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 .
Priority
The Instant application, filed 11/22/2022, is a National Stage entry of PCT/JP2020/022008 with an International filing date of 06/03/2020.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in
public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 5 and 7 are rejected on the ground of nonstatutory double patenting as being
unpatentable over claims 1, 5 and 6 of U.S. Patent No. US11973658B2 in view of Cinato US20090292948A1 and Kushnir US20160162346A1.
Claim 1 of Matsuo ‘658 teaches all of the limitations of the instant claim 1, except “divide the received pieces of observed data into a plurality of clusters according to types of information represented by the respective pieces of observed data; determine, for each location or each cause of an abnormality, a representative value as representative observed data for each of the plurality of clusters; construct casual model based on rule-based method.” Matsuo ‘658 in view of Cinato and Kushnir teaches divide the received pieces of observed data into a plurality of clusters according to types of information represented by the respective pieces of observed data (See Cinato, [0105], “… each Autonomous Agent groups the collected alarms or status changes based on information about the network apparatus that has sent the alarm or changed the status, the type of fault, and the time when the alarm was generated or the status change was notified.”); determine, for each location or each cause of an abnormality, a representative value as representative observed data for each of the plurality of clusters (See Cinato, ([0067] for each individual possible fault in the accountable network resource, in the contiguous network resource and in the interconnection resource, determining a first probability that a status information is generated when the individual fault occurs (effect/cause probability); [0068] for each status information that may be generated by the network resource managed by the agent, determining a second probability that the status information is generated when no fault occurs in the network resource managed by the agent (effect/no_cause probability). See also Kushnir, ([0027] Each cluster may be modeled by a representative root cause vector that may be derived from the ranking of all the vector members in that cluster … The average of all of the vectors in the cluster may be used to determine a representative KPI ranking that may indicate what the top leading KPI root causes for a type of KQI degradation within the cluster. Other operations may also be used to determine a representative root cause vector of a cluster. See also [0025]); construct casual model based on rule-based method (See Cinato, ([0061] constructing by the accountable agent a partial probabilistic model relating status information received by the accountable agent and faults occurred in the accountable network resource, in the contiguous network resource and in the interconnection resource. [0175] The procedure then terminates and the Accountable Agent constructs its own partial Bayesian Network shown in FIG. 5a based on the Useful Probability Group so formed. [0143] knowledge of equipment and topology concerning the resources involved by effects, the resources of network apparatuses interconnected via the resource involved by effects and the interconnection resources concerned. [0157] The existence of an effect/cause conditional probability implies a dependency between the CauseResource-Cause pair and the EffectResource-Effect pair in question. [0158] Based on the information contained in the effect/cause conditional probabilities and in the effect/no_cause conditional probabilities contained in the Useful Probability Group …). It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Matsuo ‘658 to incorporate the teachings of Cinato and Kushnir to apply clustering derived representative data generation and rule-based dependency modeling in order to improve the organization and reliability of the data used for fault estimation, thereby reducing noise and dimensionality while enabling structure casual relationships to be incorporated.
“A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). “ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Claims 5 and 7 correspond respectively to claims 5 and 6 of U.S. Patent No. US 11,973,658 and recite substantially the same inventive concepts as discussed with respect to claim 1. Therefore, Claims 5 and 7 are rejected for the same reasons discussed above.
Claim 4 is rejected on the ground of nonstatutory double patenting as being unpatentable over
claim 4 of U.S. Patent No. US11973658B2 in view of Cinato US20090292948A1.
