DETAILED ACTION This Office Action is in response to the claims filed on 11/22/2022. Claims 1-19 are pending. 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 Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. GB2007883.8, filed on 05/27/2020. Examiner Notes Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. Examiner may also include cited interpretations encompassed within parenthesis, e.g. ( Examiner’s interpretation ), for clarity. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution. 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 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. Information Disclosure Statement The information disclosure statement (IDS) submitted on 11/22/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Objections Claims 4 and 18 are objected to because of the following informalities: Claim 4, Line 3 states “monitored 5 event” and should read “monitored event”. Claim 18, Line 6 states “exceptional 5 event” and should read “exceptional event”. 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. Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception (an abstract idea), as it has not been integrated into a practical application and the claim(s) further do/does not recite significantly more than the judicial exception. Examiner has evaluated the claim(s) under the framework provided in MPEP 2106 and has provided such analysis below. To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires: Step 1 . Determining if the claim falls within a statutory category of a Process, Machine, Manufacture, or a Composition of Matter ( see MPEP 2106.03 ); Step 2A . Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea ( MPEP 2106.04 ); Step 2A is a two-prong inquiry. MPEP 2106.04(II)(A) . Under the first prong , examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2106.04(a)(2) . The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d) . Step 2B . If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106 ). Step 1 : Claims 1-17 are directed to a system , as such these claims fall within the statutory category of a manufacture . Claim 18 is directed to a method , as such these claims fall within the statutory category of process . Claim 19 is directed to a computer readable medium, as such these claims fall within the statutory category of manufacture . Step 2A, Prong 1 : The examiner submits that the foregoing claim limitations constitute abstract ideas, as the claims cover mental processes and/or mathematical concepts , given the broadest reasonable interpretation. In order to apply Step 2A, a recitation of claims is copied below. The limitations of those claims which describe an abstract idea are bolded . As per claim 1 , the claim recites the limitations of: an extraction module configured to extract one or more of the plurality of monitored event records as exceptional event records in dependence on a determination of whether the monitored values satisfy respective regularity conditions (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) which are defined as concepts that can practically be performed in the human mind (e.g. observations, evaluations, judgments, opinions), or by a human using pen and paper as a physical aid. For instance, a person can reasonably evaluate monitored values and determine whether or not those values satisfy regularity conditions with/without the aid of pen and paper. This limitation is directed towards performing a mental process on a generic computer) a modification module configured to generate one or more modified event records, each modified event record being generated by modifying the monitored attribute of at least one of the variables of one of the monitored event records (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. A person can reasonably modify an attribute of at least one variable of monitored data (e.g. amount of rainfall – reference Spec. [Pg.15 Ln.18-20) with/without the aid of pen/paper. Additionally, per Applicant’s disclosure Spec. [Pg.15 Ln.30-33], this limitation amounts to Mathematical Relationships, which are defined as a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.) Step 2A, Prong 2 : As per claim 1 , this judicial exception is not integrated into a practical application because the additional claim limitations outside the abstract idea only present mere instructions to apply an exception and/ or insignificant extra solution activity . In particular, the claim recites the additional limitations: the contingency forecast system comprising one or more computer processors configured to implement: an input module configured to receive a plurality of monitored event records each describing a state of the monitored system during a monitored event of the monitored system, each monitored event record comprising a monitored attribute for each of a plurality of variables of the monitored system; (The additional element amounts to Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process.) a selection module configured to select a subset of contingency event records from a set of event records comprising the extracted exceptional event records and the generated modified event records; (The additional element amounts to Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g).) a forecast simulation module configured to apply one or more forecasting techniques to the selected subset of contingency event records to generate one or more output parameters as the contingency forecast simulation . (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Also, use of a computer or other machinery in its ordinary capacity for economic or other tasks ( e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea ( e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Additionally, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception.) Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole. Step 2B : For step 2B of the analysis, the Examiner must consider whether each claim limitation individually or as an ordered combination amounts to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same. The additional elements as described in Step 2A Prong 2 are not sufficient to amount to significantly more than the judicial exception because the additional limitations are considered mere data gathering and mere instructions to apply an exception. See MPEP 2106.04(d) referencing MPEP 2106.05( f )/( g ) . Per MPEP 2106.05(d), another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. The courts have recognized the following applicable 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, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory. Additionally, per Applicant’s disclosure Spec. [Pg.20 Ln.18], “Suitable forecasting techniques are well-known in the art and are not described in detail here to avoid obscuring the disclosure.” For the foregoing reasons, claim 1 is directed to an abstract idea without significantly more and is rejected as not patent eligible under 35 U.S.C. 101. Independent claims 18 and 19 recites substantially the same subject matter as claim 1 and are rejected under similar rationale. (Dependent Claims) C laim 2 recites A contingency forecasting system according to claim 1, wherein the extraction module is configured to extract the exceptional event records by applying one or more anomaly detection techniques to the monitored attributes of each monitored event record . The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Additionally, “applying one or more anomaly detection techniques” is considered WURC per Applicant’s disclosure Spec. [Pg.22 Ln.3], “Anomaly detection techniques are well-known in the art”. Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 3 recites A contingency forecasting system according to claim 2, wherein the one or more anomaly detection techniques are selected from: an occurrence count of the monitored attributes; and/or cluster analysis of the monitored attributes. The additional element elaborates on the anomaly detection technique, thus further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 4 recites A contingency forecasting system according to claim1, wherein the modification module is configured to generate each modified event record by changing the monitored attribute of at least one variable of the respective monitored 5 event record to the monitored attribute of that variable in another one of the monitored event records . The additional element elaborates on the modified event record, thus further amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 5 recites A contingency forecasting system according to claim1, wherein the selection module is configured to: estimate one or more risk factors for each event record in the set of event records; (The additional element amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)).) and select one or more of the extracted exceptional event records and the modified event records from the set of event records based on the estimated risk factors. (The additional element amounts to Insignificant Extra-solution Activity (mere data gathering) per MPEP 2106.05(g).) Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 6 recites A contingency forecasting system according to claim 5, wherein the one or more risk factors include: a likelihood, or frequency, of occurrence of the monitored attributes of that event record; and/or an impact score that is indicative of the relative impact of the monitored attributes of that event record on the operation of the monitored system. The additional elements elaborates on the risk factors, thus further amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 7 recites A contingency forecasting system according to claim 5, wherein the selection module is configured to select the subset of contingency event records based on a weighted sum of the risk factors for each of the event records in the set of event records . The additional elements amount to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 8 recites A contingency forecasting system according to claim 7, wherein the selection module is configured to select the subset of contingency event records by comparing the weighted sum of the risk factors of each of the event records in the set of event records to a threshold value . The additional elements amount to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 9 recites A contingency forecasting system according to claim 7, wherein the selection module is configured to select the subset of contingency event records by: ranking the set of event records based on the weighted sum of the respective risk factors for each of the event records in the set of event records; determining the cumulative weighted sum of the respective risk factors of the highest ranking event records in the set of event records; and selecting those event records from the set of event records for which the cumulative weighted sum is less than or equal to a threshold value . The additional elements amount to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. Claim 10 recites A contingency forecasting system according to claim1, wherein the extraction module is configured to extract one or more of the plurality of monitored event records as exceptional event records that include an anomalous monitored attribute and to extract one or more of the plurality of monitored event records as regular event records that do not include an anomalous monitored attribute. The additional element elaborates on the extracted event records, thus further amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer. Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 11 recites A contingency forecasting system according to claim 10, wherein the extraction module is configured to determine an irregular pattern, for each exceptional event record, by pattern mining the one or more exceptional event records, and/or a regular pattern for each regular event record by pattern mining the one or more regular event records, (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(g). Additionally, pattern mining is interpreted as WURC pe Applicant’s disclosure Spec. [Pg.22 Ln.12], “ Pattern mining methods are well-known in the art”.) and wherein the modification module is configured to generate the modified event records in the form of modified patterns, each modified pattern being generated by modifying at least one of the monitored attributes of a respective one of the irregular patterns, or of a respective one of the regular patterns. (The additional element elaborates on the modified events record, thus further amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)) Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 12 recites A contingency forecasting system according to claim 11, wherein the extraction module is configured to determine the one or more irregular patterns and/or the one or more regular patterns using one or more pattern mining methods selected from: a frequent pattern mining technique; an Apriori algorithm; and/or an Eclat algorithm. The additional element further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(g). Additionally, pattern mining is interpreted as WURC pe Applicant’s disclosure Spec. [Pg.22 Ln.12], “ Pattern mining methods are well-known in the art”. Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 13 recites A contingency forecasting system according to claim 11, wherein each pattern comprises one or more of the monitored attributes of the respective event record and a value for a pairwise connection between each pair of monitored attributes in that pattern . The additional element elaborates on each pattern, thus further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(g). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 14 recites A contingency forecasting system according to claim1, wherein the modification module is configured to generate each modified pattern by changing at least one of: a monitored attribute, which is not an anomalous monitored attribute, of a respective regular pattern to an anomalous monitored attribute for that variable in an exceptional event record; and a monitored attribute, which is not an anomalous monitored attribute, of a respective irregular pattern to another monitored attribute for that variable, which is not an anomalous monitored attribute, in a regular event record . The additional element elaborates on each modified pattern generation, thus further amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) performed on a computer and/or Mathematical Concepts (MPEP 2106.04(a)(2)(I)). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 15 recites A contingency forecasting system according to claim 11, wherein the modification module is configured to output modified patterns to the selection module, each modified pattern that is output to the selection module having a weighted sum of pairwise distance to the respective irregular pattern, or the respective regular pattern, that is less than a threshold distance . The additional element amounts to Insignificant Extra-solution Activity (mere data outputting) per MPEP 2106.05(g). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 16 recites A contingency forecasting system according to claim 15, wherein the modification module is configured to select a set of modified patterns from the generated modified patterns to output to the selection module by: determining a weighted sum of pairwise distances between each modified pattern generated and the respective irregular pattern, or the respective regular pattern; and selecting the modified patterns having a weighted sum of pairwise distances that is less than the threshold distance . The additional elements amount to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, the claims invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks ( e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea ( e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. C laim 17 recites A contingency forecasting system according to claim11, wherein each exceptional event record in the subset of contingency event records takes the form of a respective one of the irregular patterns and each modified event record in the subset of contingency event records takes the form of a respective one of the modified patterns, the selection module being configured to select the subset of contingency event records from the one or more irregular patterns and the one or more modified patterns . The additional elements elaborate on the exceptional event records, thus further amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(g). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries set forth in Graham V. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 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 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. Claim s 1-6, 10-14, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Nasle et al. EP Patent No. 2038782 B1 (hereinafter referred to as “ Nasle ”) in view of Li et al. US P ub. No. 20190317952 A1 (hereinafter referred to as “ Li ”). Regarding claim 1 , Nasle discloses A contingency forecasting system for generating a contingency forecast simulation of a monitored system, the contingency forecast system comprising one or more computer processors configured to implement: (“the invention provides a system for real-time modelling of uninterruptible power supply (UPS) control elements protecting an electrical system, comprising: [ ] the UPS transient stability simulation engine further configured to utilize a user-defined UPS control logic model to forecast an aspect of the interaction between UPS control elements and the electrical system during a contingency event;” Nasle [P.0014]) an input module configured to receive a plurality of monitored event records each describing a state of the monitored system during a monitored event of the monitored system (“a data acquisition component communicatively connected to a sensor configured to acquire real-time data output from the electrical system” Nasle [P.0014]) , each monitored event record comprising a monitored attribute for each of a plurality of variables of the monitored system (“Continuing with Figure 1, the analytics engine 118 can be configured to implement pattern / sequence recognition into a real-time decision loop that, e.g., is enabled by a new type of machine learning called associative memory, or hierarchical temporal memory (HTM) [ ] Associative memory allows storage, discovery, and retrieval of learned associations between extremely large numbers of attributes in real time. ” Nasle [P.0080]) ; an extraction module configured to extract one or more of the plurality of monitored event records as exceptional event records in dependence on a determination of whether the monitored values satisfy respective regularity conditions (“Thus, by observing normal system operation over time, and the normal predicted system operation over time, the associative memory is able to learn normal patterns as a basis for identifying non-normal behavior and appropriate responses, and to associate patterns with particular outcomes, contexts or responses.” Nasle [P.0080]) ; a modification module configured to generate one or more modified event records, each modified event record being generated by modifying the monitored attribute of at least one of the variables of one of the monitored event records (“client 128 may utilize a variety of network interfaces [ ] to access, configure, and modify the [ ] calibration parameters (e.g., configuration files, calibration parameters, etc.)” Nasle [P.0085]) ; a selection module configured to select a subset of contingency event records from a set of event records comprising the extracted exceptional event records and the generated modified event records; and, a forecast simulation module configured to apply one or more forecasting techniques to the selected subset of contingency event records to generate one or more output parameters as the contingency forecast simulation . (“Continuing with Figure 5, the Analytics Engine 118 is communicatively interfaced with a HTM Pattern Recognition and Machine Learning Engine 551 . The HTM Engine 551 is configured to work in conjunction with the Analytics Engine 118 and a virtual system model of the monitored system to make real-time predictions (i.e., forecasts) about various operational aspects of the monitored system.” Nasle [P.0101]) Nasle fails to specifically disclose a selection module configured to select a subset of contingency event records from a set of event records comprising the extracted exceptional event records and the generated modified event records. However, Li discloses a selection module configured to select a subset of contingency event records from a set of event records comprising the extracted exceptional event records and the generated modified event records (“selecting attributes from the set of attributes using the set of clusters, wherein selecting the attributes comprises identifying, from the set of attributes, a subset of attributes that are associated with the clusters” Li [P.0009]) Nasle and Li are analogous art as they both involve systems that process data, generate predictions, and use hierarchical or structured models. Each uses real-time or timestamped data, involves clustering or pattern detection, and produces outputs that inform decision-making or forecasting. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nasle’s physical system simulation and forecasting technique to include attribute selection, such as that taught by Li, in order “to generate more accurate predictions” Li [P.0002]. Regarding claim 2 , Nasle in view of Li disclose A contingency forecasting system according to claim 1 , Nasle further discloses wherein the extraction module is configured to extract the exceptional event records by applying one or more anomaly detection techniques to the monitored attributes of each monitored event record . (“when a component begins to fail, the operating parameters will begin to change. This change may be sudden or it may be a progressive change over time. Analytics engine 118 can detect such changes and issue warnings that can allow the changes to be detected before a failure occurs. The analytic engine 118 can be configured to generate warnings that can be communicated via interface 532.” Nasle [P.0099]) Regarding claim 3 , Nasle in view of Li disclose A contingency forecasting system according to claim 2 , although Nasle fails to specifically disclose wherein the one or more anomaly detection techniques are selected from: an occurrence count of the monitored attributes; and/or cluster analysis of the monitored attributes. Li , however, discloses wherein the one or more anomaly detection techniques are selected from: an occurrence count of the monitored attributes; and/or cluster analysis of the monitored attributes. (“clustering the timestamped data into a set of clusters, wherein clustering the timestamped data comprises clustering based on patterns of the timestamped data and responses of the timestamped data to the set of independent variables” Li [P.0009]) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nasle’s physical system simulation and forecasting technique to include attribute cluster analysis, such as that taught by Li, in order “to generate more accurate predictions” Li [P.0002]. Regarding claim 4 , Nasle in view of Li disclose A contingency forecasting system according to claim1, although Nasle fails to specifically disclose wherein the modification module is configured to generate each modified event record by changing the monitored attribute of at least one variable of the respective monitored 5 event record to the monitored attribute of that variable in another one of the monitored event records . However, Li discloses , wherein the modification module is configured to generate each modified event record by changing the monitored attribute of at least one variable of the respective monitored 5 event record to the monitored attribute of that variable in another one of the monitored event records . (“ordering the selected attributes can include globally testing different combinations of attributes to identify the best hierarchy.” Li [P.0181]. The cited limitation is interpreted as testing different attribute combinations due to Applicant’s disclosure “Modifying event records in the manner described above, allows knowledge transfer between event records, generating possible, but not previously encountered, combinations of monitored attributes for the monitored system.” Spec. [Pg.9 Ln.33]) ) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nasle’s physical system simulation and forecasting technique to include attribute testing, such as that taught by Li, in order “to generate more accurate predictions” Li [P.0002]. Regarding claim 5 , Nasle in view of Li disclose A contingency forecasting system according to claim1, Nasle further discloses wherein the selection module is configured to: estimate one or more risk factors for each event record in the set of event records ( “Figure 15 is a flow chart illustrating an example process for analyzing the reliability of an electrical power distribution and transmission system [ ] First, in step 1502, reliability data ( i.e. risk factors ) can be calculated and/or determined. The inputs used in step 1502 can comprise power flow data, e.g., network connectivity, loads, generations, cables / transformer impedances, etc., which can be obtained from the predicted values generated in step 1008, reliability data associated with each power system component, lists of contingencies to be considered, which can vary by implementation including by region, site, etc. , customer damage (load interruptions) costs, which can also vary by implementation, and load duration curve information. Other inputs can include failure rates, repair rates, and required availability of the system and of the various components.” Nasle [P.0163]) ; and select one or more of the extracted exceptional event records and the modified event records from the set of event records based on the estimated risk factors. (“ In step 1504 a list of possible outage conditions and contingencies can be evaluated including loss of utility power supply, generators, UPS, and/or distribution lines and infrastructure. In step 1506, a power flow analysis for monitored system 102 under the various contingencies can be performed. This analysis can include the resulting failure rates, repair rates, cost of interruption or downtime versus the required system availability, etc.” Nasle [P.0164]) Regarding claim 6 , Nasle in view of Li disclose A contingency forecasting system according to claim 5 , Nasle further discloses wherein the one or more risk factors include: a likelihood, or frequency, of occurrence of the monitored attributes of that event record; and/or an impact score that is indicative of the relative impact of the monitored attributes of that event record on the operation of the monitored system. (“The reliability indices can include load point reliability indices, branch reliability indices, and system reliability indices. For example, various load/bus reliability indices can be determined such as probability and frequency of failure , expected load curtailed, expected energy not supplied, frequency of voltage violations , reactive power required, and expected customer outage cost.” Nasle [P.0166]) Regarding claim 10 , Nasle in view of Li disclose A contingency forecasting system according to claim1, Nasle further discloses wherein the extraction module is configured to extract one or more of the plurality of monitored event records as exceptional event records that include an anomalous monitored attribute and to extract one or more of the plurality of monitored event records as regular event records that do not include an anomalous monitored attribute (“by observing normal system operation over time, and the normal predicted system operation over time, the associative memory is able to learn normal patterns as a basis for identifying non-normal behavior and appropriate responses, and to associate patterns with particular outcomes, contexts or responses.” Nasle [P.0080]) . Regarding claim 11 , Nasle in view of Li disclose A contingency forecasting system according to claim 10, Nasle further discloses wherein the extraction module is configured to determine an irregular pattern, for each exceptional event record, by pattern mining the one or more exceptional event records, and/or a regular pattern for each regular event record by pattern mining the one or more regular event records, (“Thus, by observing normal system operation over time, and the normal predicted system operation over time, the associative memory is able to learn normal patterns as a basis for identifying non-normal behavior and appropriate responses, and to associate patterns with