Abstract: An implantable system for managing urinary incontinence includes a sling with an elongate body member having a proximal portion, a distal portion and an intermediate portion. The intermediate portion is configured to be positioned underneath urethra of a subject for providing an adequate support to prevent leakage of urine during a stress event. A pressure sensor communicatively coupled with the elongated body member is configured to be positioned in an abdominal cavity and adapted to sense an increase in intra-abdominal pressure. The pressure sensor generates a first signal that is indicative of a change in the intra-abdominal pressure upon occurrence of the stress event. A processing circuit processes the first signal sensed by the pressure sensor and generates a second signal causing an adjustment of tensioning force in the elongate body member thereby changing magnitude of a supporting force to the urethra.

Abstract: Methods and apparatuses are described for predictive analysis of transaction data using machine learning. A server computing device trains a plurality of machine learning models using historical transaction data for a set of entities as input to predict a likelihood of future transaction activity for each of the entities, each machine learning model trained on a different target transaction variable. The server computing device executes each of the plurality of machine learning models to generate, for each entity, a predicted likelihood value for a future transaction associated with the entity and each of the target transaction variables. The server computing device transmits the predicted likelihood values for each entity to a remote computing device for display.

Abstract: A computer system develops models and generates decision logic based on the developed models. The decision logic is distributed to end user devices, and the end user devices are able to implement the decision logic to detect events, determine event sequences, and correlate the determined event sequences to predicted outcomes.

Abstract: A weather intelligence system retrieves weather forecast data for a number of geographic regions. The weather intelligence system determines, using the weather forecast data for each of the geographic regions, a set of geographic regions predicted to experience a weather anomaly, or unusual weather condition, during a particular time interval. The weather intelligence system determines, through a machine-learning process, whether the weather forecast data indicates conditions that people would generally consider to be unusually hot or cold. The weather intelligence system can then programmatically enable a trigger to transmit a service-related offer associated with the weather anomaly to user devices located within one of the geographic regions where that weather anomaly is determined.

Abstract: A network computer system and method are provided in which each user of a group of users is monitored during a respective online session where the user performs a sequence of M activities, to selectively engage users of the group. A determination is made as to the impact of friction for each user of the group of users with respect to an intention of the respective user, and an action is performed for the user based at least in part on the determined impact of friction.

Abstract: Embodiments provide for a computer system and method to employ a sequence invariant model to determine user intentions, based on monitoring of real-time activities of the user.

Abstract: A computer system operates to detect a series of activities performed by a user, where the activities include interactions as between the user and one or more user interface components. The computer system recognizes the of activities as a sequence of events, where each event of the sequence corresponds to one more activities of the series. In response to the computer system detecting a current user activity, the computer system determines at least one of a user intent or interest based on an analysis of a relevant portion of the sequence of events.

Abstract: A computer system operates to detect a series of activities performed by a user, where the activities include interactions as between the user and one or more user interface components. The computer system recognizes the of activities as a sequence of events, where each event of the sequence corresponds to one more activities of the series. In response to the computer system detecting a current user activity, the computer system determines at least one of a user intent or interest based on an analysis of a relevant portion of the sequence of events.

Abstract: A weather intelligence system retrieves weather forecast data for a number of geographic regions. The weather intelligence system determines, using the weather forecast data for each of the geographic regions, a set of geographic regions predicted to experience a weather anomaly, or unusual weather condition, during a particular time interval. The weather intelligence system determines, through a machine-learning process, whether the weather forecast data indicates conditions that people would generally consider to be unusually hot or cold. The weather intelligence system can then programmatically enable a trigger to transmit a service-related offer associated with the weather anomaly to user devices located within one of the geographic regions where that weather anomaly is determined.

Abstract: In some examples, the designated set of resources are subsequently monitored for session activities of multiple users that are not of the first group. For each of the multiple users, the computer system utilizes one or more predictive models to determine a likelihood of the user performing a desired type of activity based on one or more session activities detected for that user.