Claim 4 of Matsuo ‘658 teaches all of the limitations of the instant claim 1, except “store, into the memory, a causal model for estimating the location or the cause of the abnormality, the causal model including a first causal model constructed based on a rule-based method, a second causal model constructed based on a data- driven method, and a third causal model combining the first causal model and the second causal model; estimate a location or a cause of an abnormality based on one of the first causal model, the second causal model, or the third causal model stored in the memory ” Matsuo ‘658 in view of Cinato teaches store, into the memory, a causal model for estimating the location or the cause of the abnormality, the causal model including a first causal model constructed based on a rule-based method, a second causal model constructed based on a data- driven method, and a third causal model combining the first causal model and the second causal model, and estimate based on one of models (See Cinato, Fig. 5a-5c and 6a-6b; [0168] In particular, FIGS. 5a, 5b and 5c show, respectively, examples of a partial Bayesian Network constructed by the Accountable Agent, of a partial Bayesian Network constructed by the Contiguous Agent, and of an Inference Bayesian Network constructed by the Accountable Agent by combining (merging) the two partial Bayesian Networks shown in FIGS. 5a and 5b. [0180] … FIGS. 6a and 6b and 6c how the inference process may be performed on the Inference Bayesian Network shown in FIG. 5c. [0139] Information concerning the above probabilities are stored in database 5 in FIG. 1, hereinafter referred to as Probability Database (PD), which can be maintained on an Application Agent and managed in a centralized manner or on the Autonomous Agents and managed in a distributed manner. [0143] knowledge of equipment and topology concerning the resources involved by effects, the resources of network apparatuses interconnected via the resource involved by effects and the interconnection resources concerned. [0157] The existence of an effect/cause conditional probability implies a dependency between the CauseResource-Cause pair and the EffectResource-Effect pair in question. [0158] Based on the information contained in the effect/cause conditional probabilities and in the effect/no_cause conditional probabilities contained in the Useful Probability Group, a Bayesian Network Probability Table for each effect state of the Bayesian Network is computed … [0189] effects/cause conditional probabilities are updated using statistics of the cases of alarms, events, polled statuses and test results received in association with a single fault detected in the field; [0190] effect/no_cause conditional probabilities are updated using the statistics of operation cases in which, after receiving alarms, events, polled statuses and test results, no fault has been found in the field. [0192] Learning is achieved by using statistical information relating to the CauseResource and EffectResource pair, …). It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Matsuo ‘658 to incorporate the teachings of Cinato to employ multiple casual modeling approaches and probabilistic inference structure for fault estimation in order to enable selection or use of an appropriate inference model based on available operation data and system condition, thereby providing flexibility and robustness of abnormality estimation by allowing different causal modeling strategies to be applied within the same diagnostic framework while relying on Bayesian inference techniques.
“A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). “ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Instant Application
U.S. Patent No. US 11,973,658
1. A model construction apparatus comprising:
a processor; and a memory storing program instructions that cause the processor to:
receive pieces of observed data from a communication network system that is a target for estimation of a location or a cause of an abnormality;
construct, using the representative observed data, a first causal model for estimating the location or the cause of the abnormality from the pieces of observed data
4. An estimation apparatus comprising:
a processor; and
a memory storing program instructions that cause the processor to:
receive pieces of observed data from a communication network system that is a target for estimation of a location or a cause of an abnormality;
; and
estimate, using the pieces of observed data, a location or a cause of an abnormality in the communication network system .
5. A model construction method comprising the following executed by a computer:
receiving pieces of observed data from a communication network system that is a target for estimation of a location or a cause of an abnormality;
constructing, using the representative observed data, a first causal model for estimating the location or the cause of the abnormality from the pieces of observed data
7. A non-transitory computer-readable storage medium that stores therein a program for causing a computer to execute the model construction method according to claim 5.
1. A model construction apparatus comprising:
a memory; and
a processor configured to execute
collecting pieces of first observed data related to a communication network system that is a target for estimation of a location or a cause of an abnormality, wherein the pieces of first observed data comprise network traffic information of communication network system;
collecting pieces of second observed data related to a plurality of services provided by the communication network system, wherein the pieces of second observed data comprise information on states of services provided by the communication network system; and
constructing a causal model for estimating the location or the cause of the abnormality and an abnormal service among the plurality of services, using the pieces of first observed data and the pieces of second observed data, wherein the causal model is represented by a direct graph comprising:
(i) a set of equipment nodes in a first layer in a Bayesian network, each equipment node representing a respective state of a respective apparatus of the communication network system;
(ii) a set of service nodes in a second layer in the Bayesian network, each service node representing a respective state of a respective service provided by the communication network system; and
(iii) a set of observation nodes in a third layer in the Bayesian network, each observation node representing a piece of observed data;
using the constructed causal model to (i) estimate the location or the cause of the abnormality upon identifying a first service being abnormal, the first service being represented by a first service node in the second layer, (ii) generate, in response to the first service being identified as abnormal, a posterior probability of occurrence of abnormality for each of the services provided by the communication network system, and (iii) identify a second service node in the second layer, which associates with a second service different from the first service, based on the posterior probability of occurrence of abnormality; and
outputting, via a user interface, (i) the estimated location or the cause of the abnormality and (ii) the identified second service associated with the second service node.
4. An estimation apparatus comprising:
a memory; and
a processor configured to execute
collecting pieces of first observed data related to a communication network system that is a target for estimation of a location or a cause of an abnormality, wherein the pieces of first observed data comprise network traffic information of communication network system;
collecting pieces of second observed data related to a plurality of services provided by the communication network system, wherein the pieces of second observed data comprises information of states of services provided by the communication network system;
constructing a causal model for estimating the location or the cause of the abnormality and an abnormal service among the plurality of services, using the pieces of first observed data and the pieces of second observed data, wherein the causal model is represented by a direct graph comprising:
(i) a set of equipment nodes in a first layer in a Bayesian network, each equipment node representing a respective state of a respective apparatus of the communication network system;
(ii) a set of service nodes in a second layer in the Bayesian network, each service node representing a respective state of a respective service provided by the communication network system; and
(iii) a set of observation nodes in a third layer in the Bayesian network, each observation node representing a piece of observed data;
using the constructed causal model to (i) estimate the location or the cause of the abnormality upon identifying a first service being abnormal, the first service being represented by a first service node in the second layer, (ii) generate, in response to the first service being identified as abnormal, a posterior probability of occurrence of abnormality for each of the services provided by the communication network system, and (iii) identify a second service node in the second layer, which associates with a second service different from the first service, based on the posterior probability of occurrence of abnormality; and
outputting, via a user interface, (i) the estimated location or the cause of the abnormality and (ii) the identified second service associated with the second service node.
