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 .
DETAILED ACTION
This non action is in response to RCE filed on 12/23/2025. In this RCE, claims 1, 3, 11-12 and 19-20 are amended. Claims 1 and 3-21 are pending, with claims 1, 11 and 19 being independent.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/23/2025 has been entered.
Priority
This application is a continuation of U.S. Application No. 17/032,799, filed September 25, 2020, the entire contents of which are incorporated herein by reference.
Response to Arguments
References Not Cited on Form 892
The reference is added to the Form 892.
Claim Rejection Under 35 U.S.C. § 103
Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3, 11-12 and 19-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims recite “a resource type mapped to the root cause hypothesis according to the Bayesian model” (emphasis added); however, there is no clear explanation for this subject matter described in the specification. The closest descriptions is paragraph 58 of examined application, the paragraph shows that a root cause hypothesis associated with the resource type of node. In other words, there is no clear explanation for a resource type mapped to the root cause hypothesis according to the Bayesian model.
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 of this title, 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, 3, 11-12 and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Zasadzinski et al. (US 2017/0372212, Pub. Date Dec. 28, 2017), in view of Ngampornsukswadi et al. (US 2018/0054363, Feb. 22, 2018), in view of Natu et al. (NPL: Efficient probe selection algorithms for fault diagnosis, Published online: 16 April 2008), in view of Renders et al. (US 2018/0218264, Pub. Date: Aug. 2, 2018), in view of Arora et al. (US 2020/0073742, Pub. Date: Mar. 5, 2020).
As per claim 1, Zasadzinski discloses a method (Zasadzinski Fig. 2) comprising:
generating a Bayesian model ([Per paragraph 37 of examined application's specification, a Bayesian model may be referred to a probabilistic directed acyclic graphical model]; Zasadzinski Para. [0024], the AC generator 105 generates an AC [Bayesian model] for component type Y 106; Zasadzinski Para. [0014], An arithmetic circuit ("AC") is typically depicted as a directed acyclic graph ... An AC may also be referred to as a probabilistic model; see Zasadzinski Para. [0013], A component model may be encoded as a statistical model, such as a Bayesian network, or as an undirected, possibly cyclic graph, such as a Markov network) based on resource types associated with a plurality of resources in a network and event types associated with a plurality of network events (Zasadzinski Para. [0024], the AC generator 105 generates an AC for component [resource] type Y 106 and an AC for component type Z 107 based on the component models; Zasadzinski Para. [0042], The analyzer may determine the state information by identifying event types corresponding to a particular state or variable);
generating, based on the Bayesian model (Zasadzinski Para. [0028], The diagnosis model generator 110 then determines the types of the anomalous component and the components on which the anomalous component depends. The diagnosis model generator 110 then retrieves the ACs corresponding to the component types for each of the components from the AC database 108) and fault data associated with a fault in the network (Zasadzinski Para. [0027], At stage E, the event collector 115 receives an indication of an anomalous event 117 from a component (not depicted) in the network 101. An anomalous event is an event that indicates a network occurrence or condition that deviates from a normal or expected value or outcome), a plurality of root cause hypotheses for the fault (Zasadzinski Para. [0033], At stage I, the component instance identifier 125 identifies an instance of a component(s) suspected as causing the anomalous event 117 based on output of the arithmetic circuit evaluator 120 … At stage J, the component instance identifier 125 outputs a hypothesis 130 for the anomalous event 117. The hypothesis 130 includes identifiers for instances of the component(s) identified at stage I), wherein each root cause hypothesis of the plurality of root cause hypotheses is associated with a resource type of the resource types (Zasadzinski Para. [0033], At stage I, the component instance identifier 125 identifies an instance of a component(s) suspected as causing the anomalous event 117 based on output of the arithmetic circuit evaluator 120 … At stage J, the component instance identifier 125 outputs a hypothesis 130 for the anomalous event 117. The hypothesis 130 includes identifiers for instances of the component(s) identified at stage I; Zasadzinski Para. [0062], the identifier may be “PC01” from which the analyzer may determine that the component type is “PC.”);
a resource type associated to the root cause hypothesis (Zasadzinski Para. [0033], At stage I, the component instance identifier 125 identifies an instance of a component(s) suspected as causing the anomalous event 117 based on output of the arithmetic circuit evaluator 120 … At stage J, the component instance identifier 125 outputs a hypothesis 130 for the anomalous event 117. The hypothesis 130 includes identifiers for instances of the component(s) identified at stage I; Zasadzinski Para. [0062], the identifier may be “PC01” from which the analyzer may determine that the component type is “PC.”) according to the Bayesian model ([Per paragraph 37 of examined application's specification, a Bayesian model may be referred to a probabilistic directed acyclic graphical model]; Zasadzinski fig. 1 and Para. [0024], the AC generator 105 generates an AC [Bayesian model] for component type Y 106; Zasadzinski Para. [0014], An arithmetic circuit ("AC") is typically depicted as a directed acyclic graph ... An AC may also be referred to as a probabilistic model; Zasadzinski Para. [0028], The diagnosis model generator 110 then determines the types of the anomalous component and the components on which the anomalous component depends. The diagnosis model generator 110 then retrieves the ACs corresponding to the component types for each of the components from the AC database 108).
