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
Examiner acknowledges Applicant’s claim to priority regarding 63/815,355 filed on 01/07/2024.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 01/07/2025 has been fully considered.
Claim Objections
Claims 8 and 14 are objected to because of the following informalities: Examiner suggests amending the claim for the sake of clarity by reciting “comprising at least one of an indication of a probable disruptive event, an intrastate stability score, or an interstate stress score;”
Claim 12 is objected to because of the following informalities: Examiner suggests amending the claim for the sake of clarity by reciting “The method of Claim 1, wherein identifying the pattern and trend comprises identifying variables that trend together, an order in which the identified variables move, and a velocity of movement of each of the respective identified variables in relation to others of the identified variables.”
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 1-20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-13 are directed to a method, claims 14-19 are directed to a system, and claim 20 is directed to a non-transitory computer-readable medium. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Independent claims 1, 14, and 20 recite identifying a pattern and trend to forecast risk, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to fundamental economic principles or practices including mitigating risk. Independent claim 1 recites limitations, similarly recited in claims 14 and 20, including “update a risk assessment dataset comprising a series of risk assessment states for a plurality of geopolitical entities in time increments; selecting forecasting risk assessment data from the risk assessment dataset for assessing risk for a selected time increment and a first entity from the plurality of geopolitical entities; processing the forecasting risk assessment data to identify a pattern and trend in the forecasting risk assessment data and use the pattern and trend to forecast risk assessment variable values for the first entity to generate forecasted risk assessment variable data, wherein the comprises a forecasted value for at least one indicator geopolitical risk to the first entity.” Claims 14 and 20 further recite the limitation of “the forecasted risk assessment variable values comprising at least one of an indication of a probable disruptive event, an intrastate stability score, an interstate stress score.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “inquisitive artificial intelligence engine,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “inquisitive artificial intelligence engine,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “inquisitive artificial intelligence engine” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 2-9, 12-13, and 17-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 10-11 and 15-16 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 14, and 20 do not integrate the judicial exception into a practical application. Independent claim 1 is method that recites “a method for artificial intelligence (AI)-based global geopolitical risk assessment and warning, the method comprising,” which is recited in the preamble of the claim. Independent claim 14 is a system comprising “a processor; a volatile memory coupled to the processor; a second memory coupled to the processor storing: a granular dynamic risk assessment dataset collected from a worldwide network of data sources a series of risk assessment states for a plurality of geopolitical entities in time increments; code executable to provide an inquisitive artificial intelligence engine, comprising computer-executable instructions executable by the processor for” and “loading into the volatile memory.” Independent claim 20 recites “a non-transitory, computer-readable medium embodying thereon computer-executable code, the computer-executable code comprising instructions executable for” within the preamble of the claim. Independent claim 1 recites “continuously collecting cross-sector data related to geopolitical risk from a worldwide network of data sources to update a risk assessment dataset.” Independent claim 1 recites the additional element, similarly recited in claims 14 and 20, of “dynamically generating a user interface comprising an interactive map for a user, the interactive map embodying the forecasted risk assessment variable values to display a forecasted risk for the first entity to the user in association with the first entity in the user interface.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Independent claims 1, 14, and 20 further recite the additional element of “processing the forecasting risk assessment data using an inquisitive artificial intelligence engine to identify a pattern and trend.” The limitations reciting “using an inquisitive artificial intelligence engine” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-9, 12-13, and 17-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 10 further recites the additional element of “receiving new variable values for the selected time increment, the new variable values extracted or derived from new input data from the worldwide network of computers; and automatically relearning, by the inquisitive AI engine, the pattern and trend using the new variable values.” Dependent claim 11 further recites the additional element of “further comprising the inquisitive AI engine spontaneously generating a query to a data source to collect additional risk assessment data.” Dependent claim 15 further recites the additional element of “wherein the code further comprises instructions executable for generating an automatic notification of geopolitical risk based on the forecasted risk assessment variable values.” Dependent claim 16 further recites the additional element of “wherein the inquisitive artificial intelligence engine is executable to observe dynamic movement of variables across sectors to recognize the pattern and trend.” The limitations reciting “using an inquisitive artificial intelligence engine” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the dependent claims, when considered both individually and in the context of the dependent claims, do not integrate the judicial exception into a practical application.
Step 2B: Independent claims 1, 14, and 20 do not comprise anything significantly more. Independent claim 1 is method that recites “a method for artificial intelligence (AI)-based global geopolitical risk assessment and warning, the method comprising,” which is recited in the preamble of the claim. Independent claim 14 is a system comprising “a processor; a volatile memory coupled to the processor; a second memory coupled to the processor storing: a granular dynamic risk assessment dataset collected from a worldwide network of data sources a series of risk assessment states for a plurality of geopolitical entities in time increments; code executable to provide an inquisitive artificial intelligence engine, comprising computer-executable instructions executable by the processor for” and “loading into the volatile memory.” Independent claim 20 recites “a non-transitory, computer-readable medium embodying thereon computer-executable code, the computer-executable code comprising instructions executable for” within the preamble of the claim. Independent claim 1 recites “continuously collecting cross-sector data related to geopolitical risk from a worldwide network of data sources to update a risk assessment dataset.” Independent claim 1 recites the additional element, similarly recited in claims 14 and 20, of “dynamically generating a user interface comprising an interactive map for a user, the interactive map embodying the forecasted risk assessment variable values to display a forecasted risk for the first entity to the user in association with the first entity in the user interface.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., mental process or certain methods of organizing human activity) is not anything significantly more. See MPEP 2106.05(f).
Independent claims 1, 14, and 20 further recite the additional element of “processing the forecasting risk assessment data using an inquisitive artificial intelligence engine to identify a pattern and trend.” The limitations reciting “using an inquisitive artificial intelligence engine” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., mental process or certain methods of organizing human activity) is not anything significantly more. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more.
Dependent claims 2-9, 12-13, and 17-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more.
Dependent claim 10 further recites the additional element of “receiving new variable values for the selected time increment, the new variable values extracted or derived from new input data from the worldwide network of computers; and automatically relearning, by the inquisitive AI engine, the pattern and trend using the new variable values.” Dependent claim 11 further recites the additional element of “further comprising the inquisitive AI engine spontaneously generating a query to a data source to collect additional risk assessment data.” Dependent claim 15 further recites the additional element of “wherein the code further comprises instructions executable for generating an automatic notification of geopolitical risk based on the forecasted risk assessment variable values.” Dependent claim 16 further recites the additional element of “wherein the inquisitive artificial intelligence engine is executable to observe dynamic movement of variables across sectors to recognize the pattern and trend.” The limitations reciting “using an inquisitive artificial intelligence engine” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., mental process or certain methods of organizing human activity) are not anything significantly more. See MPEP 2106.05(f).
Therefore, the dependent claims, when considered both individually and in the context of the dependent claims, are not anything significantly more.
Accordingly, claims 1-20 are rejected under 35 USC 101.
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.
Claim(s) 14-15, 18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sarkar (US 20230077527 A1).
