DETAILED ACTION
Status of Claims
1. This office action is in response to filing dated 12/31/2024.
2. Claims 1-24 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-24
Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claim 1-12 are directed to a system; claims 13-24 directed to a method – each of which is one of the statutory categories of inventions.
Step 2A: A claim is eligible at revised Step 2A unless it recites a judicial exception and the exception is not integrated into a practical application of the application.
Prong 1: Prong One of Step 2A evaluates whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon).
Groupings of Abstract Ideas:
I. MATHEMATICAL CONCEPTS
A. Mathematical Relationships
B. Mathematical Formulas or Equations
C. Mathematical Calculations
II. CERTAIN METHODS OF ORGANIZING HUMAN ACTIVITY
A. Fundamental Economic Practices or Principles (including hedging, insurance, mitigating risk)
B. Commercial or Legal Interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)
C. Managing Personal Behavior or Relationships or Interactions between People (including social activities, teaching, and following rules or instructions)
III. MENTAL PROCESSES.
Concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
See MPEP 2106.04 (a) (2) Abstract Idea Groupings [R-10.2019]
The limitations of the independent claim 1 and 13 –
[An application processing system comprising:
at least one processor and at least one memory configured to implement a deployed learning model, the deployed learning model generated via training a set of N machine learning models using a dataset of labelled features and associated values gathered across a network from input on an electronic user interface relating to applications and wherein the labelled features indicates a processing metric for the dataset, the dataset split into N folds for predicting straight through processing, each model of the set of machine learning models is trained on all but one fold of the dataset and tested on other remaining fold of the dataset, wherein the models are tested on non-overlapping datasets and repeated until all models are trained wherein a resultant model providing the deployed learning model is generated by aggregating results via model ensembling from training each said model of the set of machine learning models, the at least one processor further configured to]:
automatically process a first input on the [electronic user interface] having a plurality of associated features for a first underwriting application using the deployed learning model to generate a first processing metric for the first input; and
apply the first processing metric to a decision module having a defined threshold for straight through processing and responsive to the first processing metric exceeding the defined threshold, the at least one [processor] further configured to:
process via applying straight through processing, using the at least one [processor], the first underwriting application; and
sending, to a [computing device] associated with the first underwriting application and based on processing the first underwriting application, a display indication to output a result of processing the first underwriting application
– fall under the abstract idea categories Mental Process and/or Certain Methods of Organizing Human Activity.
The dependent claims –
(Claim 2, 14) wherein during generating of the deployed learning model, the at least one [processor] is further configured to perform feature ablation to determine features of interest and ablate remaining features via k-fold cross validation using holdout of a selected feature at a time, wherein each model is tested on a single fold which it was not trained on while removing one feature at a given time from a training and testing dataset provided in the dataset of labelled features and aggregating results to determine performance of models trained on data with the one feature removed via a performance metric and applying a defined feature threshold to the performance metric to determine features of interest having a highest performance metric.
(Claim 3, 15) wherein the at least one [processor] is further configured to perform hyperparameter optimization using k-fold cross validation performed after feature ablation on only non-ablated features.
(Claim 4, 16) wherein the dataset of features is selected from at least one of: binary, categorical and numerical data input into the electronic user interface of an associated [computing device] accessing an application programming interface.
(Claim 5, 17) wherein the at least one [processor] is further configured, to apply the first underwriting application to a rule based decisioning model to determine, via applying a defined set of rules to associated features of the dataset of the first underwriting application, an initial indication of whether to further process the first underwriting application via the [deployed learning model]; based upon a positive response for further processing, the at least one processor is configured to provide the first underwriting application to the [deployed learning model] for predicting straight through processing.
(Claim 6, 18) wherein the at least one [processor] is further configured to train the set of machine learning models based on a new dataset of labelled features and compare performance to a prior iteration to determine which instance of the machine learning models to utilize based on increased relative performance.
(Claim 7, 19) wherein the at least one [processor] is further configured to confirm validity of the deployed learning model by applying out of time data as a test set and averaging prediction results from each of the set of [machine learning models] to determine a prediction score.
(Claim 8, 20) wherein each of the set of [machine learning models] utilizes a supervised extreme gradient boosted (XGBoost) model.
(Claim 9, 21) wherein the set of machine learning models comprises 5 models and a same threshold is applied to all models to determine whether to apply straight through processing to the first underwriting application.