5. A model construction method executed by a computer including a memory and processor, the method comprising:
collecting pieces of first observed data related to a communication network system that is a target for estimation of a location or a cause of an abnormality, wherein the pieces of first observed data comprise network traffic information of communication network system;
collecting pieces of second observed data related to a plurality of services provided by the communication network system, wherein the pieces of second observed data comprises information of states of services provided by the communication network system;
constructing a causal model for estimating the location or the cause of the abnormality and an abnormal service among the plurality of services, using the pieces of first observed data and the pieces of second observed data, wherein the causal model is represented by a direct graph comprising:
(i) a set of equipment nodes in a first layer in a Bayesian network, each equipment node representing a respective state of a respective apparatus of the communication network system;
(ii) a set of service nodes in a second layer in the Bayesian network, each service node representing a respective state of a respective service provided by the communication network system; and
(iii) a set of observation nodes in a third layer in the Bayesian network, each observation node representing a piece of observed data;
using the constructed causal model to (i) estimate the location or the cause of the abnormality upon identifying a first service being abnormal, the first service being represented by a first service node in the second layer, (ii) generate, in response to the first service being identified as abnormal, a posterior probability of occurrence of abnormality for each of the services provided by the communication network system, and (iii) identify a second service node in the second layer, which associates with a second service different from the first service, based on the posterior probability of occurrence of abnormality; and
outputting, via a user interface, (i) the estimated location or the cause of the abnormality and (ii) the identified second service associated with the second service node.
6. A non-transitory computer-readable recording medium having computer-readable instructions stored thereon, which when executed, cause a computer including a memory and a processor to execute the model construction method according to claim 5.
Claim Objections
Claim 4 is objected to because of the following informalities:
Claims 4 recites “estimate, using the pieces of observed data, a location or a cause of an abnormality in the communication network system …” should read as “estimate, using the pieces of observed data, the location or the cause of the abnormality in the communication network system …”
Appropriate correction is required.
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.
The claim(s) 1-5 and 7 are rejected under 35 USC § 101 because the claimed invention is
directed to judicial exception an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register 01/07/2019, as well as subsequent USPTO eligibility guidance updates, and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1-3 are directed to model construction apparatus and fall within the statutory category of machine;
Yes, Claims 4 is directed to estimation apparatus and fall within the statutory category of machine;
Yes, Claims 5 is directed to method and fall within the statutory category of process;
Yes, Claims 7 is directed to non-transitory computer-readable storage medium and fall within the statutory category of article of manufacture.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claim 1: The limitations of “divide the received pieces of observed data into a plurality of clusters according to types of information represented by the respective pieces of observed data; determine, for each location or each cause of an abnormality, a representative value as representative observed data for each of the plurality of clusters; and construct, using the representative observed data, a first causal model for estimating the location or the cause of the abnormality from the pieces of observed data based on a rule-based method,” as drafted, are processes that, but for the recitation of generic computing components, under the broadest reasonable interpretation (BRI) in light of the specification, cover performance of the limitation in the human mind. For example, a person is capable of observing and evaluating data, mentally classifying the data into groups based on types of information, mentally determining representative values for the groups, and mentally construct a model or set of rules for estimating abnormal conditions based the determined values. The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
Claim 4: The limitations of “estimate, using the pieces of observed data, a location or a cause of an abnormality in the communication network system based on one of the first causal model, the second causal model, or the third causal model …,” as drafted, are processes that, but for the recitation of generic computing components, under the broadest reasonable interpretation (BRI) in light of the specification, cover performance of the limitation in the human mind. For example, a person is capable of observing and evaluating data, mentally applying one of several models or sets of rules to the observed data, and mentally determining a likely location or cause of an abnormality based on the applied models or rules. The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
Examiner note: The claim recites the limitations at high level of generality, and do not preclude those steps can be performed by human mind or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation in light of specification, covers performance of the limitation in the human mind, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong One, step 2A. See MPEP § 2106.04(a)(2)(III).
In MPEP § 2106.04(II)(B): A claim may recite multiple judicial exceptions. For example, claim 4 at issue in Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) recited two abstract ideas, and the claims at issue in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 101 USPQ2d 1961 (2012) recited two laws of nature. However, these claims were analyzed by the Supreme Court in the same manner as claims reciting a single judicial exception, such as those in Alice Corp., 573 U.S. 208, 110 USPQ2d 1976.
