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 .
Status of Claims
This action is in response to remarks received 04/06/2026.
The current application claims earliest priority from 14/925,974 filed 10/28/2015.
Claims 1 and 4 being independent have also been amended, and claims 2-3 and 5-6 being dependent are as previously presented.
Claims 1-6 are currently pending and have been examined.
Response to Arguments
Applicant's arguments filed 04/06/2026 have been fully considered but they are not persuasive.
With respect to Applicant’s arguments under 35 U.S.C. § 101, the arguments have been fully considered, however, the examiner respectfully disagrees.
Under Step 2A, Prong One, the Applicant argues that the claims do not recite an abstract idea, or alternatively that certain limitations, including “scrub and normalize the external data as multidimensional time series data” and the use of a trained machine-learning model, should be treated as abstract.
The claims, as amended, recite steps including retrieving insurance underwriting terms, identifying risks, determining magnitudes of risks, identifying upcoming risk events, generating a vulnerability model, predicting loss, performing simulations, and analyzing exposures and losses. These limitations, when considered as a whole, describe collecting information, analyzing the information, and using the results to predict outcomes related to insurance risk.
Such activities constitute mental processes (evaluation, judgement, prediction) and certain methods of organizing human activity, specifically fundamental economic practices relating to insurance and risk mitigation.
The additional limitation of “scrubbing and normalizing the external data as multidimensional time series data” does not remove the claims from the abstract idea category. This limitation is directed to organizing and preparing data for analysis, which is itself a form of data manipulation and information processing. The recitation of “multidimensional time series data” merely specifies a type of data format and does not change the nature of the claimed activity, which remains focused on analyzing information to support decision making.
Similarly, the recitation of a “trained machine-learning model” does not alter the abstract nature of the claims. The model is used to detect patterns and generate predictions from data, which is a form of mathematical and analytical processing. As recited, the machine-learning model performs the same type of evaluation and prediction that could be performed mentally or with pen and paper, albeit more efficiently.
For the above reasoning, the claims recite a judicial exception.
Under Step 2A, Prong Two, Applicant argues that the claims integrate the abstract idea into a practical application, particularly in view of the limitation directed to normalizing data as multidimensional time series data and by analogy to USPTO Subject Matter Eligibility Example 42; however, the Examiner respectfully disagrees.
The claims do not recite any improvement to computer functionality, network operation, or another technological field. Instead, the additional elements; including the processor, memory, machine-learning model, data normalization, simulation, and synthetic data generation; are used as tools to perform the abstract idea of risk analysis.
The limitation of “scrubbing and normalizing the external data as multidimensional time series data” does not integrate the abstract idea into a practical application. This limitation is recited at a high-level of generality and does not specify how the normalization is performed, what specific transformation is applied, or how such normalization improves the functioning of a computer or another technology. Rather, it merely places data into a format suitable for subsequent analysis.
Applicant’s reliance on Subject Matter Eligibility Example 42 is not persuasive. In Example 42, the claims were found eligible because they recited a specific improvement in how computers store, convert, and transmit data in a standardized format, resulting in improved functionality in a networked computing environment. In contrast, the present claims do not recite any specific mechanism for improving data processing or communication. The recitation of a particular data format (multidimensional time series data) without a corresponding technical implementation or improvement is insufficient to establish a practical application.
Additionally, the recitation of discrete-event simulation and synthetic data generation are also directed to analytical modeling techniques. These limitations expand the scope of the analysis but do not provide a technological improvement or impose a meaningful limit on the abstract idea.
For the above reasoning, the claims do not integrate the judicial exception into a practical application.
Under Step 2B, Applicant further argues that the claimed normalization of data into multidimensional time series data and the use of a trained machine-learning model are not well-understood, routine or conventional, and therefore provide an inventive concept; however, the Examiner respectfully disagrees.
The claims do not recite any specific algorithm, architecture, or technical implementation for performing the data normalization or machine-learning operations. Instead, these elements are described functionally and at a high-level of generality. The claims merely require that data be normalized and that a model be used to detect patterns and generate predictions, without specifying how these operations are technically achieved.