Zasadzinski does not explicitly disclose:
wherein each of the plurality of root cause hypotheses has an associated probability;
in response to disproving a root cause hypothesis of the plurality of root cause hypotheses based on probing a resource type mapped to the root cause hypothesis according to the Bayesian model, removing the root cause hypothesis from the plurality of root cause hypotheses to form an updated plurality of root cause hypotheses;
adjusting the probabilities associated with the updated plurality of root cause hypotheses based on the probability of the root cause hypothesis that was removed;
ordering the updated plurality of root cause hypotheses based on the adjusted probabilities associated with the updated plurality of root cause hypotheses to form an ordered plurality of root cause hypotheses;
selecting one or more root causes from the ordered plurality of root cause hypotheses; and
automatically performing one or more remedial actions associated with the selected one or more root causes.
Ngampornsukswadi teaches:
a resource type mapped to the root cause hypothesis (Ngampornsukswadi para. [0070-0071], processors map the system problem to a system performance alert which was triggered or received within a mapping time threshold (e.g. 10 hours) as a potential root cause … multiple delays in accessing a client record within a short period of time may be indicative of a system problem which the processors may map to a latency problem at a network router).
it would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Zasadzinski in view of Ngampornsukswadi for a resource type mapped to the root cause hypothesis according to the Bayesian model.
One of ordinary skill in the art would have been motived because it offers the advantage of rectifying the potential root cause (Ngampornsukswadi para. [0073].
Zasadzinski-Ngampornsukswadi does not explicitly disclose:
wherein each of the plurality of root cause hypotheses has an associated probability;
in response to disproving a root cause hypothesis of the plurality of root cause hypotheses based on probing a resource type, removing the root cause hypothesis from the plurality of root cause hypotheses to form an updated plurality of root cause hypotheses;
adjusting the probabilities associated with the updated plurality of root cause hypotheses based on the probability of the root cause hypothesis that was removed;
ordering the updated plurality of root cause hypotheses based on the adjusted probabilities associated with the updated plurality of root cause hypotheses to form an ordered plurality of root cause hypotheses;
selecting one or more root causes from the ordered plurality of root cause hypotheses; and
automatically performing one or more remedial actions associated with the selected one or more root causes.
Natu teaches:
in response to disproving a root cause hypothesis of the plurality of root cause hypotheses based on probing a resource associated to the root cause hypothesis (Natu pg. 117, The probes for fault localization need to be sent such that the health of all the nodes on the failed probe paths can be determined …The set of suspected nodes contains the nodes whose health needs to be determined. This suspected node set is initialized to all nodes that are present on the failed probe paths … The nodes lying on the paths of successful probes are added to the set of passed nodes and removed from the set of suspected nodes … In each iteration, the algorithm builds a probe set to be sent over the network to determine the health of the remaining suspected nodes; Natu fig. 2 and pg. 111, Once a failure is detected in a network, the Fault Localization component analyzes probe results, and selects additional probes that provide maximum information about the suspected area of the network. This process of probe analysis and selection is performed to localize the exact cause of failure; Natu pg. 115, the outcome of a set of r probes results in a binary string of length r with each digit indicating the success or failure of a probe), removing the root cause hypothesis from the plurality of root cause hypotheses to form an updated plurality of root cause hypotheses (Natu pg. 109, a probe could be a ping to collect information about connectivity of nodes; Natu pg. 117, Failure of probe 1→8 brings nodes 4, 5, 6, and 8 into the suspected node set. On observing success of probe 7 → 4, nodes 4 and 5 are removed from the set of suspected nodes and put into the set of passed nodes … In each iteration, the algorithm builds a probe set to be sent over the network to determine the health of the remaining suspected nodes; Natu pg. 115, the outcome of a set of r probes results in a binary string of length r with each digit indicating the success or failure of a probe).
Note: Zasadzinski-Ngampornsukswadi teaches a component type suspected as causing the anomalous event and mapped to the root cause hypothesis (Zasadzinski para. [33, 62] & Ngampornsukswadi para. [0070-0071]). However, Zasadzinski does not explicitly disclose disproving a root cause hypothesis of the plurality of root cause hypotheses based on probing a resource type mapped to the root cause hypothesis according to the Bayesian model. Natu teaches disproving suspected nodes based on probing a suspected node (Natu pg. 111, 117).
Therefore, it would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Natu in order to incorporate probing technique of Natu for removing the root cause hypothesis from the plurality of root cause hypotheses to form an updated plurality of root cause hypotheses in response to disproving a root cause hypothesis of the plurality of root cause hypotheses based on probing a resource type mapped to the root cause hypothesis according to the Bayesian model.
