PATENT CLAIM ANALYSIS

Application Number: 16209297
Application Type: Utility
Filing Date: 2018-12
Publication Date: 2019-04
Patent Classification: ["726", "023000"]

Abstract:
Systems and methods include: collecting digital event data for the digital account; using a trained machine learning model to extract account takeover (ATO) risk features from the collected digital event data; evaluating the extracted ATO risk features of the collected digital event data of the digital account against a plurality of ATO risk heuristics; identifying one or more of the plurality of ATO risk heuristics that is triggered by the extracted ATO risk features, wherein one or more of the plurality of ATO risk heuristics may be triggered if at least a subset of the extracted ATO risk features matches requirements of the one or more ATO risk heuristics; and generating an ATO risk assessment for the digital account using the one or more triggered ATO risk heuristics.

Claim (Index 12):
A method of identifying malicious appropriation and/or malicious access of an online account, the method comprising:\n implementing an application programming interface that is in operable communication with a remote digital threat mitigation service and that is configured to generate an API request to the remote digital threat mitigation service for an account takeover (ATO) risk score for an activity session involving the online account; at a remote digital threat mitigation service implemented by one or more computing servers that receive, via a communication network, the API request for the ATO risk score:\n collecting digital event data associated with the online account; \n implementing ATO feature extractors for a machine learning classifier that is trained to classify a plurality of disparate ATO features from the collected digital event data and extract ATO features that signal a positive likelihood of an existence of malicious activity in the activity session involving the online account; and \n implementing an ATO classifier that evaluates a plurality of distinct ATO heuristics based on inputs of the ATO features extracted from the collected digital event data and classifies which of the plurality of distinct ATO heuristics is triggered by the ATO features, wherein evaluating the plurality of distinct ATO heuristics includes mapping each of the ATO features to one or more distinct ATO heuristics of the plurality of distinct ATO heuristics; \n identifying the one or more distinct ATO heuristics of the plurality of distinct ATO heuristics with a mapping to at least one of the ATO features; \n identifying one or more distinct ATO risk levels based on a mapping of each of the identified one or more distinct ATO heuristics to each of a plurality of distinct ATO risk levels; \n computing the ATO risk score for the activity session involving the online account based on the identified one or more distinct ATO risk levels, wherein the ATO risk score indicates a likelihood that the activity session involving the online account is a result of malicious appropriation or malicious access of the online account; and \n returning, via the API, the ATO risk score thereby enabling an online service provider associated with the online account to perform one or more of automatically requesting user verification, approving, holding, and cancelling an activity or an online transaction associated with the activity session if the ATO risk score satisfies an ATO threat threshold.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 98.0
- Lexical Diversity: 2.40351
- Patent Class: 726.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15842379', '16138311', '15957761', '15653373', '15922746']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2590843605475324
- 35 USC 102 Novelty (BERT): 0.583014054148796
- Combined Prediction Score: 0.2914773299076588
- Mean Citation Score: 315.33495
- Max Citation Score: 483.51715
- Similarity Product: 426.1294726281345

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test