Abstract: Adaptive modification and presentment of user interface elements in a computerized therapeutic treatment regimen. Embodiments of the present disclosure provide for non-linear computational analysis of cData and nData derived from user interactions with a mobile electronic device executing an instance of a computerized therapeutic treatment regimen. The cData and nData may be computed according to one or more artificial neural network or deep learning technique to derive patterns between computerized stimuli or interactions and sensor data. Patterns derived from analysis of the cData and nData may be used to define an effort metric associated with user input patterns in response to the computerized stimuli or interactions being indicative of a measure of user engagement or effort. A computational model or rules engine may be applied to adapt, modify, configure or present one or more graphical user interface elements in a subsequent instance of the computerized therapeutic treatment regimen.

Abstract: Adaptive modification and presentment of user interface elements in a computerized therapeutic treatment regimen. Embodiments of the present disclosure provide for non-linear computational analysis of cData and nData derived from user interactions with a mobile electronic device executing an instance of a computerized therapeutic treatment regimen. The cData and nData may be computed according to one or more artificial neural network or deep learning technique to derive patterns between computerized stimuli or interactions and sensor data. Patterns derived from analysis of the cData and nData may be used to define an effort metric associated with user input patterns in response to the computerized stimuli or interactions being indicative of a measure of user engagement or effort. A computational model or rules engine may be applied to adapt, modify, configure or present one or more graphical user interface elements in a subsequent instance of the computerized therapeutic treatment regimen.

Abstract: Adaptive modification and presentment of user interface elements in a computerized therapeutic treatment regimen. Embodiments of the present disclosure provide for non-linear computational analysis of cData and nData derived from user interactions with a mobile electronic device executing an instance of a computerized therapeutic treatment regimen. The cData and nData may be computed according to one or more artificial neural network or deep learning technique to derive patterns between computerized stimuli or interactions and sensor data. Patterns derived from analysis of the cData and nData may be used to define an effort metric associated with user input patterns in response to the computerized stimuli or interactions being indicative of a measure of user engagement or effort. A computational model or rules engine may be applied to adapt, modify, configure or present one or more graphical user interface elements in a subsequent instance of the computerized therapeutic treatment regimen.

Abstract: In various embodiments, systems and methods for Multi-Tenant In-Memory Database access to an application program accessing an object-oriented database (OODB) are disclosed. Embodiments may include: connecting, by a processor, the application program to a data repository for a database; generating, by the processor, a first Firm for a first tenant; placing, by the processor, an address of a first object of the first Firm into a knowledge base pointer associated with the first tenant; placing, by the processor, an address of a main database object of the database into a pointer in an object of the first Firm; and accessing, by the processor, the first Firm through the knowledge base pointer and the database through the pointer object of the first Firm.