One of ordinary skill in the art would have been motived because it offers the advantage of localizing the exact cause of failure (Natu pg. 111).
Zasadzinski-Ngampornsukswadi-Natu does not explicitly disclose
wherein each of the plurality of root cause hypotheses has an associated probability;
adjusting the probabilities associated with the updated plurality of root cause hypotheses based on the probability of the root cause hypothesis that was removed;
ordering the updated plurality of root cause hypotheses based on the adjusted probabilities associated with the updated plurality of root cause hypotheses to form an ordered plurality of root cause hypotheses;
selecting one or more root causes from the ordered plurality of root cause hypotheses; and
automatically performing one or more remedial actions associated with the selected one or more root causes.
Render teaches:
each of the plurality of root cause hypotheses has an associated probability (Renders Para. [0027], Each component (possible root cause) is sampled independently so that, with the Naïve Bayes assumption, the most probable hypotheses (that is, having highest conditional probability p(hly) of hypothesis h conditioned on the root cause y) are generated. This mechanism automatically generates a ranked list of most probable hypotheses for each root cause; Render Para. [0017], the generating or updating is performed by adding hypotheses such that the ranked list for each root cause is ranked according to conditional probabilities of the hypotheses conditioned on the root cause);
adjusting the probabilities associated with the updated plurality of root cause hypotheses (Renders Fig. 1, Remove inconsistent hypotheses and recompute [adjust] probabilities of remaining hypotheses at 28 and Para. [0031], After selecting and performing the next test, an update process 28 removes from the ranked list and from the Pareto frontier any hypotheses which are inconsistent with the test result and further sampling starting (or generating) from the Pareto frontier may be performed to ensure that the remaining hypotheses cover at least the total probability mass (1-ri)) based on the probability of the root cause hypothesis that was removed (Render Para. [0016], selecting a test of the unperformed tests based on the merged ranked lists and generating or receiving a test result for the selected test, updating the set of unperformed tests U by removing the selected test, and removing from the ranked lists of hypotheses for the m root causes those hypotheses that are inconsistent with the test result of the selected test);
ordering the updated plurality of root cause hypotheses based on the adjusted probabilities associated with the updated plurality of root cause hypotheses to form an ordered plurality of root cause hypotheses (Renders Fig. 1-2, Remove inconsistent hypotheses and recompute probabilities of remaining hypotheses at 28 and Ranked list for state ym at 24; Renders Para. [0027], Each component (possible root cause) is sampled independently so that, with the Naïve Bayes assumption, the most probable hypotheses (that is, having highest conditional probability p(hly) of hypothesis h conditioned on the root cause y) are generated. This mechanism automatically generates a ranked list of most probable hypotheses for each root cause).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Renders for each of the plurality of root cause hypotheses has an associated probability; adjusting the probabilities associated with the updated plurality of root cause hypotheses based on the probability of the root cause hypothesis that was removed; and ordering the updated plurality of root cause hypotheses based on the adjusted probabilities associated with the updated plurality of root cause hypotheses to form an ordered plurality of root cause hypotheses.
One of ordinary skill in the art would have been motived because it offers the advantage of allowing user to focus on the most probable hypothesis for troubleshooting.
Zasadzinski-Ngampornsukswadi-Natu-Render does not explicitly disclose:
selecting one or more root causes from the ordered plurality of root cause hypotheses; and
automatically performing one or more remedial actions associated with the selected one or more root causes.
Arora teaches:
selecting one or more root causes from the ordered plurality of root cause hypotheses (Arora para. [0023], selecting the particular cause based at on the sequence of probabilities for the particular cause and the sequence of probabilities for each other potential cause can include selecting the particular cause in response to detecting an increase in the probabilities for the particular cause during the particular time period; Arora para. [0112], the computer system 140 can identify, as the most likely cause, a potential cause that maintains the highest probability amongst the various potential causes for at least a threshold duration of time); and
automatically performing one or more remedial actions associated with the selected one or more root causes (Arora para. [0117-0118], In step 510, the computer system 140 provides an indication of the most likely cause and/or the selected action. For example, the computer system 140 can generate and provide a user interface that presents the most likely cause (and optionally other causes, such as the top n potential causes) and/or the selected action (and optionally other potential actions, such as the top n actions that could resolve the condition) … the computer system 140 can initiate the selected action automatically; Arora para. [0120], After the selected action is performed, the computer system 140 can determine whether the selected action resolved the condition (e.g., corrected a problem with the system)).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Arora for selecting one or more root causes from the ordered plurality of root cause hypotheses; and automatically performing one or more remedial actions associated with the selected one or more root causes.
One of ordinary skill in the art would have been motived because it offers the advantage of preventing the problem from escalating (Arora para. [0037]).
