DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 filed on 5/17/2023 have been reviewed and considered by this office action.
Information Disclosure Statement
The information disclosure statements filed on 5/17/2023, 1/7/2025, 4/8/2025, 4/11/2025 and 6/3/2025 have been reviewed and considered by this office action.
Drawings
The drawings filed on 5/17/2023 have been reviewed and is considered acceptable.
Specification
The specification filed on 5/17/2023 has been reviewed is considered acceptable.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more. Claim 1 recites, “generate, based on the initial telemetry data, an initial telemetry state image;”, “generate, based on the additional telemetry data, an additional telemetry state image;”, “compare a pattern, corresponding to the initial telemetry state image, the additional telemetry state image, and a transition between the initial telemetry state image and the additional telemetry image, to the telemetry state images and the transitions between the telemetry state images of the rules-based state machine to identify a matching pattern;”, and “identify, using the identified matching pattern, a likelihood of failure for the system;”, which analyzed under Step 2A Prong One, includes generating telemetry state images off of received data which can reasonably be performed using pen and paper and further includes comparing/identifying patterns that could result in failure of the system which can reasonably be performed in the human mind, and thus, falls within the, “Mental Processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. For instance, claim 1 further includes the limitation of, “and send, based on the likelihood of failure for the system, one or more preemptive resolution commands causing modification of operations at the system to prevent a predicted failure.”, which analyzed under Step 2A Prong Two, includes sending commands to cause a modification to the system, however, review of the specification failed to provide details on what, “modifications” actually entail and thus could be broadly interpreted as simply adjusting control parameters which would simply apply the use of the judicial exception (see MPEP 2106.05(f)). Further, claim 1 includes the limitations of, “receive initial telemetry data;” and “receive additional telemetry data;”, which analyzed under Step 2A Prong Two, adds insignificant extra solution activity in the form of mere data gathering (see MPEP 2106.05(g)). Continuing, claim 1 includes the limitation of, “configure a rules-based state machine to predict system failure for a system based on telemetry state images and transitions between the telemetry state images;”, which analyzed under Step 2A Prong Two, just simply links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Finally, the limitations of, “at least one processor”, “a communication interface”, “memory”, and “a rules-based state machine”, as generally recited represent merely generic computer components for implementing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed under Step 2B, the additional elements merely amount to gathering telemetry data and sending the data over a network. Analyzed under Berkheimer, the act of gathering and sending data over a network has been deemed as well-understood, routine, and conventional by the courts (see MPEP 2106.05(d)(II), “sending/receiving data over a network”).
Independent claims 11 and 20 are substantially similar to claim 1 and are thus rejected using the same rationale as provided above.
Dependent claims 2-6, 8-10, 12-16 and 18-19 are rejected under 35 U.S.C. 101 for being directed towards an abstract idea without significantly more. For instance, claims 2-6, 10, and 12-16, each include further limitations of generating images, comparing data, identifying, predicting, and labeling, which analyzed under Step 2A Prong One, are all limitations that can reasonably be performed using pen and paper/the human mind, and thus, fall within the, “Mental processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. Claims 8-9 and 18-19, include limitations of displaying solutions to a user and receiving selection from a user based on the presented solutions without describing the functionality of how the selected solution affects operation of the system, which analyzed under Step 2A Prong Two, simply applies the use of the judicial exception (see MPEP 2106.05(f)). Further, claims 2, 10, and 12, each include additional limitations describing gathering of data, which analyzed under Step 2A Prong Two, adds insignificant extra solution activity in the form of mere data gathering (see MPEP 2106.05(g)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed under Step 2B, the additional elements merely amount to gathering telemetry data and sending the data over a network. Analyzed under Berkheimer, the act of gathering and sending data over a network has been deemed as well-understood, routine, and conventional by the courts (see MPEP 2106.05(d)(II), “sending/receiving data over a network”).
***Examiner’s note: Claims 7 and 17 each describe wherein the resolution commands cause a server to redirect incoming requests away, thus, showing how the system positively reacts to circumvent the potential failure which shows significantly more than the abstract idea. The office recommends incorporating all limitations detailed in these claims into independent form in order to overcome the current rejection.***
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gamage et al. (US PGPUB 20160292028) in view of Garbow (US PGPUB 20070101202).
