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 .
Acknowledgments
Applicant elected claims 1-27 and 30 in response to election/restriction requirement mailed 9/30/2025.
Claims 28, 29, and 31 were not elected.
Applicant provided information disclosure statement.
Claims 1-27 and 30 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-27 and 30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 19, and 30, the claims recite a risk model but then recite the climate model, it is unclear if they are the same model or not. For examining purposes, the Examiner has treated the risk model and climate model to be the same.
Regarding claim 1, the claims recite integrated risk data structures, but then recite integrated climate risk data structures. It is unclear if they are the same structure or not. For examining purposes, the Examiner has treated the two structures as the same.
Claim 14 recites the limitation " the machine learning system and the expert judgement system.” There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation " the structured expert judgement system.” There is insufficient antecedent basis for this limitation in the claim.
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 1-27 and 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-27 and 30 are directed to non-transitory computer-readable medium, system, and method.
Regarding step 2A-1, Claims 1-27 and 30 recite a Judicial Exception. Exemplary independent claim 1 and similarly claims 19 and 30 recite the limitations of
…a risk model comprising a causal graph of nodes for risk factors and a knowledge graph defining an extracted relationship of the nodes, each node storing a quantitative uncertainty value derived for a time horizon, the causal graph having edges connecting the nodes to create scenario paths for the risk model, the knowledge graph of the nodes defining a network structure with links between nodes…generate integrated risk data structures for a plurality of macro risk factors, wherein the integrated risk data structures map the plurality of macro risk factors to geographic space and time; populate data…by computing values for the plurality of macro risk factors for the time horizon using the integrated climate risk data structures, the values computed by a convolution of micro risk factor distributions to generate distribution measurements for the plurality of macro risk factors; generate multifactor scenario sets using the distribution measurements for the plurality of macro risk factors and the scenario paths for the climate model to compute the likelihood of different scenario paths for the climate model, the multifactor scenario sets representing combinations of the macro risk factors over a time horizon; generate risk metrics using the multifactor scenario sets and the knowledge graph; transmit at least a portion of the risk metrics and the multifactor scenario sets in response to queries…transmit queries…to generate visual elements at least in part corresponding to the multifactor scenario sets and the risk metrics received in response to the queries.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of generating, populating, computing, and transmitting data. The claim limitations fall under the abstract idea grouping of a mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of computing device with a hardware processor, the claim language encompasses simply generating data structures, populating data by computing values, generating scenario sets, computing additional values with respect to the scenarios sets, generating risk metrics, transmitting risk metrics/queries, and generating visuals with respect to risk metrics and scenarios. These are mere data manipulation steps that a user can do with pen and paper. For example, a user is able to compute values and generate data sets by using a pen and paper. These are merely data manipulation steps that do not require a computer. The computer components are merely a tool to carry out the mental process steps.
The claims also recite risk metrics, risk data structures, and financial impact which are with respect to a business as seen para 00271 and 00272. These make the claims fall in the abstract idea grouping of certain methods of organizing human activity (fundamental economic principles or practices; business relations, risk mitigation). It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components do not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements of non-transitory memory, computer system, hardware processor, computer device, client application, and non-transitory computer readable medium.
These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component. The additional elements also state insignificant extra solution activity such as non-transitory memory storing a risk model and store the integrated risk data structures and the multifactor scenario sets in the non- transitory memory. 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.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, 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.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe additional steps to determine micro risk factor distributions such as using Monte Carlo simulations. In addition, the dependent claims further describe what the risks relate to such as a financial impact.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Computer system, non-transitory memory, computer device, hardware processor, client application
Claim 3 and 21 recite monte carlo simulation
Claims 14 and 16 recite expert judgment system
Claim 17 recites machine learning
Claim 19 recites method, however method is not considered an additional element.
Claim 19 further recites non-transitory memory, hardware processor, client application, computer models.
Claim 30 recites non-transitory computer readable medium, hardware processor, client application, non-transitory memory.
The additional elements also include limitations such as non-transitory memory storing a risk model and store the integrated risk data structures and the multifactor scenario sets in the non- transitory memory. These limitations are seen as insignificant extra-solution activities that are considered as well‐understood, routine, and conventional functions. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions which include 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
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states general purpose computer configurations as seen in para 00303-00304.
When looking at the additional elements in combination, the Applicant’s specification merely states general purpose computer configurations as seen in para 00303-00304. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-27 and 30 are rejected under 35 U.S.C. 101.