As per claim 3, Zasadzinski-Ngampornsukswadi-Natu-Render-Arora discloses the method according to claim 1, as set forth above, Zasadzinski-Ngampornsukswadi-Natu also discloses wherein disproving the root cause hypothesis of the plurality of root cause hypotheses comprises:
determining a probe for a resource of the plurality of resources in the network (Natu pg. 117, the algorithm builds a probe set to be sent over the network to determine the health of the remaining suspected nodes), wherein the resource is an instance of the resource type (Zasadzinski Para. [0033], At stage I, the component instance identifier 125 identifies an instance of a component(s) suspected as causing the anomalous event 117 based on output of the arithmetic circuit evaluator 120 … At stage J, the component instance identifier 125 outputs a hypothesis 130 for the anomalous event 117. The hypothesis 130 includes identifiers for instances of the component(s) identified at stage I; Zasadzinski Para. [0062], the identifier may be “PC01” from which the analyzer may determine that the component type is “PC.”) mapped to the root cause hypothesis from the plurality of root cause hypotheses (Ngampornsukswadi para. [0070-0071], processors map the system problem to a system performance alert which was triggered or received within a mapping time threshold (e.g. 10 hours) as a potential root cause … multiple delays in accessing a client record within a short period of time may be indicative of a system problem which the processors may map to a latency problem at a network router);
executing the probe (Natu fig. 2 and pg. 111, Once a failure is detected in a network, the Fault Localization component analyzes probe results, and selects additional probes that provide maximum information about the suspected area of the network. This process of probe analysis and selection is performed to localize the exact cause of failure; Natu pg. 117, the algorithm builds a probe set to be sent over the network to determine the health of the remaining suspected nodes), including issuing one or more networking commands to the resource (Natu pg. 109, a probe could be a ping to collect information about connectivity of nodes; Natu pg. 117, the algorithm builds a probe set to be sent over the network to determine the health of the remaining suspected nodes);
receiving at least one value from the resource based on the one or more networking commands (Natu fig. 2, Fault Localization: sending a probes, receiving probe result and pg. 115, the outcome of a set of r probes results in a binary string of length r with each digit indicating the success or failure of a probe); and
disproving, based on the at least on value, the root cause hypothesis according to one or more conditions specified by the probe for the at least one value (Natu pg. 117, The probes for fault localization need to be sent such that the health of all the nodes on the failed probe paths can be determined …The set of suspected nodes contains the nodes whose health needs to be determined. This suspected node set is initialized to all nodes that are present on the failed probe paths … The nodes lying on the paths of successful probes are added to the set of passed nodes and removed from the set of suspected nodes; Natu fig. 2, Fault Localization: sending a probes, receiving probe result and pg. 115, the outcome of a set of r probes results in a binary string of length r with each digit indicating the success or failure of a probe).
Similar rationale in claim 1 is applied
Per claims 11-12 and 19-20, they do not teach or further define over the limitations in claims 1 and 3 respectively. As such, claims 11-12 and 19-20 are rejected for the same reasons as set forth in claims 1 and 3 respectively.
As per claim 21, Zasadzinski-Ngampornsukswadi-Natu-Render-Arora discloses the method according to claim 1, as set forth above, Arora also discloses wherein the one or more remedial actions associated with the selected one or more root causes comprises outputting the ordered plurality of root cause hypotheses via a user interface (Arora para. [0117], In step 510, the computer system 140 provides an indication of the most likely cause and/or the selected action. For example, the computer system 140 can generate and provide a user interface that presents the most likely cause (and optionally other causes, such as the top n potential causes) and/or the selected action (and optionally other potential actions, such as the top n actions that could resolve the condition).
Similar rationale in claim 1 is applied.
Claims 4-5 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zasadzinski et al. (US 2017/0372212, Pub. Date Dec. 28, 2017), in view of Ngampornsukswadi et al. (US 2018/0054363, Feb. 22, 2018), in view of Natu et al. (NPL: Efficient probe selection algorithms for fault diagnosis, Published online: 16 April 2008), in view of Renders et al. (US 2018/0218264, Pub. Date: Aug. 2, 2018), in view of Arora et al. (US 2020/0073742, Pub. Date: Mar. 5, 2020), in view of Usery et al. (US 7,945,817, Date of Patent May 17, 2011), in view of Connelly et al. (EP 1405187, Date of publication 10.04.2019), in view of Kakani et al. (US 2020/0106660, Pub. Date: Apr. 2, 2020).
As per claim 4, Zasadzinski-Ngampornsukswadi-Natu-Render-Arora discloses the method according to claim 1, as set forth above, Zasadzinski does not explicitly disclose:
determining that configuration data specifies a first waiting time period when the fault data is complete and a second waiting time period when the fault data is not complete; and
in response to determining that the fault data is complete, waiting the first waiting time period and, after the first waiting time period has elapsed, generating the plurality of root cause hypotheses.