Regarding Claims 1, 11, and 20; Gamage teaches; A computing platform comprising: at least one processor; (Gamage; at least Fig. 1; processor (30))
a communication interface communicatively coupled to the at least one processor; and (Gamage; at least Fig. 1; interface (34))
memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: (Gamage; at least Fig. 1; hard disk (36))
configure a rules-based state machine to predict system failure for a system based on telemetry state images and transitions between the telemetry state images; (Gamage; at least paragraphs [0002] and [0026]; disclose a system and method for configuring and storing a plurality event sequences that can correlate the sequence of events (i.e. wherein the sequence can include a plurality of states and transitions) to predict potential future failures in a system)
receive initial telemetry data; (Gamage; at least paragraphs [0049] and [0057]; disclose receiving real-time data from a plurality of components)
generate, based on the initial telemetry data, an initial telemetry state image; (Gamage; at least paragraph [0049]; disclose storing real-time events to generate an initial image)
receive additional telemetry data; (Gamage; at least paragraphs [0049] and [0057]; disclose receiving additional real-time data from a plurality of components)
generate, based on the additional telemetry data, an additional telemetry state image; (Gamage; at least paragraph [0049]; disclose storing real-time events to generate subsequent images to create a sequence)
compare a pattern, corresponding to the initial telemetry state image, the additional telemetry state image, and a transition between the initial telemetry state image and the additional telemetry image, to the telemetry state images and the transitions between the telemetry state images of the rules-based state machine to identify a matching pattern; (Gamage; at least paragraphs [0057]-[0066]; disclose comparing patterns of the sequence of events to a plurality of stored events and identifying any patterns that match the sequence of events recorded in real-time)
and send, based on the likelihood of failure for the system, one or more preemptive resolution commands causing modification of operations at the system to prevent a predicted failure. (Gamage; at least paragraph [0066]; disclose taking various actions such as changing component setting configurations or offloading workloads to different servers to prevent the predicted failure).
Gamage appears to be silent on; identify, using the identified matching pattern, a likelihood of failure for the system;
and send, based on the likelihood of failure for the system, one or more preemptive resolution commands causing modification of operations at the system to prevent a predicted failure.
However, Garbow teaches; identify, using the identified matching pattern, a likelihood of failure for the system; (Garbow; at least paragraphs [0065]-[0066]; disclose a system and method for monitoring real-time server statistics to generate patterns and comparing the patterns to pre-fault clusters to determine a probability of a failure occurring in the system)
and send, based on the likelihood of failure for the system, one or more preemptive resolution commands causing modification of operations at the system to prevent a predicted failure. (Garbow; at least paragraphs [0065]-[0066]; disclose initiating mitigation measures based upon the probability of a potential failure of the system).
Gamage and Garbow are analogous art because they are from the same field of endeavor or
similar problem solving area, of pattern recognition and failure prediction control systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the disclosed invention to have incorporated the known method of determining probability of the likelihood of failure as taught by Garbow with the known system of a pattern recognition and failure prediction control system as taught by Gamage in order to provide measures for facilitating prediction of server failures and implementation of proactive measures to mitigate the effects of potential failures as taught by Garbow (paragraph [0069]).
Regarding Claims 2 and 12; the combination of Gamage and Garbow teach; The computing platform of claim 1, wherein configuring the rules-based state machine comprises: receiving historical telemetry data; normalizing the historical telemetry data; generating, based on the historical telemetry data, the telemetry state images; identifying the transitions between the telemetry state images; and labelling historical patterns corresponding to the telemetry state images and the transitions between the telemetry state images based on detected failures. (Gamage; at least paragraphs [0048]-[0051]).
Regarding Claims 3 and 13; the combination of Gamage and Garbow teach; The computing platform of claim 1, wherein: generating, based on the initial telemetry data, the initial telemetry state image comprises: normalizing the initial telemetry data, and generating the initial telemetry state image based on the normalized initial telemetry data; and generating, based on the additional telemetry data, the additional telemetry state image comprises: normalizing the additional telemetry data, and generating the additional telemetry state image based on the normalized additional telemetry data. (Gamage; at least paragraphs [0057]-[0066]).
Regarding Claims 5 and 15; the combination of Gamage and Garbow teach; The computing platform of claim 1, wherein identifying, using the identified matching pattern, the likelihood of failure for the system comprises: identify a likelihood of failure of the matching pattern, wherein the matching pattern is labelled based on the likelihood of failure of the matching pattern. (Garbow; at least paragraphs [0061] and [0063]).