Regarding claim 14, Sarkar anticipates a system for artificial intelligence (AI)-based global geopolitical risk assessment and warning (Fig. 52), the system comprising:
a processor (Fig. 52 and [0233-0234] teach a computer comprising a processor and memory);
a volatile memory coupled to the processor (Fig. 52 and [0233-0234] teach a computer comprising a processor and memory, wherein the memory section includes a flash memory card, wherein the media drive unit can read/write a computer-readable medium that contains programs and databases; see also: [0152]);
a second memory coupled to the processor storing (Fig. 52 and [0233-0234] teach a computer comprising a processor and memory, wherein the memory section includes a flash memory card, wherein the media drive unit can read/write a computer-readable medium that contains programs and databases; see also: [0152]):
a granular dynamic risk assessment dataset collected from a worldwide network of data sources a series of risk assessment states for a plurality of geopolitical entities in time increments ([0178] teaches risk parameter data is collected from both end-user device management systems and IoT gateways, wherein the risk parameter related data can be plugged into the systems to collect and summarize the data at frequent/periodic intervals, wherein [0087] teaches the risk identification, quantification, and mitigation engine can collect and analyze data including global organizations data including multiple jurisdictions data, local business environment data, geo political data, as well as multiple risk category data, wherein Fig. 6 and [0115] teach the risk scores can be calculated according to assessment results for a given period, such as making comparisons with the same week of a previous month, and/or same month/quarter of a previous year, wherein [0151] teaches after collecting the risk information on a specified basis, such as at a specific period, the collected data can be pushed onto the risk management hardware device, wherein the device serves as a repository for all the risk parameters for the enterprise asset, wherein [0155] teaches on a periodic basis, the local risk information agent uses a risk management hardware device to write the parameters that it has collected from the external hardware and software components in a secure manner, wherein the data can be written onto the internal memory and the data is analyzed, as well as in [0201] teaches a computerized process that provides risk model solutions to organizations across multiple industries, including financial services, healthcare, and retail, with a particular focus on cyber, data privacy and compliance risk, wherein the computerized process can be enabled in real time and continuous quantification of risk, and wherein [0159] teaches gathering data over a network, as well as in [0064] teaches utilizing a software network including remote servers; see also: [0196]);
code executable to provide an inquisitive artificial intelligence engine (Fig. 9 and [0126] teach the risk identification, quantification, and mitigation engine delivery platform includes multiple modules, wherein [0087] teaches the risk identification, quantification, and mitigation engine can collect and analyze data including global organizations data including multiple jurisdictions data, local business environment data, geo political data, as well as multiple risk category data, wherein Fig. 6 and [0115] teach the risk scores can be calculated according to assessment results for a given period, such as making comparisons with the same week of a previous month, and/or same month/quarter of a previous year, wherein [0151] teaches after collecting the risk information on a specified basis, such as at a specific period, the collected data can be pushed onto the risk management hardware device, wherein the device serves as a repository for all the risk parameters for the enterprise asset, wherein [0155] teaches on a periodic basis, the local risk information agent uses a risk management hardware device to write the parameters that it has collected from the external hardware and software components in a secure manner, wherein [0095] teaches based on the client’s inputs, the AI engine can calculate the risk score, as well as in [0133] teaches the risk calculation engine can take inputs from multiple disparate sources, intelligently analyze, and present the organizational risk exposure, as well as in [0168] teaches extracting data from an input questionnaire; see also: [0099, 0159, 0162]), comprising computer-executable instructions executable by the processor for:
receiving an indication to generate a forecast for a first entity and a first time increment ([0110] teaches the risk scores can determine the severity of the risk levels for an organization, wherein the risk scores can be calculated and displayed with a frequency that meets a specific client’s needs, wherein [0111] teaches the customer entity can provide basic information about the industry that the customer operates in, wherein based on the data collected from other customers in the same industry and customer size, the risk score can be calculated based on industry benchmarks, wherein [0112] teaches based on the needs of the industry and for the entity, the controls can be selected to be assessed for the customer based on their needs, wherein Fig. 6 and [0115] teach the risk scores can be calculated according to assessment results for a given period, such as making comparisons with the same week of a previous month, and/or same month/quarter of a previous year, wherein [0151] teaches after collecting the risk information on a specified basis, such as at a specific period, the collected data can be pushed onto the risk management hardware device, wherein the device serves as a repository for all the risk parameters for the enterprise asset, wherein [0087] teaches the risk identification, quantification, and mitigation engine can collect and analyze data including global organizations data including multiple jurisdictions data, local business environment data, geo political data, as well as multiple risk category data, as well as in [0159-0160] teach the process can explore the various metrics of specified industries, regulations, and systems and selects the right set of modules that would be relevant; see also: [0098, 0121, 0196, 0201]);
loading into the volatile memory, forecasting risk assessment data for a defined time increment, the risk assessment data comprising data indicative of geopolitical risk ([0110] teaches the risk scores can determine the severity of the risk levels for an organization, wherein the risk scores can be calculated and displayed with a frequency that meets a specific client’s needs, wherein [0111] teaches the customer entity can provide basic information about the industry that the customer operates in, wherein based on the data collected from other customers in the same industry and customer size, the risk score can be calculated based on industry benchmarks, wherein [0112] teaches based on the needs of the industry and for the entity, the controls can be selected to be assessed for the customer based on their needs, wherein Fig. 6 and [0115] teach the risk scores can be calculated according to assessment results for a given period, such as making comparisons with the same week of a previous month, and/or same month/quarter of a previous year, wherein [0151] teaches after collecting the risk information on a specified basis, such as at a specific period, the collected data can be pushed onto the risk management hardware device, wherein the device serves as a repository for all the risk parameters for the enterprise asset, wherein [0087] teaches the risk identification, quantification, and mitigation engine can collect and analyze data including global organizations data including multiple jurisdictions data, local business environment data, geo political data, as well as multiple risk category data, as well as in [0159-0160] teach the process can explore the various metrics of specified industries, regulations, and systems and selects the right set of modules that would be relevant; see also: [0098, 0121, 0196, 0201]);
analyzing by the inquisitive artificial intelligence engine the forecasting risk assessment to identify a pattern and trend in the forecasting risk assessment data and use the pattern and trend to forecast risk assessment variable values for the first entity to generate forecasted risk assessment variable values ([0079] teaches predictive analytics include finding patterns from data using mathematical models that predict future outcomes, wherein the predictive analytics include machine learning that analyzes current and historical facts to make predictions about the future or otherwise unknown events, wherein the predictive models exploit patterns found in historical data to identify risks and opportunities, wherein the models can capture relationships among many factors to allow assessment of risk associated with a set of conditions, as well as in [0115] teaches detecting anomalies in risk scores, wherein the seasonality of risk can be considered along with its patterns as the risk may just be following a pattern even if it has varied widely from the last period of assessment, wherein a machine learning model can be trained to detect patterns and predict the approximate risk score according to existing patterns in the data, wherein the RNN can be trained on different types of patterns, as well as in [0158] teaches summarizing risk data and presenting various scoring, exposure, and trends of the entire enterprise, wherein [0159-0160] teach the process can explore the various metrics of specified industries, regulations, and systems and selects the right set of modules that would be relevant, wherein the process can derive the impact, likelihood, and risk score of the metrics along with the anomalies, wherein the AI/ML can be applied for prediction steps in order to output a summarization of various risk categories and the highest level risk score for the company; see also: [0099, 0165-0166]),
