetry data and recent trace log data.", 0014 ; Examiner Note: the AI/ML model equates to a bot)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Meriac in view of Norton in further view of Seigel in further view of Colon with those of Nyamwange in order to provide the system with the ability to identify underutilized resources which may waste floor space, increase energy consumptions and costs, and introduce security risks (Nyamwange, [0002]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Meriac (US 20190213038 A1) in view of Norton (US 7701946 B1) in further view of Seigel (US 20170230324 A1) in further view of Colon (US 12511182 B1) in further view of Sharma (US 20200328950 A1).
As per claim 7, Meriac in view of Norton in further view of Seigel in further view of Colon fully discloses the limitations of claim 1, but does not disclose the at least one agent priority state being associated with at least one of a weight or a confidence score
However, Sharma discloses:
the at least one agent priority state is associated with at least one of a weight or a confidence score ("in accordance with further example implementations, the network issue prioritization engine 120 may apply different weights to these tiers for purposes of determining the scores. Moreover, the weighting may be selected by, for example, configuration options that are provided by the IT analyst 117 via the GUI 116. For example, in accordance with some implementations, the IT analyst 117 may assign weights in a non-uniform manner for purposes of determining the score, such as, for example, a weight of “1.5” to the F frequency metric value, and weights of “1” to each of the R recency metric value and P priority value.", 0040 ; Examiner Note: the priority value equates to an agent priority state)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Meriac in view of Norton in further view of Seigel in further view of Colon with those of Sharma in order to provide the system manager or IT analyst with color coded prioritization results (from red to green) which make identifying high priority issues faster and easier (Sharma, [0039]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Meriac (US 20190213038 A1) in view of Norton (US 7701946 B1) in further view of Seigel (US 20170230324 A1) in further view of Colon (US 12511182 B1) in further view of Trundle (US 9013294 B1).
As per claim 8, Meriac in view of Norton in further view of Seigel in further view of Colon fully discloses the limitations of claim 1, but does not disclose the weight or the confidence score being compared to at least one belief state threshold in order to determine priority.
However, Trundle discloses:
the at least one of the weight or the confidence score is compared to at least one belief state threshold, and based on the comparison, the at least one agent priority state is determined ("For example, the monitoring server 30 may compare an alarm probability score associated with a detected alarm event to both a first, lower threshold, may determine that the alarm probability score does not satisfy the threshold, and based on the alarm probability score not satisfying the first threshold may assign the detected alarm event a low priority. In another example, the monitoring server may compare an alarm probability score to a first, lower threshold, may determine that the alarm probability score satisfies the threshold, and may also compare the alarm probability score to a second, higher threshold, where the monitoring server 30 may determine that the alarm probability score does not satisfy the second threshold. Based on the alarm probability score satisfying the first threshold but not the second threshold, the monitoring server 30 may assign the detected alarm event a medium priority. In yet another example, the system may compare an alarm probability score to a first, lower threshold as well as a second, higher threshold, may determine that the alarm probability score satisfies both the first and second thresholds, and as a result may assign the detected alarm event a high priority.", col.7, lines 23-43 ; Examiner Note: the alarm probability score corresponds to a confidence score)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Meriac in view of Norton in further view of Seigel in further view of Colon with those of Trundle in order to provide an alarm probability measure which assists the system in better utilizing resources and decreasing response time to actual alarm situations (Trundle, [col.2, lines 1-6]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Meriac (US 20190213038 A1) in view of Norton (US 7701946 B1) in further view of Seigel (US 20170230324 A1) in further view of Colon (US 12511182 B1) in further view of Ansari (US 20100173689 A1).
As per claim 9, Meriac in view of Norton in further view of Seigel in further view of Colon fully discloses the limitations of claim 1.
Furthermore, Norton discloses:
the number of computing agents exchange data in a diffusion process ( “The data diffusion engine is further to create one or more random connections to transmit event data between the apparatus and agents in a data management network”, col.2, lines 8-14)
Meriac in view of Norton in further view of Seigel in further view of Colon fully discloses the above limitations, but does not disclose a diffusion process comprising a probabilistic exchange between a number of computing agents
However, Ansari discloses:
the diffusion process further comprises a probabilistic exchange between the number of computing agents ("Stochastic diffusion search (SDS) is an agent-based, probabilistic, global search and optimization technique used to solve problems where the objective function can be decomposed into multiple independent partial-functions. Each agent maintains a hypothesis that is iteratively tested by evaluating a randomly selected, partial objective function having parameters populated by the agent's current hypothesis. The partial function evaluations can be binary (e.g., an agent is either active or inactive). Information on hypotheses is diffused across the population via inter-agent communication... A positive feedback mechanism can be used to stabilize a population of agents about a global-best solution.", 0017)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Meriac in view of Norton in further view of Seigel in further view of Colon with those of Ansari in order to provide agents which can communicate hypotheses using a more direct, one-to-one communication strategy (Ansari, [0017]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wen (US 20250321571 A1) – discloses multi-model fused avionic product health assessment method comprising: collecting relevant data of an avionic product ; training a plurality of base models on the basis of the first data; performing quantitative measurement and fusion on the plurality of base models to obtain an integrated model; and inputting into the integrated model the second data which serves as a test sample to obtain a health assessment result of the avionic product.
Gopalakrishnan (US 20240303108 A1) – discloses a method for assignment and prioritization of tasks for satisfying deadlines in decentralized execution of tasks comprising; receiving inputs that relate to a set of tasks, a set of agents, a set of goals, a set of priority levels that are assignable to each task, and a partial order plan that relates to ordering dependencies for performing and completing the tasks – as well as a prioritization function that relates to a proposed set of assignments of tasks to priority levels
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/R.M.V./
Examiner, Art Unit 2196
/APRIL Y BLAIR/Supervisory Patent Examiner, Art Unit 2196
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