As explained in MPEP § 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a “series of mathematical calculations based on selected information” are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a “process of organizing information through mathematical correlations” are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of “managing a stable value protected life insurance policy by performing calculations and manipulating the results” as an abstract idea).
MPEP § 2106.04(a)(2)(I)(A): A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Further, MPEP recites: “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Claim 1, The limitation recites “divide the received pieces of observed data into a plurality of clusters according to types of information represented by the respective pieces of observed data; determine, for each location or each cause of an abnormality, a representative value as representative observed data for each of the plurality of clusters; and construct, using the representative observed data, a first causal model for estimating the location or the cause of the abnormality from the pieces of observed data based on a rule-based method.” The limitation with the broadest reasonable interpretation (BRI) in light of specification that can be considered to represent mathematical concepts expressed in words including mathematical equations, calculations and relationships, for example, [0026]-[0030]. See MPEP § 2106.04(a)(2)(I).
Claim 4, The limitation recites “estimate, using the pieces of observed data, a location or a cause of an abnormality in the communication network system based on one of the first causal model, the second causal model, or the third causal model …” The limitation with the broadest reasonable interpretation (BRI) in light of specification that can be considered to represent mathematical concepts expressed in words including mathematical equations, calculations and relationships, for example, [0030], [0036] - [0046], [0050], [0052] and [0054]. See MPEP § 2106.04(a)(2)(I).
Claims 5 and 7 recites substantially the same elements as claim 1, and are rejected for the
same reasons under 35 U.S.C. 101.
Therefore, claims 1, 4, 5 and 7 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims as a whole integrates the exception into a practical application of that exception.
Step 2A Prong 2: Claims 1, 4, 5 and 7: The judicial exception is not integrated into a practical application.
In particular, the claims recite the following additional elements - "A model construction apparatus comprising: a processor; and a memory storing program instructions that cause the processor to:” and “A model construction method comprising the following executed by a computer:” and “A non-transitory computer-readable storage medium that stores therein a program for causing a computer to execute the model construction method,” which are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception (see MPEP § 2106.05(f)) with the broadest reasonable interpretation, which does not integrate judicial exception into a practical application.
Further, the following additional elements – “receive pieces of observed data from a communication network system that is a target for estimation of a location or a cause of an abnormality” and “store, into the memory, a causal model for estimating the location or the cause of the abnormality, the causal model including a first causal model constructed based on a rule-based method, a second causal model constructed based on a data- driven method, and a third causal model combining the first causal model and the second causal model,” are merely recitations of insignificant extra-solution activity as data gathering (i.e., data transmission and storage), which does not integrate a judicial exception into practical application (see MPEP § 2106.05(g)).
Additionally, adding the steps of receive pieces of observed data and store a causal model to a process that only recites dividing data, determining value, constructing mathematical model (abstract idea) does not add a meaningful limitation to the process of dividing data, determining value, constructing mathematical model and estimating a location or a cause of an abnormality.
Therefore, the receiving limitations function only as generic data gathering and do not meaningfully limit or integrate the judicial exception into a practical application.
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong One and Two, it has been concluded that claims 1, 4, 5 and 7 recite a judicial exception and are directed to the judicial exception as the judicial exception is not integrated into a practical application.
Step 2B: Claims 1, 4, 5 and 7: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
As explained in MPEP 210.05(d)(II): The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); …
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); …
iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; …
Therefore, the additional elements, when considered individually and in combination, merely apply the judicial exception using conventional computing components and do not provide significantly more than the judicial exception.
Accordingly, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1, 4, 5 and 7 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 2-3 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself (and/or mathematical operations) or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-3 are also rejected for incorporating the deficiency of their independent claims 1.
Claim 2 recites “The model construction apparatus according to claim 1, wherein the program instructions further cause the processor to: calculate a value representing a relationship between pieces of observed data when the communication network system is in a normal state among the received pieces of observed data; calculate, using the value representing the relationship, a first conditional probability representing a relationship between a location or a cause of an abnormality in the communication network system and the pieces of observed data when the communication network system is in the normal state; calculate, using pieces of observed data when the communication network system is in an abnormal state, a second conditional probability representing a relationship between the location or the cause of the abnormality and the pieces of observed data when the communication network system is in the abnormal state, based on a data-driven method; and construct a second causal model for estimating the location or the cause of the abnormality from the pieces of observed data, using the first conditional probability and the second conditional probability.”
The limitation merely defines constructing a second causal model using the first and second conditional probabilities, which involves mathematical relationships and probabilistic calculations to model relationships between variables. Accordingly, the limitation recites a mathematical concept. Therefore, the claim 2 does not recite patent-eligible subject matter under 35 U.S.C. § 101.