the forecasted risk assessment variable values comprising at least one of an indication of a probable disruptive event ([0114] teaches calculating of a risk of exposure assessment using the machine learning and other input data in order to extrapolate the data to determine the risk exposure, wherein [0116] teaches the exposures may have hierarchical dependencies and the system can automatically identify non-compliance and generate a list of possible scenarios based on risk dependency, then bubble up the most likely scenarios for the user to view, as well as in [0136] teaches risks can be categorized by risk type, function, location, and more, wherein the system can identify and quantify the likelihood of exposure in terms of cost and remediation cost, wherein [0196] teaches the risk exposure may have specified categories including vendor partner data loss, web application attacks, or other risks; see also: Fig. 31, [0194, 0200, 0202]); and
dynamically generating a user interface comprising an interactive map for a user ([0097] teaches the risk identification, quantification, and mitigation engine includes a dashboard and other interactive modules, wherein Fig. 31 and [0194-0195] teach a risk geo-map that displays the underlying data in terms of risk exposure and renumeration cost at various locations across the world, wherein the size bubbles show the relative value of each risk exposure, wherein the overall risk exposure can show an aggregated risk that includes all the regions shown in the geo-map, wherein the chart can show multivariate risk data represented on its various axes, wherein the risk geo-map can be used for risk management administrator and can be updated in real time, wherein the risks can be aggregated and displayed in order to represent top risks to an organization, wherein [0115] teaches the visualizations can display predicted versus actual scores and alert users of anomalies, as well as in [0122] teaches the dashboard can allow users to aggregate and highlight risk as a risk score that can be drilled down for each of the models and then view risk at a model level, wherein the users can also drill down into the model to view risk at a more granular detail; see also: [0099, 0164]),
the interactive map embodying the forecasted risk assessment variable values to display a forecasted risk for the first entity to the user in association with the first entity in the user interface ([0097] teaches the risk identification, quantification, and mitigation engine includes a dashboard and other interactive modules, wherein Fig. 31 and [0194-0195] teach a risk geo-map that displays the underlying data in terms of risk exposure and renumeration cost at various locations across the world, wherein the size bubbles show the relative value of each risk exposure, wherein the overall risk exposure can show an aggregated risk that includes all the regions shown in the geo-map, wherein the chart can show multivariate risk data represented on its various axes, wherein the risk geo-map can be used for risk management administrator and can be updated in real time, wherein the risks can be aggregated and displayed in order to represent top risks to an organization, wherein [0115] teaches the visualizations can display predicted versus actual scores and alert users of anomalies, as well as in [0122] teaches the dashboard can allow users to aggregate and highlight risk as a risk score that can be drilled down for each of the models and then view risk at a model level, wherein the users can also drill down into the model to view risk at a more granular detail; see also: [0099, 0164]).
Regarding claim 20, the claim recites limitations already addressed by the rejection of claim 14. Regarding claim 20, Sarkar anticipates a non-transitory, computer-readable medium embodying thereon computer-executable code, the computer-executable code comprising instructions executable for (Fig. 52 and [0234-0236] teach a computer-readable medium that contains programs compatible with a computer system). Accordingly, claim 20 is rejected as being anticipated by Sarkar.
Regarding claim 15, Sarkar anticipates all the limitations of claim 14 above.
Sarkar further anticipates wherein the code further comprises instructions executable for generating an automatic notification of geopolitical risk based on the forecasted risk assessment variable values ([0123] teaches the risk identification, quantification, and mitigation engine delivery platform has a customizable notification framework that can proactively monitor the integrating system to identify anomalies and alert the organization, wherein the risk can be tracked over a period of time and the AI/ML capabilities can predict and highlight risk, wherein the platform can include alerting and notification framework that can customize messages and recipients, as well as in [0131] teaches automatically creating risk reports and outputting the notification framework to provide a visualization for inclusion on the dashboard, as well as in [0115] teaches alerting the user to the anomalies based on the risk scores; see also: [0138, 0160]).
Regarding claim 18, Sarkar anticipates all the limitations of claim 14 above.
Sarkar further anticipates wherein the defined time increment comprises a plurality of time increments prior to the first time increment ([0110] teaches the risk scores can determine the severity of the risk levels for an organization, wherein the risk scores can be calculated and displayed with a frequency that meets a specific client’s needs, wherein [0111] teaches the customer entity can provide basic information about the industry that the customer operates in, wherein based on the data collected from other customers in the same industry and customer size, the risk score can be calculated based on industry benchmarks, wherein [0112] teaches based on the needs of the industry and for the entity, the controls can be selected to be assessed for the customer based on their needs, wherein Fig. 6 and [0115] teach the risk scores can be calculated according to assessment results for a given period, such as making comparisons with the same week of a previous month, and/or same month/quarter of a previous year, wherein [0151] teaches after collecting the risk information on a specified basis, such as at a specific period, the collected data can be pushed onto the risk management hardware device, wherein the device serves as a repository for all the risk parameters for the enterprise asset, wherein [0087] teaches the risk identification, quantification, and mitigation engine can collect and analyze data including global organizations data including multiple jurisdictions data, local business environment data, geo political data, as well as multiple risk category data, as well as in [0159-0160] teach the process can explore the various metrics of specified industries, regulations, and systems and selects the right set of modules that would be relevant; see also: [0098, 0121, 0196, 0201]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3-10, 13, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sarkar (US 20230077527 A1) in view of Balli et al. ("Geopolitical risk spillovers and its determinants," 2022).
Regarding claim 1, Sarkar teaches a method for artificial intelligence (AI)-based global geopolitical risk assessment and warning (Figs. 1, 8, and 31), the method comprising:
continuously collecting cross-sector data related to geopolitical risk from a worldwide network of data sources to update a risk assessment dataset comprising a series of risk assessment states for a plurality of geopolitical entities in time increments ([0178] teaches risk parameter data is collected from both end-user device management systems and IoT gateways, wherein the risk parameter related data can be plugged into the systems to collect and summarize the data at frequent/periodic intervals, wherein [0087] teaches the risk identification, quantification, and mitigation engine can collect and analyze data including global organizations data including multiple jurisdictions data, local business environment data, geo political data, as well as multiple risk category data, wherein Fig. 6 and [0115] teach the risk scores can be calculated according to assessment results for a given period, such as making comparisons with the same week of a previous month, and/or same month/quarter of a previous year, wherein [0151] teaches after collecting the risk information on a specified basis, such as at a specific period, the collected data can be pushed onto the risk management hardware device, wherein the device serves as a repository for all the risk parameters for the enterprise asset, wherein [0155] teaches on a periodic basis, the local risk information agent uses a risk management hardware device to write the parameters that it has collected from the external hardware and software components in a secure manner, wherein the data can be written onto the internal memory and the data is analyzed, as well as in [0201] teaches a computerized process that provides risk model solutions to organizations across multiple industries, including financial services, healthcare, and retail, with a particular focus on cyber, data privacy and compliance risk, wherein the computerized process can be enabled in real time and continuous quantification of risk; see also: [0196]);
