Abstract: An example computing system may operate on episodes stored in an episodic memory using a semantic query language. The semantic query language may associate contextual labels with the data ingested from the data sources. Systems described herein may determine probabilities of an event based on the episodes including previous observations, counts, similarities, anomalies, and causality among many other techniques and methodologies. In some examples, the systems described herein may provide result explanations by providing references to source data pertinent to a given result. The user may provide feedback to the query results and update the semantic query.

Abstract: A semantic engine including a group of functions or sub-engines configured to operate together to provide a complete analysis and understanding of digital information in order to provide results or subsequent actions. The semantic engine provide a set of algorithms, including a Naïve Bayes (Bayesian) algorithm, intelligent, self-learning semantic understanding algorithm, lexical analysis algorithm, word trimming algorithm, part-of-speech (POS) tagging algorithm, and textual inference algorithm.

Abstract: Systems and methods for semantically analyzing digital information. A cognitive engine is configured to determine useful evidentiary information from large digital content data sets. Further, the cognitive engine can analyze or manipulate the evidentiary information to derive data needed to solve problems, identify issues, and identify patterns. The results can then be applied to any application, interface, or automation as appropriate.

Abstract: A comparison engine configured to utilize combined semantic-probabilistic algorithms to differentiate and compare an input to obtain enumerated results of similarity (items that are similar to other patent-related references), differences (items that are different from other patent-related references), and uniquenesses (how the input text is distinct from other patent-related references).

Abstract: Systems and methods for semantically analyzing digital information. A cognitive engine is configured to determine useful evidentiary information from large digital content data sets. Further, the cognitive engine can analyze or manipulate the evidentiary information to derive data needed to solve problems, identify issues, and identify patterns. The results can then be applied to any application, interface, or automation as appropriate.

Abstract: A method of processing an auditable age record for an animal. The method includes acquiring an image of select teeth of the animal and associating a date the image was acquired with the image. In addition, the method includes acquiring an image of a specific biometric characteristic of the animal and generating an identification record from the biometric characteristic. In particular, an identification record may be generated from an image of the ocular fundus of the animal. The method also includes creating an auditable age record from input which includes at least the dated image of the select teeth of the animal and the identification record.