Claim 3 recites “The model construction apparatus according to claim 2, wherein the program instructions further cause the processor to construct a third causal model by modifying the first causal model based on the second causal model.”
The limitation merely defines constructing a third causal model by modifying the first causal model based on the second causal model, which involves evaluating and making a judgement for relationships between models that can be performed mentally or with pen and paper. Accordingly, the limitation recites an extension of mental process. Therefore, the claim 3 does not recite patent eligible subject matter under 35 U.S.C. § 101.
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.
Claim(s) 1-5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Cinato
US20090292948A1 in view of Kushnir US20160162346A1.
Claim 1, Cinato teaches (Previously Presented) A model construction apparatus (Fig.1, fault management system 1. [0001] The present invention relates in general to telecommunications networks, and more particularly to fault location in telecommunications networks with distributed-agent or centralized fault management system by using Bayesian networks.” [0025] constructing a probabilistic model relating possible faults and status information in the identified limited region.) comprising:
a processor; and
a memory storing program instructions that cause the processor ([0084] The present invention further relates to a software product which can be loaded into the memory of a fault locating system in a communication network and includes software-code portions for performing, when the computer program product is run on the fault processing system, the method previously described. Examiner note: A POSITA would understand that the software product, when loaded into memory and executed on the system, is necessarily executed by a processor or computing component) to:
receive pieces of observed data from a communication network system that is a target for estimation of a location or a cause of an abnormality ([0023] receiving status information relating to at least an alarm, an event, a polled status or a test result in the communication network. [0024] identifying a limited region of the communication network in which the fault has occurred based on the received status information; [0025] constructing a probabilistic model relating possible faults and status information in the identified limited region; and [0026] locating the fault based on the constructed probabilistic model and on the received status information. [0104] Initially, each Autonomous Agent collects all the reports sent by the network apparatus it manages, including alarms spontaneously generated by the network apparatus or status changes detected by the polling system, which constantly checks the value of certain indicators on the network apparatus and generates and sends reports to the Autonomous Agent (block 10).);
divide the received pieces of observed data into a plurality of clusters according to types of information represented by the respective pieces of observed data ([0105], “… each Autonomous Agent groups the collected alarms or status changes based on information about the network apparatus that has sent the alarm or changed the status, the type of fault, and the time when the alarm was generated or the status change was notified.”);
determine, for each location or each cause of an abnormality, ([0067] for each individual possible fault in the accountable network resource, in the contiguous network resource and in the interconnection resource, determining a first probability that a status information is generated when the individual fault occurs (effect/cause probability); [0068] for each status information that may be generated by the network resource managed by the agent, determining a second probability that the status information is generated when no fault occurs in the network resource managed by the agent (effect/no_cause probability). Examiner note: A POSITA would understand that the grouped alarms define clusters/groups corresponding to possible fault locations or causes, and that the subsequent determination of probabilities for each fault represents determining data associated with each of the clusters/groups for the respective location or cause of abnormality.) and
construct, using the ([0061] constructing by the accountable agent a partial probabilistic model relating status information received by the accountable agent and faults occurred in the accountable network resource, in the contiguous network resource and in the interconnection resource. [0175] The procedure then terminates and the Accountable Agent constructs its own partial Bayesian Network shown in FIG. 5a based on the Useful Probability Group so formed. [0143] knowledge of equipment and topology concerning the resources involved by effects, the resources of network apparatuses interconnected via the resource involved by effects and the interconnection resources concerned. [0157] The existence of an effect/cause conditional probability implies a dependency between the CauseResource-Cause pair and the EffectResource-Effect pair in question. [0158] Based on the information contained in the effect/cause conditional probabilities and in the effect/no_cause conditional probabilities contained in the Useful Probability Group … [0047] inferring the fault location based on the probabilistic model and on status information received from the identified limited region of the communication network. [0181] In particular, inference is a process for assessing the probability of each state of a node … Examiner note: The partial Bayesian Network (i.e., first casual model) is constructed based on predefined dependency relationships between resources derived from topology knowledge, where conditional probabilities defines dependency arcs between nodes. The probabilistic model used for inference is generated from these conditional probability relationships contained in the partial Bayesian Network and represents the same causal dependency structure. A POSITA would understand that determine a fault location based on the probabilistic model corresponds to estimating a location or cause of an abnormality using the constructed causal model, and defining causal dependencies and constructing network structure according to the predefined relationships and conditional probability associations corresponds to a rule-based method).
However, Cinato fails to teach determine a representative value as representative observed data, and construct, using the representative observed data, a causal model.
