PATENT CLAIM ANALYSIS

Application Number: 16432467
Application Type: Utility
Filing Date: 2019-06
Publication Date: 2019-12
Patent Classification: ["705", "004000"]

Abstract:
In an illustrative embodiment, systems and methods for calculating risk scores for locations potentially affected by catastrophic events include receiving a risk score request for a location, the risk score request including a request for assessment of risk exposure related to a type of catastrophic event. Based on the type of catastrophic event, a data compression algorithm may be applied to a catastrophic risk model representing amounts of perceived risk to an area surrounding the location. In response to receiving the risk score request, a risk score for the location may be calculated that corresponds to a weighted estimation of one or more data points in a compressed catastrophic risk model. A risk score user interface screen may be generated in real-time to present the catastrophic risk score and one or more corresponding loss metrics for the location due to a potential occurrence of the type of catastrophic event.

Claim (Index 16):
A method comprising:\n receiving catastrophic risk models representing risk to a plurality of entities,\n wherein each of the catastrophic risk models is associated with one of a plurality of types of catastrophic events, and \n wherein each of the catastrophic risk models includes a plurality of data points, each data point of the plurality of data points including at least two dimensions of data including, for each entity of the plurality of entities,\n a) a first dimension of the at least two dimensions corresponding to geographic coordinates of the respective entity, and \n b) a second dimension of the at least two dimensions corresponding to a measure associated with the respective entity; \n \n for each of the catastrophic risk models, compressing, by processing circuitry, the respective catastrophic risk model into a respective compressed risk model, wherein compressing the respective catastrophic risk model includes\n identifying, from the plurality of data points in the respective catastrophic risk model, a first portion of data points that can be estimated from one or more surrounding data points within a predetermined error tolerance,\n wherein the first portion of data points is identified based in part on a density of the geographic coordinates for the respective entities of the first portion of data points and an amount of variation in the measures for the respective entities of the first portion of data points, and \n \n removing, from the respective catastrophic risk model, the first portion of data points, computing, by the processing circuitry in real-time responsive to receiving a risk score request for an entity due to a type of catastrophic event identified in the request, a catastrophic risk score for the entity, wherein\n the catastrophic risk score corresponds to a weighted estimation of one or more of the respective data points in the respective stored compressed risk model for the type of catastrophic event, \n the geographic coordinates for the one or more of the respective data points are located within a predetermined distance of the entity, and \n the request is received from a second remote computing device via the network; and \n \n generating, by the processing circuitry in real-time responsive to receiving the risk score request, a risk score user interface screen presenting the catastrophic risk score for the entity due to a potential occurrence of the type of catastrophic event.

Metadata:
- Claim Count in Document: 41.0
- Percentile: 100.0
- Lexical Diversity: 2.15068
- Patent Class: 705.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['12754189', '14457732', '12347787', '13924316', '15726580']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1016941806602119
- 35 USC 102 Novelty (BERT): 0.4921707653195051
- Combined Prediction Score: 0.1407418391261413
- Mean Citation Score: 149.01846
- Max Citation Score: 153.73956
- Similarity Product: 90.172364706645

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test