Abstract: Systems and methods are described for determining a similarity between a first goods and services description and a second goods and services description. The goods and services descriptions are provided to a machine learning model. The machine learning model returns a first set of goods and services classifications for the first goods and services description and a second set of goods and services classifications for the second goods and services description. A plurality of goods and services similarity scores are determined, each goods and services similarity score indicating a similarity between a first goods and services classification from the first set of goods and services classifications and a second goods and services classification from the second set of goods and services classifications. An aggregate goods and services similarity score is determined based on the plurality of goods and services similarity scores and returned to a user as a query result.

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: Various instrumentation, implants and methodology are disclosed for implanting bone implants in the TLIF approach. The implants are preferably cortical bone of various shapes. The instruments include chisels, rasps, trials, inserters, spreaders, adjustors, curettes, rongeurs, and impactors. The instruments have straight and bent shafts. The implants may have recesses or notches in their sides for receipt of a mating insertion instrument. Some of the implants have a threaded hole for receiving a mating threaded stud of an implant insertion instrument. The implants may have saw tooth vertebral gripping surfaces which are lordotic or parallel, may be C-shaped, multi-faceted or annular.