Mx Technologies, Inc.

Josh Bodily

Brandon Dewitt

Ryan McBride

Shane Smit

Ryan Mcbride

Apparatuses, methods, systems, and computer program products are presented for dynamic feature and performance testing and adjustment. An audit module is configured to dynamically test a plurality of image capture settings for a camera of a mobile device of an end user in an executable mobile application executing on the mobile device. A feature module is configured to select one of a plurality of image capture settings for a camera of a mobile device based on a dynamic test. An adjustment module is configured to dynamically configure, during runtime of an executable mobile application on a plurality of different mobile devices of different end users, the different mobile devices to use a selected one of a plurality of image capture settings.

Dynamic feature and performance testing and adjustment

Apparatuses, methods, systems, and program products are presented for securing data based on randomization. An apparatus includes a security component that is configured to determine, during compile time of an application, at least one random value used to determine a partition size, a set of storage locations, and an access threshold for a data element during execution of the application. An apparatus includes a partition module that is configured to partition a data element into a plurality of data segments that each have a size that is randomly determined, during execution of an application, based on at least one random value to determine the partition size. An apparatus includes a storage module that is configured to randomly determine a first set of storage locations for a plurality of data segments, during execution of an application, based on at least one random value for the set of storage locations.

Securing data based on randomization

Apparatuses, systems, methods, and computer program products are presented for a propensity module based optimization. An apparatus comprises a processor and a memory that stores code executable by the processor to receive an electronic submission for a pass/fail interface, identify information from the electronic submission to suggest to a user for entering into an input field for the pass/fail interface prior to submitting the electronic submission to the pass/fail interface to reduce a likelihood that the electronic submission will be rejected at the pass/fail interface, determine the likelihood that the electronic submission will be accepted by the pass/fail interface, and submit the electronic submission to the pass/fail interface in response to the likelihood satisfying a threshold.

Propensity model based optimization

Apparatuses, methods, systems, and program products are presented for securing data based on randomization. An apparatus includes a partition module that is configured to partition a data element into a plurality of data segments. An apparatus includes a storage module configured to randomly determine a first set of storage locations for a plurality of data segments such that the data segments are not stored in contiguous storage locations. An apparatus includes a threshold module configured to randomly determine an access threshold for a data element. An access threshold, in certain embodiments, comprises a number of times a data element can be accessed before determining different storage locations for a plurality of data segments, which may be reconstituted in response to a data access. A storage module randomly determines a second set of storage locations for a plurality of data segments in response to an access threshold being satisfied.

Apparatuses, systems, methods, and computer program products are presented for a propensity module based optimization. A mobile computing device comprises one or more of a camera, a network interface, and an optimization module. An optimization module is configured to process one or more images from a camera using machine learning to determine a likelihood that the one or more images will pass submission to a pass/fail interface over a network interface. An optimization module is configured to submit one or more images to a pass/fail interface over a network interface in response to a likelihood satisfying a threshold.

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Lehi, UT, United States of America

Josh Bodily