selecting forecasting risk assessment data from the risk assessment dataset for assessing risk for a selected time increment and a first entity from the plurality of geopolitical entities ([0110] teaches the risk scores can determine the severity of the risk levels for an organization, wherein the risk scores can be calculated and displayed with a frequency that meets a specific client’s needs, wherein [0111] teaches the customer entity can provide basic information about the industry that the customer operates in, wherein based on the data collected from other customers in the same industry and customer size, the risk score can be calculated based on industry benchmarks, wherein [0112] teaches based on the needs of the industry and for the entity, the controls can be selected to be assessed for the customer based on their needs, wherein Fig. 6 and [0115] teach the risk scores can be calculated according to assessment results for a given period, such as making comparisons with the same week of a previous month, and/or same month/quarter of a previous year, wherein [0151] teaches after collecting the risk information on a specified basis, such as at a specific period, the collected data can be pushed onto the risk management hardware device, wherein the device serves as a repository for all the risk parameters for the enterprise asset, wherein [0087] teaches the risk identification, quantification, and mitigation engine can collect and analyze data including global organizations data including multiple jurisdictions data, local business environment data, geo political data, as well as multiple risk category data, as well as in [0159-0160] teach the process can explore the various metrics of specified industries, regulations, and systems and selects the right set of modules that would be relevant; see also: [0098, 0121, 0196, 0201]);
processing the forecasting risk assessment data using an inquisitive artificial intelligence engine to identify a pattern and trend in the forecasting risk assessment data and use the pattern and trend to forecast risk assessment variable values for the first entity to generate forecasted risk assessment variable data ([0079] teaches predictive analytics include finding patterns from data using mathematical models that predict future outcomes, wherein the predictive analytics include machine learning that analyzes current and historical facts to make predictions about the future or otherwise unknown events, wherein the predictive models exploit patterns found in historical data to identify risks and opportunities, wherein the models can capture relationships among many factors to allow assessment of risk associated with a set of conditions, as well as in [0115] teaches detecting anomalies in risk scores, wherein the seasonality of risk can be considered along with its patterns as the risk may just be following a pattern even if it has varied widely from the last period of assessment, wherein a machine learning model can be trained to detect patterns and predict the approximate risk score according to existing patterns in the data, wherein the RNN can be trained on different types of patterns, as well as in [0158] teaches summarizing risk data and presenting various scoring, exposure, and trends of the entire enterprise, wherein [0159-0160] teach the process can explore the various metrics of specified industries, regulations, and systems and selects the right set of modules that would be relevant, wherein the process can derive the impact, likelihood, and risk score of the metrics along with the anomalies, wherein the AI/ML can be applied for prediction steps in order to output a summarization of various risk categories and the highest level risk score for the company; see also: [0099, 0165-0166]); and
dynamically generating a user interface comprising an interactive map for a user ([0097] teaches the risk identification, quantification, and mitigation engine includes a dashboard and other interactive modules, wherein Fig. 31 and [0194-0195] teach a risk geo-map that displays the underlying data in terms of risk exposure and renumeration cost at various locations across the world, wherein the size bubbles show the relative value of each risk exposure, wherein the overall risk exposure can show an aggregated risk that includes all the regions shown in the geo-map, wherein the chart can show multivariate risk data represented on its various axes, wherein the risk geo-map can be used for risk management administrator and can be updated in real time, wherein the risks can be aggregated and displayed in order to represent top risks to an organization, wherein [0115] teaches the visualizations can display predicted versus actual scores and alert users of anomalies, as well as in [0122] teaches the dashboard can allow users to aggregate and highlight risk as a risk score that can be drilled down for each of the models and then view risk at a model level, wherein the users can also drill down into the model to view risk at a more granular detail; see also: [0099, 0164]),
the interactive map embodying the forecasted risk assessment variable values to display a forecasted risk for the first entity to the user in association with the first entity in the user interface ([0097] teaches the risk identification, quantification, and mitigation engine includes a dashboard and other interactive modules, wherein Fig. 31 and [0194-0195] teach a risk geo-map that displays the underlying data in terms of risk exposure and renumeration cost at various locations across the world, wherein the size bubbles show the relative value of each risk exposure, wherein the overall risk exposure can show an aggregated risk that includes all the regions shown in the geo-map, wherein the chart can show multivariate risk data represented on its various axes, wherein the risk geo-map can be used for risk management administrator and can be updated in real time, wherein the risks can be aggregated and displayed in order to represent top risks to an organization, wherein [0115] teaches the visualizations can display predicted versus actual scores and alert users of anomalies, as well as in [0122] teaches the dashboard can allow users to aggregate and highlight risk as a risk score that can be drilled down for each of the models and then view risk at a model level, wherein the users can also drill down into the model to view risk at a more granular detail; see also: [0099, 0164]).
However, Sarkar does not explicitly teach wherein the comprises a forecasted value for at least one indicator geopolitical risk to the first entity.
From the same or similar field of endeavor, Balli teaches wherein the comprises a forecasted value for at least one indicator geopolitical risk to the first entity (Pg. 467-468 teach constructing GPR indices for 19 countries based on country-specific factors including central government’s debt, budget deficit, stock market capitalization, and each country’s geographical area, as well as in Pg. 472 teaches applying a spillover model to measure total and pairwise geopolitical risk (GPR) transmissions among sample countries, wherein the spillover effects across various variables, wherein these measures capture GPR spillovers from one country to multiple countries and vice versa, as well as in Pg. 478 teaches explaining GPR transmissions based on the gravity model framework that captures the dyadic interaction between countries by involving each country’s size and geographical distance; see also: Pgs. 469-470, 473).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sarkar to incorporate the teachings of Balli to include wherein the comprises a forecasted value for at least one indicator geopolitical risk to the first entity. One would have been motivated to do so in order to provide a novel perspective by studying GPR transmission via information flows associated with geopolitical conflicts (Balli, Pg. 467). By incorporating the teachings of Balli, one would have been able to make forecasts in order to build resilient supply chains, develop crisis response plans, and secure credit and political risk insurance to protect their assets better (Balli, Pg. 465).
Regarding claim 3, the combination of Sarkar and Balli teaches all the limitations of claim 1 above.
Sarkar further teaches wherein the forecasting risk assessment data comprises first values for a plurality of intrastate risk indicators and intrastate risk variables for the first entity and where the forecasted risk assessment variable data comprises forecasted values for the plurality of intrastate risk indicators ([0079] teaches predictive analytics include finding patterns from data using mathematical models that predict future outcomes, wherein the predictive analytics include machine learning that analyzes current and historical facts to make predictions about the future or otherwise unknown events, wherein the predictive models exploit patterns found in historical data to identify risks and opportunities, wherein the models can capture relationships among many factors to allow assessment of risk associated with a set of conditions, as well as in [0087] teaches the risk identification, quantification, and mitigation engine can collect and analyze data including global organizations data including multiple jurisdictions data, local business environment data, geo political data, as well as multiple risk category data, wherein [0115] teaches detecting anomalies in risk scores, wherein the seasonality of risk can be considered along with its patterns as the risk may just be following a pattern even if it has varied widely from the last period of assessment, wherein a machine learning model can be trained to detect patterns and predict the approximate risk score according to existing patterns in the data, wherein the RNN can be trained on different types of patterns, as well as in [0158] teaches summarizing risk data and presenting various scoring, exposure, and trends of the entire enterprise, wherein [0159-0160] teach the process can explore the various metrics of specified industries, regulations, and systems and selects the right set of modules that would be relevant, wherein the process can derive the impact, likelihood, and risk score of the metrics along with the anomalies, wherein the AI/ML can be applied for prediction steps in order to output a summarization of various risk categories and the highest level risk score for the company; see also: [0099, 0165-0166]).
Regarding claim 4, the combination of Sarkar and Balli teaches all the limitations of claim 1 above.
However, Sarkar does not explicitly teach wherein the forecasting risk assessment data comprises an indicator of a first escalatory action associated with escalating interstate tension and wherein the forecasted risk assessment variable data identifies a probable escalatory action for the first entity.