particular outcomes, contexts or responses.” Nasle [P.0080]) and wherein the modification module is configured to generate the modified event records in the form of modified patterns, each modified pattern being generated by modifying at least one of the monitored attributes of a respective one of the irregular patterns, or of a respective one of the regular patterns (“Method 800 proceeds to operation 804 where the predicted system output value for the virtual system model is updated with a real-time output value for the monitored system. For example, if sensors interfaced with the monitored system outputs a real-time current value of A, then the predicted system output value for the virtual system model is adjusted to reflect a predicted current value of A.” Nasle [P.0111]) . Regarding claim 12 , Nasle in view of Li disclose A contingency forecasting system according to claim 11 , Nasle further discloses wherein the extraction module is configured to determine the one or more irregular patterns and/or the one or more regular patterns using one or more pattern mining methods selected from: a frequent pattern mining technique; an Apriori algorithm; and/or an Eclat algorithm. (“the analytics engine 118 can be configured to implement pattern/sequence recognition into a real-time decision loop that, e.g., is enabled by a new type of machine learning called associative memory, or hierarchical temporal memory (HTM)” Nasle [P.0080]) Regarding claim 13 , Nasle in view of Li disclose A contingency forecasting system according to claim 11, Nasle further discloses wherein each pattern comprises one or more of the monitored attributes of the respective event record (“configured to store and process patterns observed from the real-time data output and the predicted data output” Nasle [P.0014]) and a value for a pairwise connection between each pair of monitored attributes in that pattern . Nasle fails to specifically disclose a value for a pairwise connection between each pair of monitored attributes in that pattern . However, Li discloses a pairwise connection between each pair of monitored attributes in that pattern . (“selected attributes can be ordered such that the between-group distances associated with sequential pairs of selected attributes in the ordered set of selected attributes is monotonically decreasing from a highest level of the hierarchy to a lowest level of the hierarchy.” Li [P.0181]) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nasle’s physical system simulation and forecasting technique to include a pairwise connection between pattern attributes , such as that taught by Li, in order “to generate more accurate predictions” Li [P.0002]. Regarding claim 14 , Nasle in view of Li disclose A contingency forecasting system according to claim1, Nasle further discloses wherein the modification module is configured to generate each modified pattern by changing at least one of: a monitored attribute, which is not an anomalous monitored attribute, of a respective regular pattern to an anomalous monitored attribute for that variable in an exceptional event record; and a monitored attribute, which is not an anomalous monitored attribute, of a respective irregular pattern to another monitored attribute for that variable, which is not an anomalous monitored attribute, in a regular event record . (“This duplicate model can be used for what-if simulations. In other words, this model can be used to allow a system designer to make hypothetical changes to the facility and test the resulting effect, without taking down the facility or costly and time consuming analysis. Such hypothetical can be used to learn failure patterns and signatures as well as to test proposed modifications, upgrades, additions, etc., for the facility.” Nasle [P.0078]) Regarding claim 17 , Nasle in view of Li disclose A contingency forecasting system according to claim11, Nasle further discloses wherein each exceptional event record in the subset of contingency event records takes the form of a respective one of the irregular patterns and each modified event record in the subset of contingency event records takes the form of a respective one of the modified patterns (“the analytics engine 118 can be configured to implement pattern/sequence recognition into a real-time decision loop that, e.g., is enabled by a new type of machine learning called associative memory, or hierarchical temporal memory (HTM) [ ] Associative memory allows storage, discovery, and retrieval of learned associations between extremely large numbers of attributes in real time. At a basic level, an associative memory stores information about how attributes and their respective features occur together. The predictive power of the associative memory technology comes from its ability to interpret and analyze these co-occurrences and to produce various metrics. Associative memory is built through "experiential" learning in which each newly observed state is accumulated in the associative memory as a basis for interpreting future events. Thus, by observing normal system operation over time, and the normal predicted system ( i.e. modified events ) operation over time, the associative memory is able to learn normal patterns as a basis for identifying non-normal behavior ( i.e. exceptional events ) and appropriate responses, and to associate patterns with particular outcomes, contexts or responses.” Nasle [P.0080]. The predicted system is interpreted as modified events because “the virtual system models described herein are updated and calibrated with the real-time system operational data to provide better predictive output values.” Nasle [P.0079] and Applicant’s disclosure “each modified event record being generated by modifying the monitored attribute of at least one of the variables