(Claim 10, 22) wherein performing the hyperparameter optimization further comprises the at least one [processor] configured to apply Bayesian optimization for hyperparameter tuning of each said model of the set of machine learning models.
(Claim 11, 23) wherein the at least one [processor] is configured to apply a plurality of decision trees via an XGBoost model to determine the defined threshold at the decision module.
(Claim 12, 24) wherein the at least one [processor] further converts the first input on the electronic user interface to a comma separated value file having a similar format of features to the dataset of labelled features used for training the set of machine learning models prior to applying to the deployed learning model.
– fall under Mathematical Concepts and/or Mental Process and/or Certain Methods of Organizing Human Activity.
Hence under Prong One of Step 2A, claims 1-24 recite a combination of judicial exceptions.
Prong 2: Prong Two of Step 2A evaluates whether the claim recites additional elements that integrate the judicial exception into a practical application of the exception.
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
Additional element(s) recited by the claims, beyond the abstract idea, include: an application processing system comprising at least one processor and at least one memory; computing device; machine learning model; electronic user interface. Examiner finds that any additional element(s), beyond the judicial exception, has been recited at a high level of generality such that the claim limitations amount to no more than mere instructions to apply the exception using generic components (see MPEP 2106.05(f)) or insignificant data gathering activities (see MPEP 2106.05(g)).
The combination of additional elements does not purport to improve the functioning of a computer or effect an improvement in any other technology or technical field. Instead, the additional elements do no more than use the computer as a tool and/or link the use of the judicial exception to a particular technological environment or field of use. The focus of the claims is not on improvement in computers or machine learning, but on certain independently abstract ideas – automatically process a first input on the [electronic user interface]e having a plurality of associated features for a first underwriting application using the deployed learning model to generate a first processing metric for the first input; and apply the first processing metric to a decision module having a defined threshold for straight through processing and responsive to the first processing metric exceeding the defined threshold, the at least one [processor] further configured to: process via applying straight through processing, using the at least one processor, the first underwriting application; and sending, to a [computing device] associated with the first underwriting application and based on processing the first underwriting application, a display indication to output a result of processing the first underwriting application – that merely uses processor and trained machine learning model as tools. Steps that do no more than spell out what it means to “apply it on a computer” cannot confer patent eligibility. Indeed, nothing in claim 1 improves the functioning of the computer, makes it operate more efficiently, or solves any technological problem. See Trading Techs. Int’l, Inc. v. IBG LLC, 921 F.3d 1378, 1384-85 (Fed. Cir. 2019).
Hence, under Prong Two of Step 2A, the additional elements, when considered individually or in combination, do not integrate the judicial exception into a practical application.
Hence, the claims are ineligible under Step 2A.
Step 2B:
In Step 2B, the evaluation consists of whether the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception.
As discussed in Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic components. When considered individually or as an ordered combination, the additional elements fail to transform the abstract idea of – automatically process a first input on the [electronic user interface]e having a plurality of associated features for a first underwriting application using the deployed learning model to generate a first processing metric for the first input; and apply the first processing metric to a decision module having a defined threshold for straight through processing and responsive to the first processing metric exceeding the defined threshold, the at least one [processor] further configured to: process via applying straight through processing, using the at least one processor, the first underwriting application; and sending, to a [computing device] associated with the first underwriting application and based on processing the first underwriting application, a display indication to output a result of processing the first underwriting application – into significantly more.
See MPEP 2106.05(f) Mere Instructions To Apply An Exception [R-10.2019].
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Hence, the claims are ineligible under Step 2B.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to a judicial exception without significantly more.
Prior Art
Relevant Prior Art not relied upon but made of record:
TWM569030U Underwriting system using artificial intelligence
KR20220012071A Underwriting system using artificial intelligence
US20240281889 Artificial intelligence (ai) to aid underwriting and insurance agents
US20240054567 Smart underwriting system with fast, processing-time optimized, complete point of sale decision-making and smart data processing engine, and method thereof
US20230410208 Machine learning-based, predictive, digital underwriting system, digital predictive process and corresponding method thereof
US20190180379 Life insurance system with fully automated underwriting process for real-time underwriting and risk adjustment, and corresponding method thereof
Machine Learning in Insurance Underwriting Context by Boyue Yang, 2021 International Conference on Electronic Information Technology and Smart Agriculture (ICEITSA)
Conclusion
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/ARUNAVA CHAKRAVARTI/Primary Examiner, Art Unit 3692
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