Abstract:
Disclosed are systems and methods to optimize a rules engine as a platform within a computing system. The computing system may identify a context of interest, such as environment or circumstance of the computing system or a user of the computing system. Based on the identified context of interest, the rules engine platform may selectively identify rules or sets of rules that are relevant to the context of interest. Accordingly, rules or sets of rules that are irrelevant to the context of interest may be omitted from evaluation. Therefore, resources of the computing system may not consumed in some embodiments by resolving conflicts between rules and evaluating rules that result in actions that are not suitable for the context of interest.
Abstract:
Disclosed are systems and methods for providing a rules engine as a platform within a portable electronic device. In one embodiment, a rules engine platform is provided within a portable electronic device by receiving a plurality of rules for one or more modules of the portable electronic device. Additionally, the rules engine platform can receive one or more samples from one or more of the modules within the portable electronic device. The rules engine platform identifies and evaluates one or more relevant rules based on the received sample. The rules engine platform can then determine an action to provide to other modules of the portable electronic device. The rules engine platform may be configured to optimize the performance and power consumption of the portable electronic device.
Abstract:
Methods, systems, computer-readable media, and apparatuses for inferring context are provided. In one potential implementation, first context information associated with a first duration is identified, second context information is accessed to determine a context segmentation boundary; and the first context information and the second context information is then aggregated to generate an inferred segmented aggregated context. In a further implementation, the first context information is used to average inferred contexts, and the context segmentation boundary is used to reset a start time for averaging the first context information.