of one of the monitored event records” Spec. [Pg.2 Ln.19]. Also, non-normal behavior is interpreted as exceptional events due to Applicant’s disclosure “an extraction module configured to extract one or more of the plurality of monitored event records as exceptional event records in dependence on a determination of whether the monitored values satisfy respective regularity conditions” Spec. [Pg.2 Ln.15]) , the selection module being configured to select the subset of contingency event records from the one or more irregular patterns and the one or more modified patterns (“a contingency event can be chosen out of a diverse list of contingency events to be evaluated. That is, the operational stability of the electrical power system can be assessed under a number of different contingency event scenarios including but not limited to a singular event contingency or multiple event contingencies (that are simultaneous or sequenced in time). In one embodiment, the contingency events assessed are manually chosen by a system administrator in accordance with user requirements. In another embodiment, the contingency events assessed are automatically chosen in accordance with control logic that is dynamically adaptive to past observations of the electrical power system.” Nasle [P.0186]). Claim 18 recites substantially the same subject matter as claim 1 and is rejected under similar rationale. Claim 19 recites substantially the same subject matter as claim 1 and is rejected under similar rationale. Additionally, Nasle further discloses A non-transitory, computer-readable storage medium having instructions stored thereon that, when executed by a computer, cause the computer to carry out the method of claim 18 (“The systems and methods described herein can be specially constructed for the required purposes [ ] or it may be a general purpose computer selectively activated or configured by a computer program stored in the computer.” Nasle [P.0269]) Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Nasle et al. EP Patent No. 2038782 B1 (hereinafter referred to as “ Nasle ”) in view of Li et al. US Pub. No. 20190317952 A1 (hereinafter referred to as “ Li ”), in further view of Chan, Chi Kin, et al. "Tourism forecast combination using the CUSUM technique." Tourism Management 31.6 (2010): 891-897 (hereinafter referred to as “ Chan ”). Regarding claim 7 , Nasle in view of Li disclose A contingency forecasting system according to claim 5, Nasle further discloses wherein the selection module is configured to select the subset of contingency event records based on a weighted sum of the risk factors for each of the event records in the set of event records . (“a contingency event can be chosen out of a diverse list of contingency events to be evaluated. That is, the operational stability of the electrical power system can be assessed under a number of different contingency event scenarios including but not limited to a singular event contingency or multiple event contingencies (that are simultaneous or sequenced in time). In one embodiment, the contingency events assessed are manually chosen by a system administrator in accordance with user requirements. In another embodiment, the contingency events assessed are automatically chosen in accordance with control logic that is dynamically adaptive to past observations of the electrical power system. That is the control logic "learns" which contingency events to simulate based on past observations of the electrical power system operating under various conditions.” Nasle [P.0186]) Nasle fails to specifically disclose utilizing a weighted sum when selecting contingency event records. However, Chan discloses weighted sum (“This paper further investigates the impact of forecast combination on forecast accuracy [ ] by applying the quadratic programming approach to determine the combination weights for the individual forecasts.” Chan [Pg.892 Col.1 P.2]) Chan is analogous art as it relates to forecasting and its relevance to decision making. Chan discloses “This paper further investigates the impact of forecast combination on forecast a ccuracy [ ] to determine the combination weights for individual forecasts” Chan [Abstract]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nasle to include a weighted sum, as Chan discloses, when selecting contingency events based on risk factors in order “to improve forecasting accuracy in practice.” Chan [Abstract]. Regarding claim 8 , Nasle in view of Li and Chan disclose A contingency forecasting system according to claim 7, Nasle further discloses, wherein the selection module is configured to select the subset of contingency event records by comparing the weighted sum of the risk factors of each of the event records in the set of event records to a threshold value . (“Analytics engine 118 can be configured to compare predicted data based on the virtual system model 512 with real-time data received from data acquisition system 202 and to identify any differences [ ] the differences will indicate a failure, or the potential for a failure ( i.e. risk factors ).” Nasle [P.0098-99]. The analytics engine is interpreted to compare risk factors to a threshold because “where a determination is made as to whether the difference between the real-time data output and the predicted system data falls between a set value and an alarm condition value” Nasle [P.0109]) Nasle fails to specifically disclose comparing a weighted sum to a threshold value. However, Chan discloses weighted sum (“This paper further investigates the impact of forecast combination on forecast accuracy [ ] by applying the quadratic programming approach to determine the combination weights for the individual forecasts.” Chan [Pg.892 Col.1 P.2]) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modif