DETAILED ACTION
This Office Action is in response for Application # 18/444,560 filed on February 16, 2024 in which claims 1-21 are presented for examination.
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
Claims 1-21 are pending, of which claims 1-21 are rejected under 35 U.S.C. 101.
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.
Claim 1-21 are rejected under 35 U.S.C. 101. because the claims are directed to an abstract idea; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—Claim 1-20 recite a method and apparatus respectively.
The analysis of claims 1, 11 and 21 are as follows:
Step 2A, prong one:
The claim recites the following limitations which are drawn towards an abstract idea:
obtaining, from a database, first information comprising tabular features identifying prior instances of policies for at least one product transacted with a merchant entity (recites mental process with pen and paper step of getting data from a database);
obtaining, from the database, second information comprising social connection features identifying relationships between components of the policies for the at least one product and overlapping values for the components (recites mental process with pen and paper step of getting data from a database);
concatenating the first and second information into a tabular format to form a model generation data set (recites mental process with pen and paper step of getting data from a database);
applying the training data set, in a training phase, having unlabelled data to a tree classifier network for training an unsupervised isolation forest model for anomaly detection by generating an ensemble of decision trees, each decision tree setting different splitting conditions based on an unsupervised learning of the training data and providing an initial output indicative of a probability of anomaly for a given input and generating an output of the unsupervised isolation forest model during training based on a weighted combination of the initial output from each decision tree, the output indicative of a total probability of anomaly (recites an algorithmic process of applying the training unlabelled data set of splitting conditions based on an unsupervised learning and recites at a high-level of generality);
applying the tuning data set, in a tuning phase, having the labelled data indicative of fraudulent activity to tune the trained model by applying the tuning data set to the trained model to detect a set of anomalies corresponding to the tuning data set, the anomalies having the total probability generated from the ensemble of decision trees higher than a defined threshold, determining whether the set of anomalies detected corresponds to the labelled data indicative of fraudulent activity and responsive to a difference between the set of anomalies detected and the labelled data, modifying features of the trained model iteratively by retuning the model until the set of anomalies detected corresponds to the labelled data to generate a tuned model that indicates a likelihood of fraudulent activity based on the anomalies detected (recites an algorithmic process of applying the training labelled data set of splitting conditions based of identifying fraudulent activity of detecting anomalies and recites at a high-level of generality);
applying a first data set having a first feature set associated with a new policy from a requesting device received via the communication interface for the entity to the tuned model to determine, based on the output of the isolation forest model previously tuned, the probability of fraudulent activity as a weighted combination of outputs from each of the ensemble of decision trees from the tuned model (recites an algorithmic process of applying the feature set with a new policy set of splitting conditions based of identifying fraudulent activity of detecting anomalies and recites at a high-level of generality).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
splitting the model generation data set into a training data set and a tuning data set based on whether a data sample is labelled for fraudulent activity based on the prior instances, wherein the tuning data set comprises labelled data for fraudulent activity (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract idea of computation of a splitting the model generation data, see MPEP 2106.05(f));
responsive to determining the probability of fraudulent activity for the first data set exceeds a first threshold, displaying the probability and the new policy associated therewith on a graphical user interface and routing the first data set to a second computing device via the communication interface, across a communication network, for flagging and denying processing of the new policy and notifying the requesting device (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract idea of computation of determining the probability of fraudulent activity, see MPEP 2106.05(f)).
This judicial exception is not integrated into a practical application because, as seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). The additional elements merely recite, at a high-level of generality, training portions of neural networks.
Step 2B:
Below is the analysis of the claims:
A computer implemented system comprising: a communication interface; a memory storing instructions; one or more processors coupled to the communication interface and to the memory, the one or more processors configured to execute the instructions to perform operations comprising (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract idea of training portions of generative model, see MPEP 2106.05(f)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements merely recite, at a high-level of generality, dynamic threshold mechanism of similarity score distribution.
The analysis of claims 2-10 and 12-20 are as follows:
Step 2A, prong one:
The claim recites the following limitations which are drawn towards an abstract idea:
Claims 2 and 12 recites perform operations, comprising: responsive to determining the probability of fraudulent activity for the first data set is below the first threshold, displaying the probability on the graphical user interface and routing the first data set to the second computing device via the communication interface for allowing processing of the new policy and notifying the requesting device (recites at a high-level of generality of using graphical user interface to display probability).