As such, these limitations amount to the use of generic data processing and analysis techniques to implement the abstract idea. The mere recitation of advanced analytical tools, such as machine-learning or time series processing, without a specific technological improvement, does not amount to significantly more than the abstract idea.
Further, the additional recitation of simulation and synthetic data generation likewise describe conventional analytical techniques used to model and evaluate outcomes, and do not provide an inventive concept.
Accordingly, the claims do not include additional elements that amount to significantly more than the judicial exception.
For the above reasoning, Applicant’s arguments are not persuasive, and the rejection of the claims under 35 U.S.C. § 101 is maintained.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed an abstract idea without significantly more.
When taking out all the additional elements from the claims, independent claim 1 recites:
retrieve a plurality of insurance contract underwriting terms pertaining to a physical asset;
identify a plurality of risks associated with the physical asset based on the plurality of underwriting terms;
receive external data concerning the physical asset and normalize the external data as multidimensional time series data;
identify a magnitude of each of the plurality of identified risks associated with the physical asset based on the multidimensional time series data; and
identify an upcoming risk event in the geographical location of the physical asset;
generate a vulnerability model that relates the insurance contract underwriting terms and the magnitude of the plurality of risks to determine a risk response of the physical assets to the plurality of risks, wherein the vulnerability model is generated using a trained machine-learning model that automatically detects hidden patterns in historical risk and loss data; and
apply a predicted or hypothetical risk event of interest to the determined risk response to predict a loss associated with the physical asset; and
generate a blended exposures and losses model configured to analyze concentration of losses in terms of time and location;
using discrete-even simulation, perform a plurality of predictive simulations using the blended exposures, hazards, and losses model, wherein the discrete-event simulation progresses time by processing individual events in sequence and updating system state variables in response to each event;
generate synthetic data based on the magnitude of each of the plurality of risks and the predicted loss associated with the physical asset; and
pass the synthetic data through the blended exposures and losses model to determine a concentrated risk of loss associated with a plurality of assets of the same type as the physical asset.
These limitations describe collecting, analyzing and using information to evaluate risk and predict loss, which falls within certain methods of organizing human activity (insurance or risk mitigation) and mental processes (evaluation, judgment or prediction). The additional recitations of machine learning, simulation, and synthetic data do not change the focus of the claim, which remains risk analysis and insurance decision making. Claim 4 is similar to claim 1 and interpreted under the same premise.
Under Step 2A, Prong 2, the claims do not integrate the exception into a practical application.
The additional elements: “processor”, “memory”, “network interface”, “trained machine-learning model”, “multidimensional time series data”, “discrete-event simulation” and “synthetic data generation” are recited at a high-level of generality and merely perform generic data processing functions: receiving data, analyzing data, modeling data and outputting predictions. These additional elements merely describe how to generally “apply” the judicial exception in a computer environment to automate a business process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. There is no improvement to computer functionality, nor any specific implementation of how the ML model is trained or structured, how the time series processing is technically improved, nor how the simulation improves computing performance. Instead, these elements automate the abstract idea on generic computer components, see MPEP 2106.05(f), MPEP 2106.05(g).
The claim(s) does/do not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception because as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using generic computer components i.e. computing processor. The recited elements, individually and in combination: generic computing components, conventional data processing steps, generic machine learning and simulation techniques; are well-understood, routine, and conventional computing functions.
The specification itself describes these components at a high-level as standard computing operations (e.g., receiving, processing and analyzing data). The use of machine learning, discrete-event simulation and synthetic data does not provide an inventive concept because they are invoked functionally without specific technical implementation. Therefore, the independent claims are ineligible.
The dependent claims 2-3 & 5-6 further narrow the abstract idea and do not provide any additional elements individually or in combination that amount to significantly more than the abstract idea. The dependent claims merely recite extra-solution activity that are well-understood, routine and conventional in the realm of systems for risk quantification for insurance process management. For instance, claims 2-3 further define the abstract idea and apply nothing significantly more. For the above reasons, claims 2-3 & 5-6 are ineligible.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.P.K./Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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