From the same or similar field of endeavor, Balli further teaches wherein the forecasting risk assessment data comprises an indicator of a first escalatory action associated with escalating interstate tension and wherein the forecasted risk assessment variable data identifies a probable escalatory action for the first entity (Pg. 472 teaches applying a spillover model to measure total and pairwise geopolitical risk (GPR) transmissions among sample countries, wherein the spillover effects across various variables, wherein these measures capture GPR spillovers from one country to multiple countries and vice versa, as well as in Pg. 478 teaches explaining GPR transmissions based on the gravity model framework that captures the dyadic interaction between countries by involving each country’s size and geographical distance; see also: Pgs. 469-470, 473).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Sarkar and Balli to incorporate the further teachings of Balli to include wherein the forecasting risk assessment data comprises an indicator of a first escalatory action associated with escalating interstate tension and wherein the forecasted risk assessment variable data identifies a probable escalatory action for the first entity. One would have been motivated to do so in order to provide a novel perspective by studying GPR transmission via information flows associated with geopolitical conflicts (Balli, Pg. 467). By incorporating the teachings of Balli, one would have been able to make forecasts in order to build resilient supply chains, develop crisis response plans, and secure credit and political risk insurance to protect their assets better (Balli, Pg. 465).
Regarding claim 5, the combination of Sarkar and Balli teaches all the limitations of claim 4 above.
However, Sarkar does not explicitly teach further comprising processing text input from a plurality of hyper localized documents to identify, using a plurality of signals, the first escalatory action.
From the same or similar field of endeavor, Balli further teaches further comprising processing text input from a plurality of hyper localized documents to identify, using a plurality of signals, the first escalatory action (Pg. 465 teaches employing news-based indicators to predict geopolitical conflicts to offer dichotomous or probabilistic forecasts, wherein Pg. 466 teaches developing the GPR index from news stories featuring events and threats associated with geopolitical conflicts such as wars, terrorist acts, ethnic and political violence, and geopolitical tensions, wherein using the news-based GPR index, one can build upon conflict contagion literature that considers the role of GPR-related news in spreading geopolitical conflicts across borders, wherein the GPR can be tracked in real-time and continuously by a wide range of stakeholders including the public, media, investors, policy makers, and the stakeholder concerns of newspapers, wherein Pg. 468 teaches considering a broader view of GPR that encompasses realized geopolitical events and potential threats, thereby accounting for both eventual and probabilistic risk concepts and tracking of GPR perceived by a wide range of stakeholders, wherein the news stores include events and threats associated with geopolitical conflicts including wars, violence, and more, wherein keywords can capture specific groups of dimensions, wherein Pg. 469 teaches the index is sufficiently broad measure of GPR that may track single or multiple conflicts simultaneously; see also: Pgs. 467, 485).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Sarkar and Balli to incorporate the further teachings of Balli to include further comprising processing text input from a plurality of hyper localized documents to identify, using a plurality of signals, the first escalatory action. One would have been motivated to do so in order to provide a novel perspective by studying GPR transmission via information flows associated with geopolitical conflicts (Balli, Pg. 467). By incorporating the teachings of Balli, one would have been able to make forecasts in order to build resilient supply chains, develop crisis response plans, and secure credit and political risk insurance to protect their assets better (Balli, Pg. 465).
Regarding claim 6, the combination of Sarkar and Balli teaches all the limitations of claim 4 above.
However, Sarkar does not explicitly teach further comprising generating a bilateral stress score for the first entity using the probable escalatory action.
From the same or similar field of endeavor, Balli further teaches further comprising generating a bilateral stress score for the first entity using the probable escalatory action (Pg. 468 teaches considering a broader view of GPR that encompasses realized geopolitical events and potential threats, thereby accounting for both eventual and probabilistic risk concepts and tracking of GPR perceived by a wide range of stakeholders, wherein the news stores include events and threats associated with geopolitical conflicts including wars, violence, and more, wherein keywords can capture specific groups of dimensions, wherein Pg. 469 teaches the index is sufficiently broad measure of GPR that may track single or multiple conflicts simultaneously, wherein Pg. 472 teaches applying a spillover model to measure total and pairwise geopolitical risk (GPR) transmissions among sample countries, wherein the spillover effects across various variables, wherein these measures capture GPR spillovers from one country to multiple countries and vice versa, as well as in Pg. 478 teaches explaining GPR transmissions based on the gravity model framework that captures the dyadic interaction between countries by involving each country’s size and geographical distance, wherein Pg. 485 teaches generating a total pairwise directional spillovers of GPR between countries; see also: Pgs. 465-467, 486).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Sarkar and Balli to incorporate the further teachings of Balli to include further comprising generating a bilateral stress score for the first entity using the probable escalatory action. One would have been motivated to do so in order to provide a novel perspective by studying GPR transmission via information flows associated with geopolitical conflicts (Balli, Pg. 467). By incorporating the teachings of Balli, one would have been able to make forecasts in order to build resilient supply chains, develop crisis response plans, and secure credit and political risk insurance to protect their assets better (Balli, Pg. 465).
Regarding claim 7, the combination of Sarkar and Balli teaches all the limitations of claim 6 above.
However, Sarkar does not explicitly teach wherein the forecasted risk assessment variable data identifies a second entity as an anticipated participant in the probable escalatory action, and wherein the bilateral stress score is determined for the first entity with respect to the second entity.
From the same or similar field of endeavor, Balli further teaches wherein the forecasted risk assessment variable data identifies a second entity as an anticipated participant in the probable escalatory action, and wherein the bilateral stress score is determined for the first entity with respect to the second entity (Pg. 468 teaches considering a broader view of GPR that encompasses realized geopolitical events and potential threats, thereby accounting for both eventual and probabilistic risk concepts and tracking of GPR perceived by a wide range of stakeholders, wherein the news stores include events and threats associated with geopolitical conflicts including wars, violence, and more, wherein keywords can capture specific groups of dimensions, wherein Pg. 469 teaches the index is sufficiently broad measure of GPR that may track single or multiple conflicts simultaneously, wherein Pg. 472 teaches applying a spillover model to measure total and pairwise geopolitical risk (GPR) transmissions among sample countries, wherein the spillover effects across various variables, wherein these measures capture GPR spillovers from one country to multiple countries and vice versa, as well as in Pg. 478 teaches explaining GPR transmissions based on the gravity model framework that captures the dyadic interaction between countries by involving each country’s size and geographical distance, wherein Pg. 485 teaches generating a total pairwise directional spillovers of GPR between countries; see also: Pgs. 465-467, 486).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Sarkar and Balli to incorporate the further teachings of Balli to include wherein the forecasted risk assessment variable data identifies a second entity as an anticipated participant in the probable escalatory action, and wherein the bilateral stress score is determined for the first entity with respect to the second entity. One would have been motivated to do so in order to provide a novel perspective by studying GPR transmission via information flows associated with geopolitical conflicts (Balli, Pg. 467). By incorporating the teachings of Balli, one would have been able to make forecasts in order to build resilient supply chains, develop crisis response plans, and secure credit and political risk insurance to protect their assets better (Balli, Pg. 465).
Regarding claim 8, the combination of Sarkar and Balli teaches all the limitations of claim 1 above.
However, Sarkar does not explicitly teach wherein the forecasting risk assessment data comprises alliance and associations data, disruptive event data, economic index rankings, military index rankings, and activity data, wherein the forecasted risk assessment variable data comprises an indicator of a probable disruptive event.