Usery teaches:
determining that configuration data specifies a first waiting time period when the fault data is complete ([per paragraph 52 of application's specification, fault data collection may be considered complete if a resource corresponding to the failed node and a predetermined percentage of child resources corresponding to child nodes of the failed node report failures]; Usery fig. 12, More Children to Evaluate? at 1238[Wingdings font/0xE0] NO [Wingdings font/0xE0] More Records to Evaluate? at 1239: NO [Wingdings font/0xE0] Wait and col. 23 lines 45-63, When a network element enters into an alarm state, its siblings are evaluated to determine whether they too are in alarm condition … if five out of twenty five ports are in an alarm condition, and a user-defined rule stipulates that an alarm is to be generated if at least 20% of a parent's children are in alarm status, then an alarm will be generated and associated with the respective parent network element); and
in response to determining that the fault data is complete, waiting the first waiting time period (Usery fig. 12, More Children to Evaluate? at 1238[Wingdings font/0xE0] NO [Wingdings font/0xE0] More Records to Evaluate? at 1239: NO [Wingdings font/0xE0] Wait and col. 23 lines 45-63, When a network element enters into an alarm state, its siblings are evaluated to determine whether they too are in alarm condition … if five out of twenty five ports are in an alarm condition, and a user-defined rule stipulates that an alarm is to be generated if at least 20% of a parent's children are in alarm status, then an alarm will be generated and associated with the respective parent network element)
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Usery for determining that configuration data specifies a first waiting time period when the fault data is complete; and in response to determining that the fault data is complete, waiting the first waiting time period.
One of ordinary skill in the art would have been motived because it offers the advantage of helping determine the root cause (Usery col. 4 line 25).
Zasadzinski-Usery does not explicitly disclose:
a second waiting time period when the fault data is not complete; and
after the first waiting time period has elapsed, generating the plurality of root cause hypotheses.
Connelly teaches:
after the first waiting time period has elapsed, generating the plurality of root cause hypotheses (Connelly Para. [0017], after a sufficient period of time has elapsed, determines the root cause of related events; Connelly Para. [0012], When an event arrives at a root cause determiner, a timer can be initialized that determines a period of time during which related events will be collected. Once the period of time has expired, then a root cause determination can be made based on the set of collected events and correlation rules affected by such events).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Connelly for after the first waiting time period has elapsed, generating the plurality of root cause hypotheses.
One of ordinary skill in the art would have been motived because it offers the advantage of collecting sufficient number of events for analysis (see Connelly Para. [0017]).
Zasadzinski-Usery-Connelly does not explicitly disclose:
a second waiting time period when the fault data is not complete.
Kakani teaches:
a waiting time period when the fault data is not complete (Kakani fig. 5, Do Any Event Correlations Satisfy A Statistical Threshold? At 508 [Wingdings font/0xE0] No [the fault data is not complete] [Wingdings font/0xE0] Wait For Additional Time Period and para. [0037], The system determines whether any event correlations satisfy a statistical threshold (508). The system may compare values representing a probability of the statistical correlations to one or more thresholds to determine whether any of the correlations have a satisfactory statistical power or confidence … the system can determine whether the event sequence has occurred a threshold number of times or a sufficient number of times to satisfy a statistical probability that the sequence is not a random occurrence and represents correlated events; Kakani para. [0038], the system waits until a subsequent time period has elapsed and retrieves events for that time period; Kakani para. [0014], an event may be that a file was added to a file system, that a number of users of an application exceeds a threshold number of users, that an amount of available memory falls below a memory amount threshold, or that a component stopped responding or failed).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Kakani for a second waiting time period when the fault data is not complete.
One of ordinary skill in the art would have been motived because it offers the advantage of collecting correlated events to detect root cause (see Kakani para. Fig. 5 and [0037]).
As per claim 5, Zasadzinski-Ngampornsukswadi-Natu-Render-Arora-Usery-Connelly-Kakani discloses the method according to claim 4, as set forth above, Zasadzinski does not explicitly disclose wherein determining that the fault data is complete comprises determining that a threshold percentage of child resources have provided fault information, wherein the child resources correspond to child nodes of a resource node in the resource dependency model and the resource node corresponds to a resource that provided the fault data.
Usery teaches:
determining that the fault data is complete comprises determining that a threshold percentage of child resources have provided fault information (see Usery col. 23 lines 45-63, When a network element enters into an alarm state, its siblings are evaluated to determine whether they too are in alarm condition, if five out of twenty five ports are in an alarm condition, and a user-defined rule stipulates that an alarm is to be generated if at least 20% of a parent's children are in alarm status, then an alarm will be generated and associated with the respective parent network), wherein the child resources correspond to child nodes of a resource node in the resource dependency model (see Usery col. 24 lines 46-49, Patterning engine 525 can determine the total number of a parent's child components and identify each of the child components by referencing a topological database) and the resource node corresponds to a resource that provided the fault data (Usery col. 24 lines 41-43, The network component [resource node] that entered into alarm status in this example is port C 1326. Its parent was identified as card C 1318 at step 1220).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Usery for determining that the fault data is complete comprises determining that a threshold percentage of child resources have provided fault information, wherein the child resources correspond to child nodes of a resource node in the resource dependency model and the resource node corresponds to a resource that provided the fault data.