Claims 3 and 13 recites determining a defined set of top contributing features for all input features in the model generation data set contributing to the detection of a particular anomaly not corresponding to thereby not indicative of fraudulent activity based on the labelled data in the tuning data set; removing the set of top contributing features in the tuning data set and the training data set to bias the model to consider other features in an updated feature set; and iteratively retraining and retuning the model based on the updated feature set indicative of fraudulent activity to generate an updated model for subsequent incoming policies (recites at a high-level of generality of detection of a particular anomaly).
Claims 4 and 14 recites determining the defined set of top contributing features contributing to the detection comprises applying depth based isolation forest feature importance (DIFFI) to generate DIFFI values providing a measure of feature contribution of each feature for all the input features in the model generation data set to splitting and isolation of anomalous cases in the generated ensemble of decision trees by the unsupervised isolation forest model trained and applying the feature contribution to remove features with DIFFI values below a selected threshold and repeating iteratively training and tuning of the model based on remaining features to determine the selected threshold to provide a desired feature set having an improved correlation between anomaly detection as compared to an indication of fraudulent activity in the labelled data compared to a prior iteration of the model (recites at a high-level of generality of applying depth based isolation forest feature importance).
Claims 5 and 15 recites rendering the likelihood of fraudulent activity and the measure of feature contribution provided via DIFFI values for each feature of a set of input features for the new policy contributing to anomaly detection prediction, as interactive interface elements on the graphical user interface (recites at a high-level of generality of using graphical user interface to display probability and measure of feature contribution provided via DIFFI).
Claims 6 and 16 recites receiving additional features or updated features for the model generation data set in a subsequent model iteration and removing features one at a time in each iteration of model training and model tuning to compare performance change as compared to the labelled data during the tuning phase to determine an optimal set of features for generating the unsupervised isolation forest model (recites at a high-level of generality of removing features one at a time in each iteration of model training).
Claims 7 and 17 recites applying a plurality of data sets for new policies associated with the merchant entity to the tuned model and determining a ranked list of each of the new policies based on the likelihood of fraudulent activity determined from the tuned model (recites at a high-level of generality of removing features one at a time in each iteration of model training).
Claims 8 and 18 recites a set of labelled fraudulent policies interspersed with a set of labelled non-fraudulent policies (recites at a high-level of generality of labelled non-fraudulent policies).
Claims 9 and 19 recites unlabelled fraudulent and non-fraudulent policies (recites at a high-level of generality of unlabelled fraudulent and non-fraudulent policies).
Claims 10 and 20 recites operations of the processor to generate a social network graph of connectivity between components of the policies comprising policy information, policy holder information, identification information for the at least one product, and social entities along with associated values for the components, wherein graph links are connected between a set of nodes relating to a set of policies sharing a same component value (recites at a high-level of generality of generate a social network graph of connectivity between components of the policies).
As seen from above, the identified limitations recite concepts associated with an abstract idea and thus the respective claim recites a judicial exception (see 2106.04(a)) and thus requires further analysis as discussed below.
Step 2A, Prong Two:
The following limitations have been identified as being additional elements as discussed below.
Claims 7 and 17 recites operations further comprise: rendering the ranked list as interactive interface elements on the graphical user interface for receiving input accepting or denying each policy and operations of the processor further configured to feed back the input to retrain and retune the isolation forest model (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract idea of training portions of generative model, see MPEP 2106.05(f)).
This judicial exception is not integrated into a practical application because, as seen from the above discussion, the identified limitations did not integrate the judicial exception into a practical application (see MPEP 2106.04(d)). The additional elements merely recite, at a high-level of generality, training portions of neural networks.
Step 2B:
Below is the analysis of the claims:
A computer implemented system comprising: a communication interface; a memory storing instructions; one or more processors coupled to the communication interface and to the memory, the one or more processors configured to execute the instructions to perform operations comprising (recites implementing the abstract idea on a generic computer hardware which amounts to merely using the computer as a tool to implement the abstract idea of training portions of data for fraudulent transactions, see MPEP 2106.05(f)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements merely recite, at a high-level of generality, training portions of neural networks.
Allowable Subject Matter
Claims 1-21 are allowed over prior art. However, the claims need to overcome 35 U.S.C. 101 rejection for allowability.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J Lo can be reached on (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAVAN MAMILLAPALLI/
Primary Examiner, Art Unit 2159
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