Abstract: Machine learning-based techniques for predicting trademark similarity are described. For instance, a first trademark pair comprising an attribute of a first trademark and a corresponding attribute of a second trademark is received. A level of similarity is determined between the first trademark pair and respective second trademark pairs included in respective legal proceedings maintained in a database. A subset of proceedings from the proceedings is selected, each proceeding of the subset including a respective second trademark pair that has the level of similarity with the first trademark pair. A feature vector is generated based on each proceeding of the subset. The feature vector is provided to a machine learning model that outputs a prediction score, based on the feature vector, as to whether a subsequent proceeding would find a likelihood of confusion between the first trademark and the second trademark. The prediction score is provided to a user interface.

Abstract: An apparatus is described herein. The apparatus includes a first processor and a second processor. The first processor and the second processor communicate via an inter-processor communication link. The apparatus also includes a inactivity timer control module The inactivity timer control module is to manage a state of the communications link based on data traffic characteristics.