Intel Corporation

Ali Ashrafi

Rita Chattopadhyay

Kalpana A Algotar

Amith Harsha

Chetan Patel

John Pilkin

A disclosed example method to predict an injury for a target player on a target date includes determining a first probability of injury of the target player based on probabilities of injuries of second players having similarities with the target player; determining a second probability of injury of the target player based on injuries of the target player; determining a third probability of injury of the target player based on the first probability of injury of the target player and the second probability of injury of the target player; and generating, by executing an instruction with the processor, a report of a predicted probability of injury of the target player for the target date based on the third probability of injury of the target player.

Methods and apparatus to predict sports injuries

Systems and methods for recognizing an analogous case from a knowledge base including a plurality of candidate cases are disclosed. The system may include a data receiver to receive a representation of a target case, and a microprocessor coupled to the data receiver to generate a plurality of target attributes from the representation of the target case. The microprocessor may query the knowledge base to recognize the analogous case similar to the target case in information content using the target attributes and a plurality of attribute memories each including first and second attributes and an inter-attribute contextual representation of interactions between the first and second attributes in a context of the plurality of candidate cases. The system may include an output circuit to output the recognized analogous case to a user or a process.

Similarity-based reasoning with ensemble memory voting

System and techniques for identifying novel information are described herein. A classified experience may be obtained. The classified experience may include a set of attributes. Memory counts of members of the set of attributes for a user may be obtained. A novelty score for the classified experience may be computed by comparing the set of attributes to the memory counts. The classified experience may be presented to the user when the novelty score meets a qualification criterion.

Identifying novel information

Methods and apparatus for detecting anomalies in electronic data are described. An example apparatus includes a signature generator to generate a signature of context information for electronic transactions and to query a memorybase of previously received electronic transactions to identify a first entity associated with a subset of historical transactions that are associated with the signature, a neighborhood generator to query the memorybase to generate a neighborhood for the first entity, a target category identifier to determine a target category value for entities included in the neighborhood, and an anomaly detector to determine a score for the first entity based on the target category value and to present an alert indicating that the first entity is anomalous based on the score.

Growing community of inventors

Chapel Hill, NC, United States of America

Ali Ashrafi