Kushnir teaches determine a representative value as representative observed data for each of the plurality of clusters, and construct, using the representative observed data, a causal model ([0027] Each cluster may be modeled by a representative root cause vector that may be derived from the ranking of all the vector members in that cluster … The average of all of the vectors in the cluster may be used to determine a representative KPI ranking that may indicate what the top leading KPI root causes for a type of KQI degradation within the cluster. See also [0025]. Examiner note: the reference describes generating representative root cause vectors from clustered observed data and modeling each cluster using the representative vector. A POSITA would understand that the representative vector constitute representative observed data derived from grouped observations and can be used as inputs to a causal modeling framework to characterize relationships between observed indicators and underlying causes.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cinato to incorporate the teachings of Kushnir and to apply clustering based determination of representative values derived from grouped observed data to the observed data used within Cinato’s causal modeling framework in order to provide representative observed data inputs for casual model construction and inference. In this case, Cinato teaches receiving alarms and status information from a communication network, grouping the observed data according to types of information, determining conditional probability information associated with possible fault locations or causes, and constructing a partial Bayesian network causal model to infer the location or cause of faults in the communication network. Kushnir teaches determining representative values from clustered observed data, including deriving representative root cause vectors or representative KPI rankings from groups of observed indicators, and performing statistical modeling of root causes using Bayesian inference based on clustered and representative data. The combination of teachings would provide benefit of improving the robustness and computational efficiency of the causal modeling and fault estimation process by providing representative aggregated observed data inputs derived from clustered observations, which reduce variability in the input data and decrease the computational cost of the probabilistic inference while maintaining accurate casual relationships.
Claim 2, Cinato teaches (Previously Presented) The model construction apparatus according to claim 1, wherein the program instructions further cause the processor to:
calculate a value representing a relationship between pieces of observed data when the communication network system is in a normal state among the received pieces of observed data ([0190] … the statistics of operation cases in which, after receiving alarms, events, polled statuses and test results, no fault has been found in the field. Examiner note: the statistics of operation cases correspond to calculated quantitative relationships among the received pieces of observed data (e.g., alarms, events, statuses, and test results) observed when no fault is present (i.e., when the communication network system is in normal state). Therefore, these statistical correlations constitute a value representing a relationship between pieces of observed data in a normal state.);
calculate, using the value representing the relationship, a first conditional probability representing a relationship between a location or a cause of an abnormality in the communication network system and the pieces of observed data when the communication network system is in the normal state ([0032] for each possible status information in the identified limited region, determining a second probability that the status information is generated when no fault occurs in the identified limited region (effect/no_cause probability). [0190] effect/no_cause conditional probabilities are updated using the statistics of operation cases in which, after receiving alarms, events, polled statuses and test results, no fault has been found in the field. Examiner note: the identified limited region corresponds to a location in the communication network, and the absence of a fault corresponds to a normal state of the communication network system. The probability that status information is generated when no fault occurs represents a conditional probabilistic relationship between the location or cause of an abnormality and received pieces of observed data (e.g., alarms, events, statuses, and test results) when the communication network is in the normal state. Further, these conditional probabilities are calculated and updated using statistics derived from observed operation cases ([0190]), which corresponds to the calculated value representing relationships among pieces of observed data in the normal state.);
calculate, using pieces of observed data when the communication network system is in an abnormal state, a second conditional probability representing a relationship between the location or the cause of the abnormality and the pieces of observed data when the communication network system is in the abnormal state, based on a data-driven method ([0031] for each possible fault in the identified limited region, determining a first probability that a status information is generated when the fault occurs (effect/cause probability). [0189] effects/cause conditional probabilities are updated using statistics of the cases of alarms, events, polled statuses and test results received in association with a single fault detected in the field. Examiner note: the occurrence of a fault corresponds to an abnormal state of the communication network system, and the identified limited region corresponds to a location in the communication network. The probabilities updated using statistics of operation cases in which faults have been observed to represent probabilistic relationships between the location or cause of the abnormality and the received pieces of observed data (e.g., alarms, events, statuses, and test results) when the communication network system is in an abnormal state. Further, deriving conditional probabilities from historical operation data constitutes a data-driven method because the probabilistic relationships are learned from empirical observed data rather than predefined rules.); and
construct a second causal model for estimating the location or the cause of the abnormality from the pieces of observed data, using the first conditional probability and the second conditional probability ([0168] In particular, FIGS. 5a, 5b and 5c show, respectively, examples … of a partial Bayesian Network constructed by the Contiguous Agent … [0062] constructing by the contiguous agent a partial probabilistic model relating status information received by the contiguous agent and faults occurred in the accountable network resource, in the contiguous network resource and in the interconnection resource. [0069] constructing the partial probabilistic model based on the determined first and second probabilities. [0126] … each Contiguous Agent that has received the request sent by the Accountable Agent constructs its own partial Bayesian Network (block 70). [0047] inferring the fault location based on the probabilistic model and on status information received from the identified limited region of the communication network. [0181] In particular, inference is a process for assessing the probability of each state of a node … Examiner note: the reference teaches constructing the partial Bayesian Network constructed by the Contiguous Agent based on the determined first and second conditional probabilities, and inferring the fault location using the probabilistic model.).
