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
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/12/2025 has been entered.
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 09/12/2025 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.
On page 5, the Applicant submits that:
1. Claim 1 does not recite an/any abstract idea(s);
2. Even assuming arguendo it does, Claim 1 imposes meaningful limitations on any such abstract ideas that confine the scope of any alleged abstract idea(s);
3. These meaningful limitations prevent preemption by applying the concept in a specific, technological way, not merely linking it to a general environment;
4. By imposing meaningful limitations on any abstract idea(s), claim 1 does not preempt the use of the abstract idea(s) by others and therefore integrates any alleged abstract idea into a practical application, rendering it patent eligible under Step 2A, Prong Two of the Alice/Mayo framework;
However, the examiner respectfully disagrees.
Under Step 2A, Prong 1, unlike PowerBlock Holdings, Inc. v. iFit, Inc. the current claims do not recite any specific technological structure or techncail mechanism. Instead, the claims are directed to retrieving underwriting terms, identifying a plurality of risks associated; identifying a magnitude of the plurality of identified risks associated; and identifying an upcoming risk event in the geographical location; generate a vulnerability model that relates the insurance contract underwriting terms and magnitude of the plurality of risks to determine a risk response, wherein the vulnerability model is 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; and generate a blended exposures, hazard and losses models configured to analyze individual, aggregate and concentration of losses in terms of time, location or other factors of interest; using discrete-event simulation, perform a plurality of predictive simulations using the blended exposures, hazards, and losses models, wherein the discrete-event simulation progresses time by processing individual events in sequence and updating system state variables in response to the event; generate synthetic data based on each of the plurality of risks and the predicted loss associated with each exposure, hazard and loss output from an considered scenario associated; and pass the synthetic data through the exposures, hazard and losses analysis process to determine individual, aggregate, and concentrated risk of loss associated. These steps constitute abstract information-processing steps in order to mitigate risks or control outcomes in a commercial or legal context, untethered to any particular hardware configuration. Such risk mitigation through information processing reflects abstract decision-making and methods of organizing human activity, rather than a technological improvement. The claims focus on functional results, rather than a concrete technological solution. Accordingly, unlike PowerBlock, the current claims are directed to an abstract idea, rather than a specific, non-abstract technological structure.
Under Step 2A, Prong 2, Appellant’s reliance on PowerBlock, is unpersuasive. In PowerBlock, the claims were integrated into a practical application because they recited a specific physical structure, a factorized dumbbell, with defined mechanical relationships that produced a real-world, technological result. In contrast, the current claims do not recite a concrete technological implementation or a specific technical solution. The claims merely recite generic information processing steps to achieve outcome as mitigating risk or controlling a commercial or legal process. These steps are performed using generic computing components and do not improve the functioning of a computer or another technology. According, unlike PowerBlock, the claims are not integrated into a practical application, but instead use a computer as a tool to carry out an abstract idea.
The newly added limitations reciting a trained machine-learning model do not integrate the abstract idea into a practical application. The claims merely use the trained model to automate the same business or legal decision-making process, such as evaluating information, determining an outcome, and transmitting a result. The claims do not recite how the machine-trained model is technically trained, structured, or deployed in a manner that improves computer functionality or another technology. Instead, the model is used as a generic computing tool to perform abstract information processing more q=quickly or automatically. Such automation of an abstract idea, even when performed by a machine-learning model, does not constitute a practical application. At most, the claims may recite an improvement to the abstract idea, however, an improvement to the abstract idea is still abstract and non-technical. Accordingly, the claims amount to applying an abstract idea using generic computing components, rather than a technological implementation.
Applicant further argues Step 2B, that the use of a machine trained model provides an inventive concept under Berkheimer v. HP Inc; however, the examiner respectfully disagrees. In Berkheimer, the court explained that an inventive concept must reflect something more than well-understood, routine, and conventional activity. Here, the claims merely recite the use of a machine-trained model or artificial intelligence to automate abstract information-processing steps, such as evaluating data, determining an outcome, and transmitting a result. The claims do not recite a specific model architecture, training technique, or technical improvement to computer functionality. Using a trained model in this manner is analogous to using a generic computer to perform an abstract idea, and constitutes routine automation of a business process, rather than an unconventional technological solution. Accordingly, the claims do not include an inventive concept under Berkheimer.
For the above reasoning, the current 35 U.S.C. § 101 rejection of the claims 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;
identify a magnitude of each of the plurality of identified risks associated with the physical asset; 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.
Thus, under the broadest reasonable interpretation, the claim 1 merely recites apply the upcoming risk event 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; perform a plurality of predictive simulations using the blended exposures, hazards, and losses models; 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. Therefore, both claims are directed towards a “Method of Organizing Human Activity” relating to “fundamental economic principles or practices” i.e. including hedging, insurance, mitigating risk. Further, both claims are related to mental processes including concepts performed in the human mind (including an observation, evaluation, judgment and opinion). Claim 4 is similar to claim 1 and interpreted under the same premise.
The judicial exceptions are not integrated into a practical application because the combination of additional element(s) of a “memory”, “processor”, and “computing device” are recited at a high level of generality i.e., as a generic processor performing generic computational functions as designed to do. 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, see MPEP 2106.05 (f).
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 same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See specification at least paragraphs [038]-[044] & [053] disclosing generic commercially available computing components i.e. the sending, receiving and modifying data steps, see MPEP 2106.05(d)(II). 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
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/T.P.K./Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696