One of ordinary skill in the art would have been motived because it offers the advantage of helping determine the root cause (Usery col. 4 line 25).
Per claims 13-14, they do not teach or further define over the limitations in claims 4-5 respectively. As such, claims 13-14 are rejected for the same reasons as set forth in claim 4-5 respectively.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zasadzinski et al. (US 2017/0372212, Pub. Date Dec. 28, 2017), in view of Ngampornsukswadi et al. (US 2018/0054363, Feb. 22, 2018), in view of Natu et al. (NPL: Efficient probe selection algorithms for fault diagnosis, Published online: 16 April 2008), in view of Renders et al. (US 2018/0218264, Pub. Date: Aug. 2, 2018), in view of Arora et al. (US 2020/0073742, Pub. Date: Mar. 5, 2020), in view of Usery et al. (US 7,945,817, Date of Patent May 17, 2011), in view of Kakani et al. (US 2020/0106660, Pub. Date: Apr. 2, 2020).
As per claim 6, Zasadzinski-Ngampornsukswadi-Natu-Render-Arora discloses the method according to claim 1, as set forth above, Zasadzinski does not explicitly disclose further comprising:
determining that configuration data specifies a first waiting time period when the fault data is complete and a second waiting time period when the fault data is not complete; and
in response to determining that the fault data is not complete, waiting the second waiting time period and, after the second waiting time period longer has elapsed, generating the plurality of root cause hypotheses.
Usery teaches:
determining that configuration data specifies a first waiting time period when the fault data is complete ([per paragraph 52 of application's specification, fault data collection may be considered complete if a resource corresponding to the failed node and a predetermined percentage of child resources corresponding to child nodes of the failed node report failures]; Usery fig. 12, More Children to Evaluate? at 1238[Wingdings font/0xE0] NO [Wingdings font/0xE0] More Records to Evaluate? at 1239: NO [Wingdings font/0xE0] Wait and col. 23 lines 45-63, When a network element enters into an alarm state, its siblings are evaluated to determine whether they too are in alarm condition … if five out of twenty five ports are in an alarm condition, and a user-defined rule stipulates that an alarm is to be generated if at least 20% of a parent's children are in alarm status, then an alarm will be generated and associated with the respective parent network element).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Usery for determining that configuration data specifies when the fault data is complete and a when the fault data is not complete.
One of ordinary skill in the art would have been motived because it offers the advantage of helping determine the root cause (Usery col. 4 line 25).
Zasadzinski-Usery does not explicitly disclose:
a second waiting time period when the fault data is not complete; and
in response to determining that the fault data is not complete, waiting the second waiting time period and, after the second waiting time period longer has elapsed, generating the plurality of root cause hypotheses.
Kakani teaches:
a waiting time period when the fault data is not complete (Kakani fig. 5, Do Any Event Correlations Satisfy A Statistical Threshold? At 508 [Wingdings font/0xE0] No [the fault data is not complete] [Wingdings font/0xE0] Wait For Additional Time Period and para. [0037], The system determines whether any event correlations satisfy a statistical threshold (508). The system may compare values representing a probability of the statistical correlations to one or more thresholds to determine whether any of the correlations have a satisfactory statistical power or confidence … the system can determine whether the event sequence has occurred a threshold number of times or a sufficient number of times to satisfy a statistical probability that the sequence is not a random occurrence and represents correlated events; Kakani para. [0038], the system waits until a subsequent time period has elapsed and retrieves events for that time period; Kakani para. [0014], an event may be that a file was added to a file system, that a number of users of an application exceeds a threshold number of users, that an amount of available memory falls below a memory amount threshold, or that a component stopped responding or failed);
in response to determining that the fault data is not complete, waiting the waiting time period and (Kakani fig. 5, Do Any Event Correlations Satisfy A Statistical Threshold? at 508 [Wingdings font/0xE0] No [the fault data is not complete] [Wingdings font/0xE0] Wait For Additional Time Period), after the waiting time period longer has elapsed (Kakani para. [0038], the system waits until a subsequent time period has elapsed and retrieves events for that time period), generating the plurality of root cause hypotheses (Kakani fig. 5, Wait For Additional Time Period at 508 [Wingdings font/0xE0] Identify A Root Cause Of The Anomalous Event Based On Events Within The Service Domain at 520).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Kakani for a second waiting time period when the fault data is not complete; and in response to determining that the fault data is not complete, waiting the second waiting time period and, after the second waiting time period longer has elapsed, generating the plurality of root cause hypotheses.