Claim 3, Cinato teaches (Previously Presented) The model construction apparatus according to claim 2, wherein the program instructions further cause the processor to construct a third causal model by modifying the first causal model based on the second causal model ([0128] Then the Accountable Agent constructs a complete Bayesian Network, hereinafter referred to as Inference Bayesian Network, based on, and in particular by combining, its own partial Bayesian Network and the partial Bayesian Networks received from the Contiguous Agents, as described in detail further on (block 90), which Inference Bayesian Network is maintained by the Accountable Agent and regards faults on the network apparatus managed by the Accountable Agent, on the Silent Resource, and on the network apparatuses managed by the Contiguous Agents, and alarms collected by, and/or status changes notified to, the Accountable and the Contiguous Agents. [0177] the Accountable Agent merges together the cause states common to the two partial Bayesian Networks, in the example shown in FIGS. 5a and 5b … [0154] … the Accountable Agent builds the Inference Bayesian Network based on the its individual partial Bayesian Network and the individual partial Bayesian networks built by the Contiguous Agents. In particular, … the Inference Bayesian Network is constructed via the overlaying of the two individual partial Bayesian Networks, obtained by pooling the cause states and keeping the relationships between causes and effects. Examiner note: A POSITA would understand that combining and overlaying the accountable agent’s partial Bayesian Network with the partial Bayesian networks received from contiguous agents modifies the first causal model using information from the second causal model, thereby producing a new (third) causal model).
The elements of claims 5 and 7 are substantially the same as those of claim 1. Therefore, the elements of claims 5 and 7 are rejected due to the same reasons as outlined above for claim 1.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and
103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for
the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 4 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cinato US20090292948A1.
Claim 4, Cinato teaches (Previously Presented) An estimation apparatus ([0001] The present invention relates in general to telecommunications networks, and more particularly to fault location in telecommunications networks with distributed-agent or centralized fault management system by using Bayesian networks.” [0025] constructing a probabilistic model relating possible faults and status information in the identified limited region.) comprising:
a processor; and
a memory storing program instructions that cause the processor ([0084] The present invention further relates to a software product which can be loaded into the memory of a fault locating system in a communication network and includes software-code portions for performing, when the computer program product is run on the fault processing system, the method previously described.) to:
receive pieces of observed data from a communication network system that is a target for estimation of a location or a cause of an abnormality ([0023] receiving status information relating to at least an alarm, an event, a polled status or a test result in the communication network. [0024] identifying a limited region of the communication network in which the fault has occurred based on the received status information; [0025] constructing a probabilistic model relating possible faults and status information in the identified limited region; and [0026] locating the fault based on the constructed probabilistic model and on the received status information. [0104] Initially, each Autonomous Agent collects all the reports sent by the network apparatus it manages, including alarms spontaneously generated by the network apparatus or status changes detected by the polling system, which constantly checks the value of certain indicators on the network apparatus and generates and sends reports to the Autonomous Agent (block 10).);
divide the received pieces of observed data into a plurality of clusters according to types of information represented by the respective pieces of observed data ([0105], “… each Autonomous Agent groups the collected alarms or status changes based on information about the network apparatus that has sent the alarm or changed the status, the type of fault, and the time when the alarm was generated or the status change was notified.”);
store, into the memory, a causal model for estimating the location or the cause of the abnormality, the causal model including a first causal model constructed based on a rule-based method, a second causal model constructed based on a data- driven method, and a third causal model combining the first causal model and the second causal model (Fig. 5a-5c and 6a-6b; [0168] In particular, FIGS. 5a, 5b and 5c show, respectively, examples of a partial Bayesian Network constructed by the Accountable Agent, of a partial Bayesian Network constructed by the Contiguous Agent, and of an Inference Bayesian Network constructed by the Accountable Agent by combining (merging) the two partial Bayesian Networks shown in FIGS. 5a and 5b. [0180] … FIGS. 6a and 6b and 6c how the inference process may be performed on the Inference Bayesian Network shown in FIG. 5c. [0139] Information concerning the above probabilities are stored in database 5 in FIG. 1, hereinafter referred to as Probability Database (PD), which can be maintained on an Application Agent and managed in a centralized manner or on the Autonomous Agents and managed in a distributed manner. [0143] knowledge of equipment and topology concerning the resources involved by effects, the resources of network apparatuses interconnected via the resource involved by effects and the interconnection resources concerned. [0157] The existence of an effect/cause conditional probability implies a dependency between the CauseResource-Cause pair and the EffectResource-Effect pair in question. [0158] Based on the information contained in the effect/cause conditional probabilities and in the effect/no_cause conditional probabilities contained in the Useful Probability Group, a Bayesian Network Probability Table for each effect state of the Bayesian Network is computed … [0189] effects/cause conditional probabilities are updated using statistics of the cases of alarms, events, polled statuses and test results received in association with a single fault detected in the