Allowable Subject Matter
Claims 5 and 23 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Zhu (US20040015376A1) in further view of Crabtree (US20210019674A1) in further view of Field (US20170220967A1) in further view of Shih (20190370708) who teaches carbon asset risk management. However, with respect to exemplary claims 5 and 23, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 5 and 23.
Claims 6 and 24 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Zhu (US20040015376A1) in further view of Crabtree (US20210019674A1) in further view of Field (US20170220967A1) in further view of Nagaoka (US20190026698A1) who teaches target values with respect to risks. However, with respect to exemplary claims 6 and 24, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 6 and 24.
Claims 10, 11, 15, and 18 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Zhu (US20040015376A1) in further view of Crabtree (US20210019674A1) in further view of Field (US20170220967A1) in further view of Mun (US20150088783A1) who teaches probability distributions with respect to a forward method as well as extreme value theory. However, with respect to exemplary claims 10, 15, and 18, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 10, 15, and 18.
Claims 12 is allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Zhu (US20040015376A1) in further view of Crabtree (US20210019674A1) in further view of Field (US20170220967A1) in further view of Joshi (11797318) who teaches forward edges in a graph. However, with respect to exemplary claim 12, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 12.
Claims 13 is allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Zhu (US20040015376A1) in further view of Crabtree (US20210019674A1) in further view of Field (US20170220967A1). However, with respect to exemplary claim 13, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 13.
Claims 14, 16, and 17 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Zhu (US20040015376A1) in further view of Crabtree (US20210019674A1) in further view of Field (US20170220967A1) in further view of Smith (US20180101795A1) who teaches expert judgment with respect to causal influence relationships. However, with respect to exemplary claims 14, 16, and 17, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 14, 16, and 17.
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, 2, 3, 4, 7, 8, 9, 19, 20, 21, 22, 25, 26, 27, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu (US20040015376A1) in further view of Crabtree (US20210019674A1) in further view of Field (US20170220967A1).
Regarding claim 1, and similarly claims 19 and 30, Zhu teaches
A computer system…non-transitory memory…a hardware processor with a communication path to the non-transitory memory to (See abstract-A method and system which deal with the evaluation of the impact of political risk on forecast and value of a project.) This teaches a system. (See para 0118-FIG. 16 schematically illustrates a hardware environment of an embodiment of the present invention. A computer system 1000 has a server 1002 in communication with a storage device 1004 and a central processing unit 1006. ) This teaches a processor and memory of the system.
A computer method (See abstract-A method and system which deal with the evaluation of the impact of political risk on forecast and value of a project.) This teaches a method.
Non-transitory computer readable medium storing instructions…which when executed by a hardware processor (See para 0118-FIG. 16 schematically illustrates a hardware environment of an embodiment of the present invention. A computer system 1000 has a server 1002 in communication with a storage device 1004 and a central processing unit 1006. ) This teaches a processor and memory of the system.
…a risk model comprising a causal graph of nodes for risk factors (See fig. 1) This shows a risk model by the system that shows a causal graph with nodes as risk categories.
…the causal graph having edges connecting the nodes to create scenario paths for the risk model (See fig. 1) This shows the causal with edges that connect the nodes which make scenario paths. For example, this shows the risk of war/terrorism/labor leads to a scenario of physical disruption of the project.
Even though Zhu teaches risk model that includes a causal graph, it is unclear if it is stored, however another section of Zhu teaches storing (See para 0115-The results of the risk analysis can be stored in the computer (see FIGS. 6-11).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the causal graph also stored in the art of Zhu. For example, the graph can be stored in the user interface of Zhu so the user can see the causal graph. This would give the user more insight in how the nodes are connected and which nodes belong to macro risk categories and which nodes do not. This makes the art of Zhu more useful to the user by allowing the user to see more detailed information.
In addition, even though Zhu teaches a causal graph, it is not clear that Zhu teaches a separate knowledge graph, however Crabtree teaches a knowledge graph defining an extracted relationship of the nodes, each node storing a quantitative uncertainty value derived for a time horizon…the knowledge graph of the nodes defining a network structure with links between nodes/with weights (See para 0127-After the ontological databases have been created and/or updated, a directed computational graph module 155 utilizing the ontologies generates a knowledge graph. A GraphStack service 145 identifies subgraphs of interest (from the knowledge graph), returns the subgraphs of interest) (See fig. 32) This shows a knowledge graph and knowledge subgraph, the knowledge subgraph takes an extraction of nodes from the knowledge graph. The knowledge graph and subgraph are with respect to a time horizon since it deals with temporal data (See para 0057-Additionally, the knowledge graphs generated are not only weighted and directional but attribute temporal and spatial context which may be viewed as independent time-slices, geospatial-slices, or temporospatial-slices). In addition, the nodes of the knowledge graph have uncertainty values/weights which correspond to inferred risk they contain (See para 0132-As another example, the system may be configured to show the inferred risk as differing diameters of vertices.)
