Amplero, Inc.

Curinos, Inc.

Luca Cazzanti

Oliver B Downs

Courosh Mehanian

Jesse Hersch

Kyle David Estlick

Sidney Hong

Matthew G Danielson

Andrew Thomas Shade

Julie Penzotti

Scott Allen Miller

Doug Scott

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 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

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

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Seattle, WA, United States of America

Luca Cazzanti