field; [0190] effect/no_cause conditional probabilities are updated using the statistics of operation cases in which, after receiving alarms, events, polled statuses and test results, no fault has been found in the field. [0192] Learning is achieved by using statistical information relating to the CauseResource and EffectResource pair, … Examiner note: The reference discloses three causal models, including a first partial Bayesian Network constructed by the Accountable agent (Fig.5a) corresponds to the first casual model and is constructed based on predefined dependency relationships derived from topology knowledge and stored conditional probabilities, which constitutes a rule-based method; a second partial Bayesian Network constructed by the Contiguous agent (Fig.5b) corresponds to the second casual model and is based on conditional probabilities learned and updated using empirical operational statistics collected during system operation, which constitutes a data-driven method; an inference Bayesian Network (Fig.5c) constructed by combining the partial networks corresponds to the third casual model combing the first and second casual models for the inference process. Further, the reference inherently stores the constructed Bayesian Network models and associated probability information in memory, because the models are constructed and subsequently used by the accountable agent to perform inference which necessarily require the models to be store in memory accessible to any computing component); and
estimate, using the pieces of observed data, a location or a cause of an abnormality in the communication network system based on one of the first causal model, the second causal model, or the third causal model stored in the memory ([0129] Finally, the Accountable Agent performs an inference process on the complete Bayesian Network (of the considered limited network region) using the information of those alarms that have been received by, and/or status changes that have been notified to, the Accountable Agent and the Contiguous Agent(s) as input data to the Inference Bayesian Network (block 100), thus identifying the fault (block 110). In particular, starting from the complete Bayesian Network and from the alarms received by the Accountable and Contiguous Agents and/or the status changes notified thereto, the inference process allows computing the probability of each possible cause. Examiner note: The reference discloses constructing Bayesian network models, including partial Bayesian networks (constructed by accountable and Contiguous Agent as first and second casual model) and an inference (complete) Bayesian network (i.e., third casual model; see, e.g., para. 153-154, 0177-0179). The reference further disclose performing an inference process using the constructed Bayesian network to identify faults based on received alarms and status information ([0129], [0181]).).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Khanduja US20110055138A1, discloses a method of processing network activity data, includes
receiving network activity data and generating an event based on the network activity data. The method also includes generating a probability based at least in part on Bayesian statistics, the probability corresponding to a likelihood that the event caused or was caused by another event. The method also includes generating an event message corresponding to the event based on the probability.
Skaanning US20010011260A1, discloses an automated diagnostic system uses Bayesian networks to
diagnose a system. Knowledge acquisition is performed in preparation to diagnose the system. An issue to diagnose is identified. Causes of the issue are identified. Subcauses of the causes are identified. Diagnostic steps are identified. Diagnostic steps are matched to causes and subcauses. Probabilities for the causes and the subcauses identified are estimated. Probabilities for actions and questions set are estimated. Costs for actions and questions are estimated.
Yemini US20050137832A1, discloses determining the source of a problem in a complex system of
managed components based upon symptoms. The problem source identification process is split into different activities. Explicit configuration non-specific representations of types of managed components, their problems, symptoms and the relations along which the problems or symptoms propagate are created that can be manipulated by executable computer code. A data structure is produced for determining the source of a problem by combining one or more of the representations based on information of specific instances of managed components in the system. Computer code is then executed which uses the data structure to determine the source of the problem from one or more symptoms.
Lakshmanan US20140006871A1, discloses techniques are provided for gathering network
information, analyzing the gathered information to identify correlations, and for diagnosing a problem based upon the correlations. The diagnosis may identify a root cause of the problem. In certain embodiments, a computing device may be configurable to determine a first event from information, allocate a first event to a first cluster, the first cluster is from one or more clusters of events, based on a set of attributes for the first event, and determine a set of attributes for the first cluster, and rank the first cluster against the other clusters from the one or more clusters of events based on the set of attributes for the first cluster. The set of attributes may be indicative of the relationship between events in the cluster. In some embodiments, one or more recommendations may be provided for taking preventative or corrective actions for the problem.
M. Julia Flores et al., “Incorporating expert knowledge when learning Bayesian network structure: A medical case study,” Published in 2011, discloses a methodology for incorporating expert knowledge as structural priors when learning BNs … We also presented novel visualisations of the learned networks, which support the interactive development process by allowing the knowledge engineers to assess intermediate results and revise experimental parameters. These visualisations could also assist comparisons of BN learning algorithms (e.g., [12]) …
Any inquiry concerning this communication or earlier communications from the examiner should be
directed to YI HAO whose telephone number is (571)270-1303. The examiner can normally be reached Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached on (571)272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187