From the same or similar field of endeavor, Balli further teaches wherein the forecasting risk assessment data comprises alliance and associations data, disruptive event data, economic index rankings, military index rankings, and activity data (Pg. 466 teaches developing the GPR index from news stores featuring events and threats associated with geopolitical conflicts such as wars, terrorist acts, ethnic and political violence, and geopolitical tensions, wherein the conflict contagion literature indicates that geopolitical conflicts spread across borders, wherein the geopolitical conflict information includes political violence, terrorism, stock market volatility, economic reforms, economic policy and uncertainty, wherein Pg. 468 teaches identifying country specific factors and bilateral factors, wherein the bilateral factors include bilateral trade, colonial ties, contiguity, common language, geographical distance, wherein the country specific factors include the central government’s debt, budget deficit, stock market capitalization, and geographical area, wherein the keywords capturing GPR dimensions that are used to collect data include military, war, coup, guerilla, threat, air strike, casualties, and more, wherein Pg. 490 teaches the GPR findings suggests that states undergoing economic problems are likely to be contagious to their neighbors, wherein the GPR spillover data includes information regarding fiscal imbalances and financial liabilities can be responsible for cross country spillovers of economic uncertainty; see also: Pgs. 467, 470-475, 485),
wherein the forecasted risk assessment variable data comprises an indicator of a probable disruptive event (Pg. 468 teaches considering a broader view of GPR that encompasses realized geopolitical events and potential threats, thereby accounting for both eventual and probabilistic risk concepts and tracking of GPR perceived by a wide range of stakeholders, wherein the news stores include events and threats associated with geopolitical conflicts including wars, violence, and more, wherein keywords can capture specific groups of dimensions, wherein Pg. 469 teaches the index is sufficiently broad measure of GPR that may track single or multiple conflicts simultaneously, wherein Pg. 472 teaches applying a spillover model to measure total and pairwise geopolitical risk (GPR) transmissions among sample countries, wherein the spillover effects across various variables, wherein these measures capture GPR spillovers from one country to multiple countries and vice versa, as well as in Pg. 478 teaches explaining GPR transmissions based on the gravity model framework that captures the dyadic interaction between countries by involving each country’s size and geographical distance, wherein Pg. 485 teaches generating a total pairwise directional spillovers of GPR between countries; see also: Pgs. 465-467, 486).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Sarkar and Balli to incorporate the further teachings of Balli to include wherein the forecasting risk assessment data comprises alliance and associations data, disruptive event data, economic index rankings, military index rankings, and activity data, wherein the forecasted risk assessment variable data comprises an indicator of a probable disruptive event. One would have been motivated to do so in order to provide a novel perspective by studying GPR transmission via information flows associated with geopolitical conflicts (Balli, Pg. 467). By incorporating the teachings of Balli, one would have been able to make forecasts in order to build resilient supply chains, develop crisis response plans, and secure credit and political risk insurance to protect their assets better (Balli, Pg. 465).
Regarding claim 9, the combination of Sarkar and Balli teaches all the limitations of claim 8 above.
Sarkar further teaches wherein the probable disruptive event is selected from a group consisting of: cyberattack ([0114] teaches calculating of a risk of exposure assessment using the machine learning and other input data in order to extrapolate the data to determine the risk exposure, wherein [0116] teaches the exposures may have hierarchical dependencies and the system can automatically identify non-compliance and generate a list of possible scenarios based on risk dependency, then bubble up the most likely scenarios for the user to view, as well as in [0136] teaches risks can be categorized by risk type, function, location, and more, wherein the system can identify and quantify the likelihood of exposure in terms of cost and remediation cost, wherein [0196] teaches the risk exposure may have specified categories including vendor partner data loss, web application attacks, or other risks; see also: Fig. 31, [0194, 0200, 0202]).
Regarding claim 10, the combination of Sarkar and Balli teaches all the limitations of claim 1 above.
Sarkar further teaches further comprising: receiving new variable values for the selected time increment, the new variable values extracted or derived from new input data from the worldwide network of computers ([0003] teaches providing real-time risk identification and quantification of risk exposure based on [0005-0006] teaches organizational changes including new systems, new laws, regulations, standards, and more, wherein the system provides a real-time, on-demand quantification tool that provides an enterprise wide, centralized view of an organization’s current risk profile and risk exposure, wherein [0074] teaches the artificial intelligence model provides computers with the ability to learn without being explicitly programmed and can teach itself to grow and change when being exposed to new data, wherein [0086] teaches performing continuous risk monitoring using the risk identification, quantification, and mitigation engine, wherein [0099] teaches a continuous AI feedback loop is implemented between the visualization module and the data ingestion and smart data discovery engine, wherein the feedback can be implemented within the engine and can include application data and systems, as well as cloud-based platform stores, wherein [0178] teaches risk parameter data is collected from both end-user device management systems and IoT gateways, wherein the risk parameter related data can be plugged into the systems to collect and summarize the data at frequent/periodic intervals, wherein [0087] teaches the risk identification, quantification, and mitigation engine can collect and analyze data including global organizations data including multiple jurisdictions data, local business environment data, geo political data, as well as multiple risk category data, wherein Fig. 6 and [0115] teach the risk scores can be calculated according to assessment results for a given period, such as making comparisons with the same week of a previous month, and/or same month/quarter of a previous year, wherein [0151] teaches after collecting the risk information on a specified basis, such as at a specific period, the collected data can be pushed onto the risk management hardware device, wherein the device serves as a repository for all the risk parameters for the enterprise asset, wherein [0155] teaches on a periodic basis, the local risk information agent uses a risk management hardware device to write the parameters that it has collected from the external hardware and software components in a secure manner, wherein the data can be written onto the internal memory and the data is analyzed, as well as in [0178] teaches aggregating data from an IoT gateway and collecting and summarizing the data at a frequent/periodic basis; see also: [0087-0088, 0196, 0201]); and
automatically relearning, by the inquisitive AI engine, the pattern and trend using the new variable values ([0079] teaches predictive analytics include finding patterns from data using mathematical models that predict future outcomes, wherein the predictive analytics include machine learning that analyzes current and historical facts to make predictions about the future or otherwise unknown events, wherein the predictive models exploit patterns found in historical data to identify risks and opportunities, wherein the models can capture relationships among many factors to allow assessment of risk associated with a set of conditions, wherein [0099] teaches a continuous AI feedback loop is implemented between the visualization module and the data ingestion and smart data discovery engine, wherein the feedback can be implemented within the engine and can include application data and systems, as well as cloud-based platform store, as well as in [0115] teaches detecting anomalies in risk scores, wherein the seasonality of risk can be considered along with its patterns as the risk may just be following a pattern even if it has varied widely from the last period of assessment, wherein a machine learning model can be trained to detect patterns and predict the approximate risk score according to existing patterns in the data, wherein the RNN can be trained on different types of patterns, as well as in [0158] teaches summarizing risk data and presenting various scoring, exposure, and trends of the entire enterprise, wherein [0159-0160] teach the process can explore the various metrics of specified industries, regulations, and systems and selects the right set of modules that would be relevant, wherein the process can derive the impact, likelihood, and risk score of the metrics along with the anomalies, wherein the AI/ML can be applied for prediction steps in order to output a summarization of various risk categories and the highest level risk score for the company; see also: [0099, 0165-0166]).
Regarding claim 13, the combination of Sarkar and Balli teaches all the limitations of claim 1 above.