Zhu and Crabtree are analogous art because they are from the same problem-solving area of determining risks and both belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Zhu’s invention by incorporating the method of Crabtree because Zhu could also incorporate risk values with respect to the vertices of the causal graph. This would give the user of Zhu more insight into the nodes by showing how important one risk factor is to another. Zhu’s system can use this data to determine how a project would be affected by giving the different risk values for the nodes in the causal graph.
In addition, Zhu teaches generate integrated risk data structures for a plurality of macro risk factors, wherein the integrated risk data structures map the plurality of macro risk factors to geographic space and time (See figures 3A-3E) This shows the system generates a risk data structures (i.e. graphs) for the macro risk factors seen from figure 1. (See para 0065-FIGS. 3A-3E illustrate the resultant cumulative probabilities for the five macro risk categories (as defined in the embodiment illustrated) for various countries over the five-year period starting from Q1, 2001) These map the macro risk factors to geographic space of country and time.
populate data in the memory by computing values for the plurality of macro risk factors for the time horizon using the integrated climate risk data structures (See fig. 3A-3E) This shows that the system computes values for the macro risk factors using the graphs (i.e. risk data structures). These are populated in memory since they are shown to the user on their device’s interface where they can choose the different macro risk factors from the drop-down menu. The values are with respect to time.
However, even though Zhu teaches macro risk categories and a blend of micro risk categories (i.e. project specific political risk events), it is not clear that the values for the macro risk event categories come from a distribution of the micro risk events, however Field teaches the values computed by a convolution of micro risk factor distributions to generate distribution measurements for the plurality of macro risk factors (See para 0097-The system is designed so that the “Macro” and “Mid” level assessments are made based on the data that is drawn from the “Micro” risk assessments ) This teaches that the macro risk values (i.e. overall risk for each excursion) is made by a blend of micro risk values. (See fig. 4F)
Zhu and Field are analogous art because they are from the same problem-solving area of determining risks and both belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Zhu’s invention by incorporating the method of Field because Zhu could also determine macro risk category values from the micro risk values. This would provide Zhu another way to determine an overall risk score for the project in addition what is already seen in para 0121 of Zhu. This would make the system of Zhu more sophisticated in determining risk values.
Zhu further teaches generate multifactor scenario sets using the distribution measurements for the plurality of macro risk factors and the scenario paths for the climate model to compute the likelihood of different scenario paths for the climate model, the multifactor scenario sets representing combinations of the macro risk factors over a time horizon; (See para 0107- A probabilistic assessment is conducted, block 136, FIG. 2B. In the preferred embodiment, the Microsoft excel program and the “Crystal Ball” add-in (sold by Decisioneering, Inc) are used to simulate all possible political outcomes, based on the probabilities previously inputted. FIG. 13 provides an illustration of this iterative process and the processes involved in deriving the risked and unrisked project economic value. After the macro political risk quantifications, conditional risk probabilities of project manifestations, and impacts to economic parameters are defined, the simulation can begin. First the number of desired iterations is determined, block 150. For each iteration of the simulation, one possible instance of a political risk scenario is generated, block 152, and changes if any, to the economic parameters for all the years of the project are determined, block 154… When the simulation is finished, the overall risked project value matrices will be estimated using the recorded results of the individual iterations, block 160…The simulation is repeated until the predetermined number of scenarios is reached, block 158. In a preferred embodiment, the simulation run should have 3,000 or more iterations to generate results that are considered statistically stable.) This shows that scenario sets are determined based on the probabilities of the macro risk sets that are determined (i.e. distribution measurements of risk factors) like those in figures 3A-3E which are respect to time and represent the risk model. The scenarios deal with paths as seen in fig. 14 (See para 0108- In essence, this invention allows one to investigate the project over each possible distinctive path to derive a compressive view of how the project value will likely be impacted by political uncertainties.) The scenarios also deal with likelihood since the inputs are probability values.