One of ordinary skill in the art would have been motived because it offers the advantage of collecting correlated events to detect root cause (see Kakani para. Fig. 5 and [0037]).
Per claim 15, it does not teach or further define over the limitations in claim 6. As such, claim 15 is rejected for the same reasons as set forth in claim 6.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zasadzinski et al. (US 2017/0372212, Pub. Date Dec. 28, 2017), in view of Ngampornsukswadi et al. (US 2018/0054363, Feb. 22, 2018), in view of Natu et al. (NPL: Efficient probe selection algorithms for fault diagnosis, Published online: 16 April 2008), in view of Renders et al. (US 2018/0218264, Pub. Date: Aug. 2, 2018), in view of Arora et al. (US 2020/0073742, Pub. Date: Mar. 5, 2020), in view of Rother (US 2005/0137762, Pub. Date Jun. 23, 2005).
As per claim 7, Zasadzinski-Ngampornsukswadi-Natu-Render-Arora discloses the method according to claim 1, as set forth above, Zasadzinski also discloses each node corresponding to root cause hypothesis (Zasadzinski Para. [0033], the component instance identifier 125 identifies an instance of a component(s) suspected as causing the anomalous event 117 based on output of the arithmetic circuit evaluator 120 … The hypothesis 130 includes identifiers for instances of the component(s) identified at stage I).
Zasadzinski does not explicitly disclose:
receiving a confirmation of a root cause hypothesis of the plurality of root cause hypotheses; and
increasing a probability associated with the confirmed root cause hypothesis.
Rother teaches:
receiving a confirmation of a root cause hypothesis of the plurality of root cause hypotheses (see Rother Para. [0039], from a displayed list, the user selects [confirms] a particular cause is found to be possible for more than one of the selected symptoms, the probability of that cause being the root problem will increase); and
increasing a probability associated with the confirmed root cause hypothesis (Rother Para. [0039], the user selects more than one symptom and a particular cause is found to be possible for more than one of the selected symptoms, the probability of that cause being the root problem will increase).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Rother for receiving a confirmation of a root cause hypothesis of the plurality of root cause hypotheses; and increasing a probability associated with each node corresponding to the confirmed root cause hypothesis.
One of ordinary skill in the art would have been motived because it offers the advantage of providing recommendation for the associated test (Rother Para. [0039]).
Per claim 16, it does not teach or further define over the limitations in claim 7. As such, claim 16 is rejected for the same reasons as set forth in claim 7.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zasadzinski et al. (US 2017/0372212, Pub. Date Dec. 28, 2017), in view of Ngampornsukswadi et al. (US 2018/0054363, Feb. 22, 2018), in view of Natu et al. (NPL: Efficient probe selection algorithms for fault diagnosis, Published online: 16 April 2008), in view of Renders et al. (US 2018/0218264, Pub. Date: Aug. 2, 2018), in view of Arora et al. (US 2020/0073742, Pub. Date: Mar. 5, 2020), in view of Maiti et al. (US 2019/0266253, filed Feb. 27, 2018), in view of Rother (US 2005/0137762, Pub. Date Jun. 23, 2005).
As per claim 8, Zasadzinski-Ngampornsukswadi-Natu-Render-Arora discloses the method according to claim 1, as set forth above, Render also discloses further comprising:
adding the root cause hypothesis to the Bayesian network (Renders fig. 2, Add zero, one or two new hypotheses generated by adjusting configuration at 44 and Renders Para. [0027], Each component (possible root cause) is sampled independently so that, with the Naïve Bayes assumption, the most probable hypotheses (that is, having highest conditional probability p(hly) of hypothesis h conditioned on the root cause y) are generated. This mechanism automatically generates a ranked list of most probable hypotheses for each root cause).
Similar rationale in claim 1 is applied.
Zasadzinski does not explicitly disclose:
receiving a user-generated root cause hypothesis of the plurality of root cause hypotheses;
receiving an indication of a probe associated with the user-generated root cause hypothesis; and
adding the user-generated root cause hypothesis to the Bayesian network.
Maiti teaches:
receiving a user-generated root cause hypothesis of the plurality of root cause hypotheses (Maiti Para. [0120], The leamability box 730 allows the user to enter possible causes manually. For example, in some embodiments, if the user is aware of the cause of the problem (e.g., latency) impacting the component (e.g., the virtual machine, VM1), the user may simply enter that cause into an interface provided within the leamability box 730. By entering the cause manually, the troubleshooting back-end system 310 may update the cause determination graph to include the cause entered by the user as a possible cause for future reference).
Note: Render teaching adding root cause hypothesis to the Bayesian network (Renders Fig. 2 and Para. [0027]). However, Render does not teach user-generated hypothesis. Maiti teaches user-generated hypothesis (Maiti Para. [120]).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Maiti for receiving a user-generated root cause hypothesis of the plurality of root cause hypotheses; and adding the user-generated root cause hypothesis to the Bayesian network.
