Abstract: The present design is directed to a system for measuring values qualitatively across a terrain including multiple dimensions using cognitive computing techniques, comprising a plurality of event reception components configured to operate on each event in a stream relevant to the terrain, the plurality of event reception components including a threshold application component configured to apply a threshold to each element in the stream, a terrain updater configured to update the terrain based on at least one event, an outlier analysis module configured to determine any outlier in the stream of events, a threshold violation predictor configured to predict threshold violations based on the stream of events, a time-ordered behavior evaluator configured to evaluate behavior based on the stream of events, and a graph updater to update a graph based on the stream of events.

Abstract: The present design is directed to a cognitive system including a receiver configured to receive a set of actors and associated actor information and receive assets and their associated asset information, a creation apparatus configured to create data dictionary entries for a taxonomy based on the set of actors and the assets and create a cognitive model using the data dictionary entries for a time period, and a computing apparatus configured to compute trust of the cognitive model as a fuzzy number and activate the cognitive model if trust of the cognitive model is above a cognitive model trust threshold. When the cognitive model is activated, the cognitive modeling system is configured to schedule a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model.

Abstract: The present design is directed to a system for detecting and adjusting qualitative contexts across multiple dimensions for multiple actors with cognitive computing techniques including a series of periodic execution components configured to operate over full or partial sets of received data, the series of periodic components comprising a peer to peer analyzer configured to detect anomalous behaviors among work-specific peer actors sharing similar types tasks, an actor behavior analyzer configured to examine change in an actor's behavior over time by comparing the similarity of past behavior and current behavior, a rate of change predictor configured to study changes in behavior over time for peer to peer performance according to the peer to peer analyzer, actor behavior change according to the actor behavior analyzer, and actor correlation analysis, and a semantic rule analyzer configured to encode conditional, provisional, cognitive, operational, and functional knowledge, and a plurality of signal managers.

Abstract: The present design is directed to a system for performing cognitive modeling, including an event acquirer configured to acquire an event comprising an associated date and set of data fields, an analyzer element comprising a plurality of components repeated for each field in an event received from the event acquirer, wherein the analyzer element applies thresholds to each event, determines outliers, evaluates time-ordered behavior, and predicts threshold violations for the event, a periodic set of components configured to operate periodically on demand, the periodic set of components configured to perform peer to peer analysis, actor correlation analysis, actor behavior analysis, semantic rule analysis, and predict rates of change, and a plurality of signal managers interfacing with the analyzer element and the periodic set of components configured to exclude signals based on content properties of data transmitted.

Abstract: The present design is directed to a system for defuzzification of multiple qualitative signals into human-centric threat notifications using cognitive computing techniques, including a series of periodic execution components configured to operate over full or partial sets of received data, and a plurality of event reception components configured to operate on each event in a stream relevant to a terrain and selectively provide information to the series of periodic execution components. The system detects and signals anomalies upon detecting behavior inconsistent with normal behavior.

Abstract: The present design is directed to a system for measuring values qualitatively across a terrain including multiple dimensions using cognitive computing techniques, comprising a plurality of event reception components configured to operate on each event in a stream relevant to the terrain, the plurality of event reception components including a threshold application component configured to apply a threshold to each element in the stream, a terrain updater configured to update the terrain based on at least one event, an outlier analysis module configured to determine any outlier in the stream of events, a threshold violation predictor configured to predict threshold violations based on the stream of events, a time-ordered behavior evaluator configured to evaluate behavior based on the stream of events, and a graph updater to update a graph based on the stream of events.

Abstract: The present design is directed to a cognitive system including a receiver configured to receive a set of actors and associated actor information and receive assets and their associated asset information, a creation apparatus configured to create data dictionary entries for a taxonomy based on the set of actors and the assets and create a cognitive model using the data dictionary entries for a time period, and a computing apparatus configured to compute trust of the cognitive model as a fuzzy number and activate the cognitive model if trust of the cognitive model is above a cognitive model trust threshold. When the cognitive model is activated, the cognitive modeling system is configured to schedule a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model.

Abstract: The present design is directed to a system for dispatching cognitive computing across multiple workers, comprising a supervisory node configured to dispatch work to a plurality of compute servers forming interconnected knowledge nodes and a relevancy engine provided in at least one knowledge node. The relevancy engine comprises a rule package and a series of processors organized in a tree arrangement, the series of processors configured to perform functions according to the rule package.

Abstract: The present design is directed to a system for detecting and adjusting qualitative contexts across multiple dimensions for multiple actors with cognitive computing techniques including a series of periodic execution components configured to operate over full or partial sets of received data, the series of periodic components comprising a peer to peer analyzer configured to detect anomalous behaviors among work-specific peer actors sharing similar types tasks, an actor behavior analyzer configured to examine change in an actor's behavior over time by comparing the similarity of past behavior and current behavior, a rate of change predictor configured to study changes in behavior over time for peer to peer performance according to the peer to peer analyzer, actor behavior change according to the actor behavior analyzer, and actor correlation analysis, and a semantic rule analyzer configured to encode conditional, provisional, cognitive, operational, and functional knowledge, and a plurality of signal managers.

Abstract: The present design is directed to a series of interconnected compute servers including a supervisory hardware node and a plurality of knowledge hardware nodes, wherein the series of interconnected compute servers are configured to categorize and scale performance of multiple disjoint algorithms across a seemingly infinite actor population, wherein the series of interconnected compute servers are configured to normalize data using a common taxonomy, distribute normalized data relatively evenly across the plurality of knowledge hardware nodes, supervise algorithm execution across knowledge hardware nodes, and collate and present results of analysis of the seemingly infinite actor population.

Abstract: Presently disclosed is a system for attribute-based transaction categorization that utilizes transaction designation attributes other than or in addition to a payee name to provide reduced user effort and improved accuracy in the categorization of transactions. Further, the system for transaction categorization may retroactively re-categorize and/or re-name previous transactions based on subsequent transaction categorization. The transaction categorization system may assign match scores based on the number and/or type of designation attributes that match rules for associating a designation to a transaction. If a match score exceeds a predetermined threshold and/or is greater than other match scores, the transaction is automatically designated. Otherwise, the user may manually designate the transaction. Manually designated transactions may be used by the transaction categorization system to generate new designation rules.

Abstract: Presently disclosed is a system and method for community-based transaction categorization that utilizes previous categorizations of transactions by members of a community of users to categorize a new transaction for an individual member of the community. Each manual categorization of a transaction by a user within the community may be used to generate a community designation rule. Then each community designation rule may be used to categorize transactions of another user within the community. More specifically, when a user within a community manually categorizes a transaction, an individual rule applicable to all the user's transactions with similar attributes is generated. If the individual rule meets certain criteria, the individual rule becomes a community rule. The community rule is then applicable to all users within the community having transactions with similar attributes.