Sarkar further teaches wherein the forecasting risk assessment data corresponds to a plurality of increments prior to the selected time increment ([0110] teaches the risk scores can determine the severity of the risk levels for an organization, wherein the risk scores can be calculated and displayed with a frequency that meets a specific client’s needs, wherein [0111] teaches the customer entity can provide basic information about the industry that the customer operates in, wherein based on the data collected from other customers in the same industry and customer size, the risk score can be calculated based on industry benchmarks, wherein [0112] teaches based on the needs of the industry and for the entity, the controls can be selected to be assessed for the customer based on their needs, wherein Fig. 6 and [0115] teach the risk scores can be calculated according to assessment results for a given period, such as making comparisons with the same week of a previous month, and/or same month/quarter of a previous year, wherein [0151] teaches after collecting the risk information on a specified basis, such as at a specific period, the collected data can be pushed onto the risk management hardware device, wherein the device serves as a repository for all the risk parameters for the enterprise asset, wherein [0087] teaches the risk identification, quantification, and mitigation engine can collect and analyze data including global organizations data including multiple jurisdictions data, local business environment data, geo political data, as well as multiple risk category data, as well as in [0159-0160] teach the process can explore the various metrics of specified industries, regulations, and systems and selects the right set of modules that would be relevant; see also: [0098, 0121, 0196, 0201]).
Regarding claim 17, Sarkar anticipates all the limitations of claim 14 above.
However, Sarkar does not explicitly teach wherein the forecasting risk assessment data comprises risk assessment variable values extracted or derived from hyper-localized data associated with the first entity.
From the same or similar field of endeavor, Balli teaches wherein the forecasting risk assessment data comprises risk assessment variable values extracted or derived from hyper-localized data associated with the first entity (Pg. 465 teaches employing news-based indicators to predict geopolitical conflicts to offer dichotomous or probabilistic forecasts, wherein Pg. 466 teaches developing the GPR index from news stories featuring events and threats associated with geopolitical conflicts such as wars, terrorist acts, ethnic and political violence, and geopolitical tensions, wherein using the news-based GPR index, one can build upon conflict contagion literature that considers the role of GPR-related news in spreading geopolitical conflicts across borders, wherein the GPR can be tracked in real-time and continuously by a wide range of stakeholders including the public, media, investors, policy makers, and the stakeholder concerns of newspapers, wherein Pg. 468 teaches considering a broader view of GPR that encompasses realized geopolitical events and potential threats, thereby accounting for both eventual and probabilistic risk concepts and tracking of GPR perceived by a wide range of stakeholders, wherein the news stores include events and threats associated with geopolitical conflicts including wars, violence, and more, wherein keywords can capture specific groups of dimensions, wherein Pg. 469 teaches the index is sufficiently broad measure of GPR that may track single or multiple conflicts simultaneously; see also: Pgs. 467, 485).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sarkar to incorporate the teachings of Balli to include wherein the forecasting risk assessment data comprises risk assessment variable values extracted or derived from hyper-localized data associated with the first entity. One would have been motivated to do so in order to provide a novel perspective by studying GPR transmission via information flows associated with geopolitical conflicts (Balli, Pg. 467). By incorporating the teachings of Balli, one would have been able to make forecasts in order to build resilient supply chains, develop crisis response plans, and secure credit and political risk insurance to protect their assets better (Balli, Pg. 465).
Regarding claim 19, Sarkar anticipates all the limitations of claim 14 above.
However, Sarkar does not explicitly teach wherein the code is further executable to process text input from a plurality of hyper localized documents to identify, using a plurality of signals, a first escalatory action by a second entity with respect to the first entity, wherein the forecasted risk assessment variable values indicate a probable escalatory action by the second entity with respect to the first entity.
From the same or similar field of endeavor, Balli teaches wherein the code is further executable to process text input from a plurality of hyper localized documents to identify, using a plurality of signals, a first escalatory action by a second entity with respect to the first entity (Pg. 465 teaches employing news-based indicators to predict geopolitical conflicts to offer dichotomous or probabilistic forecasts, wherein Pg. 466 teaches developing the GPR index from news stories featuring events and threats associated with geopolitical conflicts such as wars, terrorist acts, ethnic and political violence, and geopolitical tensions, wherein using the news-based GPR index, one can build upon conflict contagion literature that considers the role of GPR-related news in spreading geopolitical conflicts across borders, wherein the GPR can be tracked in real-time and continuously by a wide range of stakeholders including the public, media, investors, policy makers, and the stakeholder concerns of newspapers, wherein Pg. 468 teaches considering a broader view of GPR that encompasses realized geopolitical events and potential threats, thereby accounting for both eventual and probabilistic risk concepts and tracking of GPR perceived by a wide range of stakeholders, wherein the news stores include events and threats associated with geopolitical conflicts including wars, violence, and more, wherein keywords can capture specific groups of dimensions, wherein Pg. 469 teaches the index is sufficiently broad measure of GPR that may track single or multiple conflicts simultaneously; see also: Pgs. 467, 485),
wherein the forecasted risk assessment variable values indicate a probable escalatory action by the second entity with respect to the first entity (Pg. 468 teaches considering a broader view of GPR that encompasses realized geopolitical events and potential threats, thereby accounting for both eventual and probabilistic risk concepts and tracking of GPR perceived by a wide range of stakeholders, wherein the news stores include events and threats associated with geopolitical conflicts including wars, violence, and more, wherein keywords can capture specific groups of dimensions, wherein Pg. 469 teaches the index is sufficiently broad measure of GPR that may track single or multiple conflicts simultaneously, wherein Pg. 472 teaches applying a spillover model to measure total and pairwise geopolitical risk (GPR) transmissions among sample countries, wherein the spillover effects across various variables, wherein these measures capture GPR spillovers from one country to multiple countries and vice versa, as well as in Pg. 478 teaches explaining GPR transmissions based on the gravity model framework that captures the dyadic interaction between countries by involving each country’s size and geographical distance, wherein Pg. 485 teaches generating a total pairwise directional spillovers of GPR between countries; see also: Pgs. 465-467, 486).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sarkar to incorporate the teachings of Balli to include wherein the code is further executable to process text input from a plurality of hyper localized documents to identify, using a plurality of signals, a first escalatory action by a second entity with respect to the first entity, wherein the forecasted risk assessment variable values indicate a probable escalatory action by the second entity with respect to the first entity. One would have been motivated to do so in order to provide a novel perspective by studying GPR transmission via information flows associated with geopolitical conflicts (Balli, Pg. 467). By incorporating the teachings of Balli, one would have been able to make forecasts in order to build resilient supply chains, develop crisis response plans, and secure credit and political risk insurance to protect their assets better (Balli, Pg. 465).
Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over Sarkar (US 20230077527 A1) in view of Balli et al. ("Geopolitical risk spillovers and its determinants," 2022) in view of Quansheng et al. (“Spatio-temporal simulation of the geopolitical environment system,” 2018).
Regarding claim 2, the combination of Sarkar and Balli teaches all the limitations of claim 1 above.
However, Sarkar does not explicitly teach further comprising synchronizing global observation of atmospheric, terrestrial, and oceanic conditions, human activity, and artificial systems across geographic regions.