generate risk metrics using the multifactor scenario sets (See para 0107- A probabilistic assessment is conducted, block 136, FIG. 2B. In the preferred embodiment, the Microsoft excel program and the “Crystal Ball” add-in (sold by Decisioneering, Inc) are used to simulate all possible political outcomes, based on the probabilities previously inputted. FIG. 13 provides an illustration of this iterative process and the processes involved in deriving the risked and unrisked project economic value. After the macro political risk quantifications, conditional risk probabilities of project manifestations, and impacts to economic parameters are defined, the simulation can begin. First the number of desired iterations is determined, block 150. For each iteration of the simulation, one possible instance of a political risk scenario is generated, block 152, and changes if any, to the economic parameters for all the years of the project are determined, block 154… When the simulation is finished, the overall risked project value matrices will be estimated using the recorded results of the individual iterations, block 160) This shows that risk metrics such as risk matrices are determined from the scenarios being run.
However Zhu doesn’t teach a separate knowledge graph to determine the risk metrics, however Crabtree teaches (as already taught above)e) generate risk metrics using…knowledge graph (See para 0132- As another example, the system may be configured to show the inferred risk as differing diameters of vertices.) This shows risk metrics by the way of diameter of the nodes.
Zhu and Crabtree are analogous art because they are from the same problem-solving area of determining risks and both belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Zhu’s invention by incorporating the method of Crabtree because Zhu could also incorporate risk values with respect to the vertices of the causal graph. This would give the user of Zhu more insight into the nodes by showing how important one risk factor is to another. Zhu’s system can use this data to determine how a project would be affected by giving the different risk values for the nodes in the causal graph.
Zhu further teaches transmit at least a portion of the risk metrics and the multifactor scenario sets in response to queries by a client application; Examiner interprets this to mean the results for the project risks that include running the simulation of scenarios are transmitted for the user to see with respect to the user querying a client application such as by inputting values in Microsoft excel to determine the project risks (See para 0038- These values are inputted to a predetermined economic model/computer program, (e.g., an Excel spreadsheet model)… This analysis can be done by using a spreadsheet program such as Microsoft Excel in conjunction with a statistical program, such as “Crystal Ball”, sold as an add-in for Excel by Decisioneering, Inc.).
store the integrated risk data structures and the multifactor scenario sets in the non- transitory memory; Zhu teaches this since the risk data structures of fig. 3A-3E are stored on the system since the user is able to view these graphs on the user device’s interface. (See fig. 15) This teaches the results of the simulation of scenarios are stored in the chart for a manager to see. This chart is stored on the computer. (See para 0113- FIG. 15 illustrates the results of the Monte Carlo simulation and the results of the sensitivity analysis for the illustrated embodiment.) (See para 0115- The results of the risk analysis can be stored in the computer (see FIGS. 6-11).)
Zhu further teaches and a computer device with a hardware processor having the client application to transmit queries to the hardware processor (See para 0038- These values are inputted to a predetermined economic model/computer program, (e.g., an Excel spreadsheet model)… This analysis can be done by using a spreadsheet program such as Microsoft Excel in conjunction with a statistical program, such as “Crystal Ball”, sold as an add-in for Excel by Decisioneering, Inc.). (See para 0117- In the system of the present invention, any suitable data processing system can be employed such as a computer which preferably has an input device, central processing unit, an output device, and a storage device.) This teaches a computer and the client can transmit queries via inputting data into Microsoft excel to determine risks for a project.
and an interface to generate visual elements at least in part corresponding to the multifactor scenario sets and the risk metrics received in response to the queries. (See fig. 15) (See para 0113- FIG. 15 illustrates the results of the Monte Carlo simulation and the results of the sensitivity analysis for the illustrated embodiment. The worth of the unrisked project is estimated to be $470 million, but that value is reduced by $240 million after incorporating political uncertainties to produce a risked value of $230 million, item 902. In addition, graph 904 shows how each project specific event risk affects the value of the project as a result of the sensitivity analysis. Each project risks 906 are provided on the Y-axis, and the dollar amount in millions is shown along the X-axis. ) This shows interface to display a visual with respect to risks and the scenario sets that are simulated.
Regarding claims 2 and 20, Zhu, Field, and Crabtree teach the limitations of claims 1 and 19, however Zhu further teaches
wherein the hardware processor computes the convolution of the micro risk factor distributions using simulations (See fig. 15 and para 0113- FIG. 15 illustrates the results of the Monte Carlo simulation and the results of the sensitivity analysis for the illustrated embodiment. ) This shows specific project risk events (i.e. micro risk factors) are determined for their risk using simulations.
wherein the micro risk factor distributions correspond to a plurality of micro variables for the macro risk factors. The micro risks (i.e. project specific risk events) corresponds to macro risks as seen here (See table 6).
