Lytx, Inc.

Fair Isaac Corporation

Gabriela Surpi

Daniel A Lambert

Syrus C Nemat-Nasser

Larry Richardson

Quoc Chan Quach

Timothy Edmonds

Brett Hoye

A system for determining risky events includes an input interface and a processor. The input interface is for receiving sensor data on environmental conditions. The processor is for determining whether the environmental conditions indicate an increase in event probability and, in the event that environmental conditions indicate the increase in the event probability, adjusting an event detection threshold.

Environmental condition-based risk level

A system for determining proximity events includes an input interface and a processor. The input interface is to receive a lane change information. The processor is to determine a proximity event based at least in part on the lane change information.

Proximity event determination with lane change information

A system for transferring data comprises an input interface and a processor. The input interface is configured to receive event information from a vehicle event recorder including an event type for each event associated with the event information. The processor is configured to determine a historical coachable profile for event types associated with the event information and select a group of events to transfer based at least in part on the historical coachable profile for the event types.

Back-end event risk assessment with historical coaching profiles

A system for decoupling accelerometer signals includes an interface and a processor. The interface is configured to receive accelerometer data and receive coarse behavior data. The processor is configured to determine a coarse acceleration based at least in part on the coarse behavior data, determine a perturbation acceleration using the coarse acceleration and the accelerometer data, and determine an anomalous event based at least in part on the perturbation acceleration.

Decoupling of accelerometer signals

A system for driver risk determination using machine vision includes an interface and one or more processors. The interface is to receive one or more video data streams. The one or more processors is/are to determine, whether a driving behavior risk type appears in one of the one or more video data streams; and in the event that the driving behavior risk type appears in the one of the one or more video data streams, indicate the driver behavior risk type.

Detecting risky driving with machine vision

A system for frequent data readings includes an input interface and a processor. The input interface is to receive frequent data readings. The frequent data readings include a newest data reading. The processor is to determine a simplified running characteristic based at least in part on the newest data readings and a last simplified running characteristic, and store the simplified running characteristic.

Running characteristic for frequent data readings

A system for using a traffic proxy measure includes an input interface and a processor. The input interface is to receive a vehicle data and a road data. A processor is to determine a traffic proxy measure based at least in part on the vehicle data and the road data.

Traffic estimation

A dynamic uploading protocol comprises an input interface configured to receive a manifest comprising a plurality of events which may be uploaded; wherein the manifest additionally comprises sensor information relating to each of the plurality of events. The system for a dynamic uploading protocol additionally comprises a processor configured to determine whether to upload additional information about each event, wherein determining whether to upload additional information about each event is based at least in part on the sensor information and contextual information. The system for a dynamic uploading protocol additionally comprises an output interface configured to request the additional information. The system for a dynamic uploading protocol additionally comprises a memory coupled to the processor and configured to provide the processor with instructions.

Dynamic uploading protocol

A system and method for building a predictive score without model training are disclosed. A set of predictive variables is defined based on raw data fields generated from raw data from one or more sources and domain knowledge. The raw data includes a historical set of transactions previously generated by one or more raw data sources. An scaled relative risk table to describe each predictive variable of the set of predictive variables is generated. The set of predictive variables is combined based on their associated relative risk tables to generate a predictive score for a future set of transactions.

Growing community of inventors

San Diego, CA, United States of America

Gabriela Surpi