One of ordinary skill in the art would have been motived because it offers the advantage of providing possible causes for future reference (see Maiti Para. [0120]).
Zasadzinski-Maiti does not explicitly disclose:
receiving an indication of a probe associated with the user-generated root cause hypothesis.
Rother teaches:
receiving an indication of a probe associated with the root cause hypothesis (Rother Para. [0040], Once the symptom or symptoms have been selected and the associated recommended test procedures displayed, the user can then select one of the displayed test procedures, and the system will then launch or initiate that procedure; see Rother Para. [0027-0028], receiving test procedures [probe] to be performed to check for those causes; Rother Para. [0026], The test procedures are listed in the order of the probability or likelihood that the test will be successful in diagnosing the cause of the selected symptom or symptoms, this ranking being displayed as at 43).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Rother for receiving an indication of a probe associated with the user-generated root cause hypothesis.
One of ordinary skill in the art would have been motived because it offers the advantage of analyzing and providing recommendation for the associated test (see Rother Para. [0039]).
Per claim 17, it does not teach or further define over the limitations in claim 8. As such, claim 17 is rejected for the same reasons as set forth in claim 8.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Zasadzinski et al. (US 2017/0372212, Pub. Date Dec. 28, 2017), in view of Ngampornsukswadi et al. (US 2018/0054363, Feb. 22, 2018), in view of Natu et al. (NPL: Efficient probe selection algorithms for fault diagnosis, Published online: 16 April 2008), in view of Renders et al. (US 2018/0218264, Pub. Date: Aug. 2, 2018), in view of Arora et al. (US 2020/0073742, Pub. Date: Mar. 5, 2020), in view of Maiti et al. (US 2019/0266253, filed Feb. 27, 2018), in view of Rother (US 2005/0137762, Pub. Date Jun. 23, 2005), in view of Gao et al. (US 2019/0230003, Pub. Date Jul. 25, 2019).
As per claim 9, Zasadzinski-Ngampornsukswadi-Natu-Render-Arora-Maiti-Rother discloses the method according to claim 8, as set forth above, Zasadzinski does not explicitly disclose wherein the probe comprises a new probe, and wherein the method further comprises receiving a mapping of resource properties of a resource node to inputs of the new probe.
Gao teaches:
the probe comprises a new probe (Gao Para. [0126], FIG. 3 shows an exemplary Executable Procedure 300 including a Process Node 301, which further includes one or more Probes (Probe1 303, Probe2 302, etc.)), and wherein the method further comprises receiving a mapping of resource properties of a resource node to inputs of the new probe (see Gao Fig.15, method to define a Ping Probe; Gao Para. [0177], To define a Ping Probe, a user needs to define a source 1510 (the device to ping from) and a destination 1520 (the IP [resource properties] to ping to)).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Gao for wherein the probe comprises a new probe, and wherein the method further comprises receiving a mapping of resource properties of a resource node to inputs of the new probe.
One of ordinary skill in the art would have been motived because it offers the advantage of checking the connectivity between devices (Gao Para. [0119]).
Claims 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zasadzinski et al. (US 2017/0372212, Pub. Date Dec. 28, 2017), in view of Ngampornsukswadi et al. (US 2018/0054363, Feb. 22, 2018), in view of Natu et al. (NPL: Efficient probe selection algorithms for fault diagnosis, Published online: 16 April 2008), in view of Renders et al. (US 2018/0218264, Pub. Date: Aug. 2, 2018), in view of Arora et al. (US 2020/0073742, Pub. Date: Mar. 5, 2020), in view of Yaghi et al. (US 2015/0003595, Pub. Date Jan. 1, 2015).
As per claim 10, Zasadzinski-Ngampornsukswadi-Natu-Render-Arora discloses the method according to claim 1, as set forth above, Zasadzinski does not explicitly disclose further comprising initializing the probability associated with each node of the Bayesian model to an equal probability.
Yaghi teaches:
initializing the probability associated with each node of the Bayesian model to an equal probability (Yaghi Para. [0605], A Bayesian process which assigns equal probability and weight to each training component at the start of the training program).
It would been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to further modify Zasadzinski in view of Yaghi for initializing a probability associated with each node of the Bayesian model to an equal probability.
One of ordinary skill in the art would have been motived because it offers the advantage of utilizing a multitude of statistical methodologies to identify root cause.
Per claim 18, it does not teach or further define over the limitations in claim 10. As such, claim 18 is rejected for the same reasons as set forth in claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yuan et al. (US 7761765) Automated Root Cause Identification Of Logic Controller Failure;
Uthe (US 20050278273) System And Method For Using Root Cause Analysis To Generate A Representation Of Resource Dependencies;
Barkai et al. (US 6941362) Root Cause Analysis In A Distributed Network Management Architecture.
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/VINH NGUYEN/Examiner, Art Unit 2453
/KAMAL B DIVECHA/Supervisory Patent Examiner, Art Unit 2453
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