Regarding claims 3 and 21, Zhu, Field, and Crabtree teach the limitations of claims 2 and 20, however Zhu further teaches
wherein the simulation is based on a Monte Carlo simulation. (See fig. 15 and para 0113- FIG. 15 illustrates the results of the Monte Carlo simulation and the results of the sensitivity analysis for the illustrated embodiment. )
Regarding claims 4 and 22, Zhu, Field, and Crabtree teach the limitations of claims 1 and 19, however Zhu further teaches
wherein each macro risk factor comprises of a set of micro risk factors having corresponding micro risk factor distributions over time (See table 6) This shows each macro risk factor comprises micro risk factors (i.e. project specific risk events). These are with respect to time such as time frames (See para 0090- In the illustrated embodiment, the macro political risk and the project specific political risk events are related to the projected time frames in which the manifestations would be applicable.)
wherein the processor computes a distribution measurement for the respective of macro risk factor (See figure 3A-3E) This shows distribution measurements for the macro risk factors.
However, even though Zhu teaches macro risk categories and a blend of micro risk categories (i.e. project specific political risk events), it is not clear that the values for the macro risk event categories come from a distribution of the micro risk events, however Field teaches
using a convolution of the micro risk factor distributions. (See para 0097-The system is designed so that the “Macro” and “Mid” level assessments are made based on the data that is drawn from the “Micro” risk assessments ) This teaches that the macro risk values (i.e. overall risk for each excursion) is made by a blend of micro risk values. (See fig. 4F)
Zhu and Field are analogous art because they are from the same problem-solving area of determining risks and both belong to G06Q10 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Zhu’s invention by incorporating the method of Field because Zhu could also determine macro risk category values from the micro risk values. This would provide Zhu another way to determine an overall risk score for the project in addition what is already seen in para 0121 of Zhu. This would make the system of Zhu more sophisticated in determining risk values.
Regarding claims 7 and 25, Zhu, Field, and Crabtree teach the limitations of claims 1 and 19, however Zhu further teaches
wherein the interface has a visualization depicting climate risk ratings of a financial impact of a stress scenario on an asset. (See para 0113- FIG. 15 illustrates the results of the Monte Carlo simulation and the results of the sensitivity analysis for the illustrated embodiment. The worth of the unrisked project is estimated to be $470 million, but that value is reduced by $240 million after incorporating political uncertainties to produce a risked value of $230 million, item 902.). (See fig. 15) This shows risk ratings for the asset (i.e. project) with respect to a financial impact from the risk stress to the project. The risk stress representing the stress scenario.
Regarding claims 8 and 26, Zhu, Field, and Crabtree teach the limitations of claims 1 and 19, however Zhu further teaches
wherein the processor generates forward looking uncertainty distributions for each of the macro risk factors, in each geography, at each time horizon. (See fig. 3A-3E) This shows forward distributions for the macro risk factors for plurality of regions and time horizons. These are forward distributions since it goes from 2001 to five years ahead. (See para 0065- FIGS. 3A-3E illustrate the resultant cumulative probabilities for the five macro risk categories (as defined in the embodiment illustrated) for various countries over the five-year period starting from Q1, 2001: “).
Regarding claims 9 and 27, Zhu, Field, and Crabtree teach the limitations of claims 1 and 19, however Zhu further teaches
wherein the processor generates a transition scenario for a macro risk factor as a selection of the macro risk factor in a given location repeated over each time period or horizon. (See para 0046- This may be outputted as shown in FIG. 15. Since political risks can be very fluid, the assumptions can be revisited and periodically updated to obtain the latest risked value of the project, block 144. The above overview is more fully explained below.) Examiner interprets the transition scenario to be when the scenarios are updated with new data. The risk analysis would be run again and updated with the new data (i.e. risk factor repeated over a time period/horizon).
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Shih (20190370708) who teaches carbon asset risk management.
Nagaoka (US20190026698A1) who teaches target values with respect to risks
Mun (US20150088783A1) who teaches probability distributions with respect to a forward method as well as extreme value theory.
Joshi (11797318) who teaches forward edges in a graph.
Smith (US20180101795A1) who teaches expert judgment with respect to causal influence relationships.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUSTAFA IQBAL whose telephone number is (469)295-9241. The examiner can normally be reached Monday Thru Friday 9:30am-7:30 CST.
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/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625