From the same or similar field of endeavor, Quansheng teaches further comprising synchronizing global observation of atmospheric, terrestrial, and oceanic conditions, human activity, and artificial systems across geographic regions (Pgs. 872-873 teach treating the Earth as an integrated system and utilizing earth system science, which is about interactions of the lithosphere, hydrosphere, bio-sphere, and atmosphere, as well as the impact of human societies on these components, wherein the ESS studies the earth system at multiple scales and from a systematic point of view that helps achieve better understanding of the nature of what we depend on for our survival, wherein the global change research is aimed to explore the dynamics of climate change and its impacts on physical environment as well as social society worldwide, wherein the future earth is an observation technique that utilizes machine learning to transcend the boundaries of traditional studies, wherein the GES study can be a regional study to an integrated simulation of multiple elements at multiple scales, wherein geopolitical risks may cause significant effects worldwide, wherein the simulation of the GES can include the assessment of risk and forecasting geopolitical events by combining physical and social sciences, wherein Pg. 875-876 teach qualified and timely data is critical to the geopolitical environment system research, wherein the data may include micrometeorological data, energy and water cycle, climate and cryosphere, land use cover, atmosphere, oceans, human security, and biodiversity on a global scale for the geopolitical system research, wherein global and regional spatial data of population-based, on-land use and nighttime lighting, and global GDP spatial distribution data can be utilized, as well as world bank urbanization datasets can be utilized, wherein this data can be used to forecast unexpected geopolitical events, wherein Pg. 878 teaches forecasting geopolitical events with machine learning and artificial intelligence techniques based on physical models, such as the land surface model, climate model, and ocean model, should be coupled at regional and global scales, wherein socio-economic models, including international trade, urban development, and more, can be simulated; see also: Pgs. 874, 877).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Sarkar and Balli to incorporate the teachings of Quansheng to include further comprising synchronizing global observation of atmospheric, terrestrial, and oceanic conditions, human activity, and artificial systems across geographic regions. One would have been motivated to do so in order to study the earth system at multiple scales and from a systematic point of view that helps achieve better understanding of the nature of what we depend on for our survival (Quansheng, Pgs. 872-873). By incorporating the teachings of Quansheng, one would have been able to evaluate and mitigate global geopolitical risks including political, economic, social, environmental, and technological risks using integration and interdisciplinary studies (Quansheng, Pg. 878).
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Sarkar (US 20230077527 A1) in view of Balli et al. ("Geopolitical risk spillovers and its determinants," 2022) in view of Agbamu (US 20230134651 A1).
Regarding claim 11, the combination of Sarkar and Balli teaches all the limitations of claim 1 above.
However, Sarkar does not explicitly teach further comprising the inquisitive AI engine spontaneously generating a query to a data source to collect additional risk assessment data.
From the same or similar field of endeavor, Agbamu teaches further comprising the inquisitive AI engine spontaneously generating a query to a data source to collect additional risk assessment data ([0007] teaches the system using a machine artificial intelligence in adjusting risk scores for classifying and predicting risks, wherein the data can be refreshed over a fix, predetermined, or random time, thus continuously providing robust authentication, wherein [0041] teaches extracting additional information during any time in the client lifecycle in order to seamless extract information and thus improve efficiency, wherein [0042] teaches identifying politically exposed persons and determining risks for users; see also: [0049]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Sarkar and Balli to incorporate the teachings of Agbamu to include further comprising the inquisitive AI engine spontaneously generating a query to a data source to collect additional risk assessment data. One would have been motivated to do so in order to continuously provide robust authentication services and empower timely response and notification, thus providing real-time evaluation (Agbamu, [0007]). By incorporating the teachings of Agbamu, one would have been able to extract additional information at any time during the lifecycle to seamlessly extract information, thus improving efficiency (Agbamu, [0041]).
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Sarkar (US 20230077527 A1) in view of Balli et al. ("Geopolitical risk spillovers and its determinants," 2022) in view of Urban et al. (US 20190188616 A1).
Regarding claim 12, the combination of Sarkar and Balli teaches all the limitations of claim 1 above.
However, Sarkar does not explicitly teach wherein identifying the pattern and trend comprises identifying variables that trend together, an order in which the identified variables move, a velocity of movement of each of the respective identified variables in relation to others of the identified variables.
From the same or similar field of endeavor, Urban teaches wherein identifying the pattern and trend comprises identifying variables that trend together, an order in which the identified variables move, a velocity of movement of each of the respective identified variables in relation to others of the identified variables ([0008] teaches by executing the risk model using dynamic signal processing and considering impact, velocity, likelihood, and interconnectedness of risk factors in a real-time manner, potential third party disruption and/or vulnerability can be determined and forecasted, wherein risk factors impact other risk factors and the map may provide for diffusion of risk to other risk factors from a risk factor that is active, wherein [0090] teaches the scores can be represented as trend lines, wherein [0100] teaches setting impact and likelihood parameters for each of the risk factors and their correlated risk factors, wherein the impacts can be evaluated as being low, medium, and high, wherein [0103] teaches evaluating a scenario after selecting, connecting, and setting impact, likelihood, and velocity of risk factors, wherein the connections and directionality between risk factors can be evaluated related to impact, likelihood, and velocity, wherein certain connections and directionality of connections have low impact or large impact, wherein certain velocities can be slow or faster impacting the scenario; see also: [0048, 0087, 0093]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Sarkar and Balli to incorporate the teachings of Urban to include wherein identifying the pattern and trend comprises identifying variables that trend together, an order in which the identified variables move, a velocity of movement of each of the respective identified variables in relation to others of the identified variables. One would have been motivated to do so in order to allow users to potentially avoid disruption of production or other operations by considering impact, velocity, likelihood, and interconnectedness of risk factors (Urban, [0008]). By incorporating the teachings of Urban, one would have been able to better enable a user to visually inspect each node in order to understand directionality, impact, likelihood, and velocity (Urban, [0103]).
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Sarkar (US 20230077527 A1) in view of Urban et al. (US 20190188616 A1).
Regarding claim 16, Sarkar anticipates all the limitations of claim 14 above.
However, Sarkar does not explicitly teach wherein the inquisitive artificial intelligence engine is executable to observe dynamic movement of variables across sectors to recognize the pattern and trend.
From the same or similar field of endeavor, Urban teaches wherein the inquisitive artificial intelligence engine is executable to observe dynamic movement of variables across sectors to recognize the pattern and trend ([0008] teaches by executing the risk model using dynamic signal processing and considering impact, velocity, likelihood, and interconnectedness of risk factors in a real-time manner, potential third party disruption and/or vulnerability can be determined and forecasted, wherein risk factors impact other risk factors and the map may provide for diffusion of risk to other risk factors from a risk factor that is active, wherein [0090] teaches the scores can be represented as trend lines, wherein [0100] teaches setting impact and likelihood parameters for each of the risk factors and their correlated risk factors, wherein the impacts can be evaluated as being low, medium, and high, wherein [0103] teaches evaluating a scenario after selecting, connecting, and setting impact, likelihood, and velocity of risk factors, wherein the connections and directionality between risk factors can be evaluated related to impact, likelihood, and velocity, wherein certain connections and directionality of connections have low impact or large impact, wherein certain velocities can be slow or faster impacting the scenario; see also: [0048, 0087, 0093]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Sarkar to incorporate the teachings of Urban to include wherein the inquisitive artificial intelligence engine is executable to observe dynamic movement of variables across sectors to recognize the pattern and trend. One would have been motivated to do so in order to allow users to potentially avoid disruption of production or other operations by considering impact, velocity, likelihood, and interconnectedness of risk factors (Urban, [0008]). By incorporating the teachings of Urban, one would have been able to better enable a user to visually inspect each node in order to understand directionality, impact, likelihood, and velocity (Urban, [0103]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Leidner et al. (US 20160371618 A1) discloses performing risk identification for the supply chain
O’Toole et al. (US 20210216928 A1) discloses alerting a user to threat events and risks posed to assets
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625