Snap Inc.

Farnaz Azmoodeh

Michael David Marr

Jun Huang

Michael Ronald Cieslak

Jiayao Yu

Kai Chen

Zahra Ferdowsi

Samir Ahmed

Peter Sellis

Douglas Cohen

Brent Davis

Olamide Valerie Olatunji

David Boyle

Claire Reinert

Dhritiman Sagar

Fedir Poliakov

Jinlin Yang

Istvan Marton

Kevin Lee Penner

Cristian Zaloj

Providing branch prediction of APIs to determine the best API for an application to use when performing a function. An API history buffer stores a history of performance data in association with APIs when the function is performed by an application. An API map dynamically stores application performance data in association with APIs. This history data and the API map is used by a circumstance engine and a circumstance engine service to provide a suggested API to an application when it is ready to perform a function.

Branch prediction for APIs

Systems, devices, media, and methods are presented for testing the capability of a mobile device to run a particular feature of an application. Each feature may include a package of code, a call, a state, and a condition; each condition correlated with a specific state. The call is a query that includes instructions to evaluate whether the application will run successfully if a particular feature is running. If the call fails, the feature condition remains Off, resulting in a rollback of the feature. If the call succeeds, the feature condition is changed to On. The system may include an experimentation framework on each mobile device for registering features, executing calls, logging results, and maintaining states and conditions.

Feature rollback for incapable mobile devices

Systems, devices, media, and methods are presented for releasing an application feature in incremental stages while monitoring the application for anomalies. The feature includes a package of code and an action setting. The methods in some implementations include identifying active devices on which the application has been installed, monitoring the application according to a set of metrics, activating the feature by changing its action setting for a first segment of the active devices, pausing the feature if an anomaly is detected among the set of metrics, and generating a repair ticket. As long as no anomaly is detected, the activating step proceeds for subsequent segments of the active devices, iteratively, until the release is completed. A feature rank may be used to process and release a plurality of features in order of priority.

Staged release of updates with anomaly monitoring

Aspects of the present disclosure involve systems and methods for performing operations comprising providing a messaging application comprising a feature to a client device, the feature being implemented by operations having alternative complexity levels, wherein a first complexity level represents a first amount of device resources consumed by a first set of operations, and wherein a second complexity level represents a second amount of device resources consumed by a second set of operations; determining that the first configuration rule is satisfied by a first property of the client device; and in response to determining that the first configuration rule is satisfied by the first property of the client device, causing the feature to be implemented on the client device by the first set of operations having the first complexity level that consume a greater amount of device resources than the second set of operations having the second complexity level.

Automated scaling of application features based on rules

Systems, devices, media, and methods are presented for assigning configuration parameters to unknown devices by comparing characteristics of unknown devices to characteristics of known devices. Characteristics of an unknown device are compared to characteristics of known devices to identify a similar device having known configuration parameters. The unknown devise is then assigned the configuration parameters of the identified similar device.

Device configuration parameter determination

In various embodiments, boundaries of geo-fences can be made mutable based on principles described herein. The term 'mutable' refers to the ability of a thing (in this case, the boundary of a geo-fence) to change and adjust. In a typical embodiment, a mutable geo-fence system is configured to generate and monitor a geo-fence that encompasses a region, in order to dynamically vary the boundary of the geo-fence based on a number of boundary variables. The term “geo-fence” as used herein describes a virtual perimeter (e.g., a boundary) for a real-world geographic area. A geo-fence could be a radius around a point (e.g., a store), or a set of predefined boundaries. Boundary variables, as used herein, refers to a set of variables utilized by the mutable geo-fence system in determining a location of the boundary of the geo-fence.

Mutable geo-fencing system

Systems, devices, media, and methods are presented for determining performance metrics of client devices on a network using benchmark applications. Benchmark applications are distributed to client devices to produce performance metrics for the client devices. Performance metrics of the client devices received from the client devices are grouped to obtain performance metrics for different types of client devices.

Network device performance metrics determination

Systems, devices, media, and methods are presented for throttling (i.e., adjusting) the workload of an application (e.g., number of task requests) in order to improve processor core usage within a heterogeneous multiprocessor system. When high-performance processing is beneficial to the application, the number of task requests may be increased in order to have high-performance processor cores within the heterogeneous multiprocessor system core processor perform the tasks. On the other hand, when high-performance processing is not beneficial, the number of task requests may be decreased in order to have low-performance processor cores within the heterogeneous multiprocessor system perform the tasks. Processor core usage is monitored, and the number of tasks being performed are adjusted to match the processor core usage to a target processor core usage for functions the application is performing.

Core targeting in heterogeneous multiprocessor systems

Aspects of the present disclosure involve systems and methods for performing operations comprising receiving, with a messaging application, user input to access a graphical image modification feature of the messaging application; in response to receiving, causing display of a video; accessing a first configuration rule of a plurality of configuration rules that associates a first device property rule with the graphical image modification feature of the messaging application; determining that the first configuration rule is satisfied by a first property of the client device; and in response to determining that the first configuration rule is satisfied by the first property of the client device, causing display of a first plurality of graphical image modification options each associated with performing a different modification to the video.

Automated graphical image modification scaling based on rules

An example method comprises: receiving, at the server from a first client device, a request for access to a client feature on the first client device; determining, by the server, an applicable rule for the access request, the applicable rule having a plurality of nodes; determining, by the server, device capabilities needed for the determined rule; determining, by the server, nodes that can be executed and nodes that cannot be executed, based on the device capabilities; executing, by the server, nodes that can be executed to reach a partial decision for the applicable rule; pruning the rule to remove executed nodes and generate a pruned rule that includes nodes that cannot be executed; transmitting the pruned rule and partial decision to the device. The pruned rule is executed on the first client device with partial decision to generate a final decision. The client feature is configured based on the decision.

Messaging system with circumstance configuration framework

An example method comprises: receiving, at a server from a first client device, a request for access to a client feature on the first client device; determining, by the server, an applicable rule for the access request, the applicable rule having a plurality of nodes; determining, by the server, device capabilities needed for the determined rule; determining, by the server, nodes that can be executed and nodes that cannot be executed, based on the device capabilities, the nodes that can be executed including device hardware capabilities and the nodes that cannot be executed including real-time device capabilities; executing, by the server nodes that can be executed to reach a partial decision for the applicable rule; pruning the applicable rule to remove executed nodes and generate a pruned rule that includes nodes that cannot be executed; transmitting the pruned rule and partial decision to the first client device.

Messaging system with circumstance configuration framework for hardware

The systems and methods manage thermal states of a device through user configuration of a client application on the device. The systems and methods set thermal thresholds associated with the device. The systems and methods infer the thermal thresholds from information gathered by a client application running on the device. The systems and methods implement a stored policy associated with a violation of one of the thermal thresholds by one of the monitored thermal states.

Configuration management based on thermal state

Systems, devices, media, and methods are presented for categorizing unknown devices using benchmark applications. Benchmark applications are distributed to client devices to produce performance metrics for the client devices. Performance metrics of the client devices are used to categorize unknown devices by comparing the performance metrics of these devices to performance metrics of known devices.

Growing community of inventors

Venice, CA, United States of America

Farnaz Azmoodeh