Abstract: In some examples, the designated set of resources are subsequently monitored for session activities of multiple users that are not of the first group. For each of the multiple users, the computer system utilizes one or more predictive models to determine a likelihood of the user performing a desired type of activity based on one or more session activities detected for that user.

Abstract: A computer system operates to detect a series of activities performed by a user, where the activities include interactions as between the user and one or more user interface components. The computer system recognizes the of activities as a sequence of events, where each event of the sequence corresponds to one more activities of the series. In response to the computer system detecting a current user activity, the computer system determines at least one of a user intent or interest based on an analysis of a relevant portion of the sequence of events.

Abstract: In some examples, the designated set of resources are subsequently monitored for session activities of multiple users that are not of the first group. For each of the multiple users, the computer system utilizes one or more predictive models to determine a likelihood of the user performing a desired type of activity based on one or more session activities detected for that user.

Abstract: A computer system develops models and generates decision logic based on the developed models. The decision logic is distributed to end user devices, and the end user devices are able to implement the decision logic to detect events, determine event sequences, and correlate the determined event sequences to predicted outcomes.

Abstract: A network computing system, and method for implementing a network computing system, to analyze events accumulated over digital channels of an enterprise, for purpose of determining contextual and/or customized outputs that facilitate a desired objective of the enterprise.

Abstract: Software system facts comprising concepts, concept instances and relationships within the software system are identified and stored in a repository. The software system facts are extracted from artifacts comprising the software system or are provided through explicit definition. Architectures of the software system are recovered from the stored software system facts. Layered views of the recovered architectures are generated. The stored software system facts are checked against architectural rules to ensure architectural compliance of the software system. The impact of proposed changes to the software system is assessed by querying the identified software system facts.

Abstract: Software system facts comprising concepts, concept instances and relationships within the software system are identified and stored in a repository. The software system facts are extracted from artifacts comprising the software system or are provided through explicit definition. Architectures of the software system are recovered from the stored software system facts. Layered views of the recovered architectures are generated. The stored software system facts are checked against architectural rules to ensure architectural compliance of the software system. The impact of proposed changes to the software system is assessed by querying the identified software system facts.

Abstract: Versioning may be utilized in a knowledge base decision tree in order to provide several useful features. To accomplish this, when a decision tree is traversed, the decision tree representing a knowledge base and having non-leaf nodes with one or more branches representing possible symptoms, and leaf nodes with no branches, branches may be followed corresponding to symptoms experience by the application until a leaf node is reached. This traversal may be recorded as a version, with subsequent traversals having a different version. This allows a user to rerun performance tuning either from the beginning or from an earlier node without having to re-enter information already provided. It also allows a user to resume the performance tuning should he be interrupted in the middle, such as by a crash or by having to halt a long traversal.

Abstract: A server has a memory and an analyzer. The memory stores a library of symptom descriptions, a library of corresponding diagnoses, a library of corresponding remedies, and a library of corresponding probes. The analyzer is coupled to the memory and has an identifier, a comparator, and a reiterater. The identifier identifies at least one symptom of an application to be probed based on an input. That input can either be a user input describing the symptoms of the application or symptoms previously already identified. The comparator compares the symptoms of the application with the library of symptom descriptions. The reiterator reiteravely operates the identifier on the comparator until the symptoms correspond with a diagnosis from the library of corresponding diagnoses.

Abstract: In order to diagnose applications, a specialized knowledge base may be created that is static upon creation but may become dynamic when traversed. The knowledge base may be defined as a decision tree having one or more diagnosis nodes, one or more analysis nodes, and one or more symptom branches. The diagnosis nodes are leaf nodes and indicate proposed diagnoses and/or proposed remedies for an application. The symptom branches may connect analysis nodes to other analysis nodes or analysis nodes to diagnosis nodes, and may indicate possible symptoms of the application. The analysis nodes may be non-leaf nodes and indicate information required to determine which symptom branches to follow during traversal. Additionally, the analysis nodes may indicate additional information required from a collector agent before traversal can be continued. This allows the knowledge base to be utilized dynamically, improving performance and reliability.