Regarding Claims 6 and 16; the combination of Gamage and Garbow teach; The computing platform of claim 5, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: compare the likelihood of failure of the matching pattern to a failure threshold, wherein sending the one or more preemptive resolution commands causing modification of the operations at the system to prevent the predicted failure is in response to identifying that the likelihood of failure of the matching pattern meets or exceeds the failure threshold. (Gamage; at least paragraphs [0049] and [0068]).
Regarding Claims 7 and 17; the combination of Gamage and Garbow teach; The computing platform of claim 1, wherein sending the one or more preemptive resolution commands comprises directing a load management server associated with the system to redirect incoming requests away from the system. (Gamage; at least paragraph [0066]).
Regarding Claims 8 and 18; the combination of Gamage and Garbow teach; The computing platform of claim 1, wherein sending the one or more preemptive resolution commands comprises directing a user device to display a recommended solution to avoid the predicted failure along with a prompt for whether or not the recommended solution should be executed. (Garbow; at least paragraph [0066]).
Regarding Claims 9 and 19; the combination of Gamage and Garbow teach; The computing platform of claim 8, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: receive user input accepting the recommended solution; and execute, in response to receiving the user input, the recommended solution. (Garbow; at least paragraph [0066]).
Regarding Claim 10; the combination of Gamage and Garbow teach; The computing platform of claim 1, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: receive third telemetry data; generate, based on the third telemetry data, a third telemetry state image; compare an updated pattern, corresponding to the initial telemetry state image, the additional telemetry state image, the transition between the initial telemetry state image and the additional telemetry state image, the third telemetry state image, and a transition between the additional telemetry state image and the third telemetry state image, to the telemetry state images and the transitions between the telemetry state images of the rules-based state machine to identify an updated matching pattern; and identify, using the identified updated matching pattern, a new likelihood of failure for the system, wherein the new likelihood of failure is different than the likelihood of failure. (Gamage; at least paragraphs [0057]-[0066]).
Allowable Subject Matter
The office would first like to state that the identified claims have an outstanding 101 rejection that must be resolved prior to consideration of allowance.
Claims 4 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 4 and 14 recite: “The computing platform of claim 1, wherein comparing the pattern to the telemetry state images and the transitions between the telemetry state images of the rules-based state machine to identify the matching pattern comprises:
using an image matching model to: identify a match between the initial telemetry state image and a first image of the telemetry state images, and identify a match between the additional telemetry state images and a second image of the telemetry state images, wherein the second image of the telemetry state images is linked to the first image of the telemetry state images within the rules-based state machine, wherein a transition between the initial telemetry state image and the additional telemetry state image matches a transition between the first image and the second image.”
The closest prior art of record is Gamage et al. (US PGPUB 20160292028) and Garbow (US PGPUB 20070101202). Gamage discloses an event pattern correlation system and method which tracks in real-time, a sequence of events creating an event pattern. This event pattern is then compared to a plurality of previously identified event sequences that resulted in failure, and if the event pattern matches one of the stored sequences, initiating corrective actions. Garbow discloses a pattern matching system and method for identifying fault conditions in a server system. The method includes grouping data into previously identified clusters and determining a probability that a failure could be pending based upon where the data patter is being grouped. However, neither Gamage nor Garbow teach, “using an image matching model to: identify a match between the initial telemetry state image and a first image of the telemetry state images, and identify a match between the additional telemetry state images and a second image of the telemetry state images, wherein the second image of the telemetry state images is linked to the first image of the telemetry state images within the rules-based state machine, wherein a transition between the initial telemetry state image and the additional telemetry state image matches a transition between the first image and the second image.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nucci et al. (US PGPUB 20190097873): disclose a system and method for collecting a log of events, comparing the logs to known patterns of normal events, and detecting malicious activity based on a discrepancy.
Spencer et al. (US PGPUB 20200151042): disclose a system and method for collecting and providing a classification score of a plurality of determined errors that can be used in pattern matching during subsequent periods.
Kumar et al. (US PGPUB 20150067410): disclose a system and method for collecting a plurality of syslog messages relating to system errors and further providing labels to various patterns to identify common errors that can readily be identified based on the label provided.
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/CHRISTOPHER W CARTER/Examiner, Art Unit 2117