Inrix Inc.

Atigeo Corporation

Groupon, Inc.

Here Global B.v.

Amplero, Inc.

Blab, Inc.

Pelago, Inc.

Inrix, Inx.

Curinos, Inc.

Oliver B Downs

Craig H Chapman

Robert Cahn

Alec Barker

Jesse S Hersch

Kush G Parikh

Michael Sandoval

Darren Erik Vengroff

Claudiu Alin Branzan

Vlad Mircea Iovanov

Sopurkh Singh Khalsa

Luca Cazzanti

Toby Tennent

Scott R Love

Mitchel A Burns, Jr

David Aaron Snelling

Praveen Arcot

Bernhard G Zimmermann

Randolph Browning

Stefan Nicholas Papp

Todd Asher

Radu Ioan Bisca

Catalin Teodor Milos

Mitchel A Burns

Bruce A Bernhardt

Courosh Mehanian

David Bradley Boardman

Wayne Stoppler

Julie Penzotti

Kyle David Estlick

Sidney Hong

Andrew Thomas Shade

Matthew G Danielson

Scott Allen Miller

Doug Scott

Jesse Hersch

James Allen England

Robert C Cahn

Techniques are described for automatically updating settings of computing devices and/or accounts, such as to improve security to prevent unauthorized intrusions. Effectiveness of particular configuration settings may be analyzed after attempted intrusions and/or messages sent to associated users to prompt performance of unauthorized actions. Configuration settings to automatically apply and/or messages to be sent may be selected using reinforcement learning with a selection policy based on multi-armed bandit techniques with Thompson Sampling to learn and exploit the most effective messages, and decision tree(s) may be generated whose leaf nodes each represents a learned segment with a group of computing devices and/or users who share common attributes, and describe a value function for the reinforcement learning. One or more trained large language models (LLMs) may also be used to further generate new messages by modifying aspects of existing messages, such as for the most effective identified messages.

Automated device and account security setting update initiation

Techniques are described for determining locations of interest based on user visits. In some situations, the techniques include obtaining information about actual locations of users at various times, and automatically analyzing the information to determine particular locations in a geographic area that are of interest, such as for frequent destinations visited by users. After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).

Determining locations of interest based on user visits

In one embodiment, an incident report including a path segment identifier and an incident identifier is received at a computing device. The incident identifier is sent to a traffic prediction model. The traffic prediction model returns a traffic distribution value. The traffic distribution value identifies a portion of a traffic prediction distribution derived from historical data. The computing device accesses a lookup table according to traffic distribution value and the path segment identifier to receive a speed prediction.

Predictive incident aggregation

Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads, as well as in some situations data from one or more other sources (such as physical sensors near to or embedded in the roads). The assessment of road traffic conditions based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics from the data samples. In some situations, the filtering of the data samples includes identifying data samples that are inaccurate or otherwise unrepresentative of actual traffic condition characteristics, such as data samples that are not of interest based at least in part on roads with which the data samples are associated and/or that otherwise reflect vehicle locations or activities that are not of interest.

Filtering road traffic condition data obtained from mobile data sources

Techniques are described for automatically and dynamically modifying ongoing operations of computing devices in device-specific manners, such as based on an automated identification of a computing device's status (e.g., identifying a likely ongoing or imminent failure of a smart phone or other computing device based on a series of observed hardware states of the computing device, and taking automated corrective actions to prevent or otherwise mitigate such device failure, such as by modifying configuration settings on the computing device or on associated systems). The techniques may include, for each of multiple device status outcomes of interest (e.g., device failure versus device non-failure), generating a state-space outcome model representing devices that reach that status outcome within a time period of interest, and using such outcome models to identify a likely ongoing or imminent outcome of a current device, with corresponding automated corrective actions then taken.

Automated identification of device status and resulting dynamic modification of device operations

Techniques are described for automatically and dynamically modifying ongoing operation of computing devices in device-specific manners, such as to improve ongoing performance of the computing devices by modifying configuration settings on the computing devices or on associated systems in communication with the computing devices. The techniques may include generating one or more decision structures that are each specific to a type of measured performance effect, and using the decision structure(s) to improve corresponding performance of a computing device, with the generating of the decision structure(s) including analyzing training data that associates prior measured performance effects with corresponding attributes of computing devices and of modification actions that were performed for the computing devices. Non-exclusive examples of modifying operation of a smartphone computing device include modifying configuration settings affecting use of one or more of the device's battery, memory, storage and network communications.

Automated device-specific dynamic operation modifications

A method and apparatus for traffic modeling comprising: receiving by a server, traffic data generated by one or more devices configured to record data indicating speeds of vehicles traveling a road segment. The server separates, with a processor, the traffic data into zero speed data and non-zero speed data. The server determines, with the processor, a zero speed data characteristic value of the zero speed data. The server determines, with the processor, at least one non-zero speed data characteristic value of the non-zero speed data. The server generates, with the processor, a representative traffic value for the road segment as a function of the zero speed characteristic value and the non-zero speed characteristic value.

Traffic speed modeling

Techniques are described for analyzing user-supplied information, including in at least some situations to predict future aspects of additional related information that will be supplied by users. The user-supplied information that is analyzed may, for example, include distributed group discussions that involve numerous users and occur via user comments made to one or more social networking sites and/or other computer-accessible sites. The analysis of user-supplied information may, for example, include determining particular topics that are of interest for a specified category during one or more periods of time, quantifying an amount of user interest in particular topics and the category during the period of time, predicting future amounts of user interest in the particular topics and the category during one or more future period of times, and taking one or more further actions based on the predicted information.

Analyzing distributed group discussions

Techniques are disclosed that automatically identify and order the most differentiated clusters from a given collection of clusters within a dataset. A measure of dissimilarity is computed for each cluster from a defined reference cluster, and the clusters are ordered according to the chosen dissimilarity. At least N clusters are selected as the most differentiated clusters relative to the defined reference. Within each cluster, the top-M most distinguishing cluster attributes can be automatically identified by an analogous process that computes the dissimilarity of each cluster attribute to its corresponding attribute in the reference cluster, and orders the attributes by dissimilarity. This then allows for automatic surfacing of what it is about a cluster that differentiates its members relative to the population as a whole, and to provide insight on what action or treatment might be made to address that specific segment of the underlying population.

Unsupervised prioritization and visualization of clusters

Techniques are described for determining and using information related to domains of interest, such as by automatically analyzing documents and other information related to a domain in order to automatically determine relationships between particular terms within the domain. Such automatically determined information may then be used to assist users in obtaining information from the domain that is of interest (e.g., documents with contents that are relevant to user-specified terms and/or to other terms that are determined to be sufficiently related to the user-specified terms). For example, recommendations may be automatically generated for a user by using information about specified preferences or other interests of the user with respect to one or more terms and identifying other particular terms that are sufficiently probable to be of interest to that user, such as based on a generated probabilistic representation of relationships between particular terms for the domain.

Providing recommendations using information determined for domains of interest

Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Filtering road traffic data from multiple data sources

Growing community of inventors

Seattle, WA, United States of America

Oliver B Downs