Camelot UK Bidco Limited

Peter Keyngnaert

Jan Waerniers

Ann Smet

Jan Schietse

Christoph Liekens

Akanksha Mishra

Systems and methods for generating strings based on a seed string are disclosed. Machine learning models are trained using domain-specific training data. Random walk models are derived from the trained machine learning models. A seed string is input into each of the random walk models, and each of the random walk models iteratively generate one or more next characters for the seed string to generate at least one term from each of the random walk models. A predicted class for the at least one term generated by each of the random walk models can be determined, and a ranked order for the at least one term generated by each of the random walk models with the predicted classes can be output to a graphical user interface.

Systems and methods for training and employing machine learning models for unique string generation and prediction

Exemplary embodiments of the present disclosure provide for identifying similar trademarks from one or more repositories and providing an interactive graphical user interface in which selectable graphical segments corresponding to the trademarks in a graphical user interface are grouped based on similarity measures associated with one or more characters of the trademarks and arranged radially and circumferentially in the graphical user interface about a central circle representing the trademark input string of an electronic search order. Retrieval of the trademarks in the set can include a subset of possible trademarks available for retrieval that would satisfy one or more of the similarity measures and a color of each of the selectable graphical segments depends on whether the trademarks retrieved corresponding to the selectable graphical segments represent a complete set of the possible trademarks available, a subset of the possible trademarks available, or a null set of the possible trademarks available. Operations like adding and removing sets of trademarks defined by interacting with the graphical representation is possible.

Systems and methods for similarity and context measures for trademark and service mark analysis and repository searches

Exemplary embodiments of the present disclosure provide for extraction data from one or more repositories in response to an electronic search order including a text-based input string, a list of jurisdictions, and a list of international classes. Conditional logic of one or more rules is executed to process the search order, and the search order is transformed into one or more queries based on execution of the conditional logic of the one or more rules. A set of data is retrieved from the one or more repositories using the one or more queries to generate search results, where the set of trademarks retrieved can be limited to the list of jurisdictions and the list of international classes identified in the search order. At least a portion of the search results are transmitted to a user computing device to be rendered in a graphical user interface of a display device associated with the user computing device.

System and methods for automating trademark and service mark searches

Exemplary embodiments of the present disclosure provide for identifying similar trademarks from one or more repositories based on training a goods and/or services similarity engine to identify similarities between pairs of descriptions of goods and/or services in a corpus of training data that includes the descriptions of goods and/or services for registered trademarks and trademark classes associated with the descriptions of goods and/or services. A goods and/or services similarity value indicative of similarities between a reference description of goods and/or services and descriptions of goods and/or services associated with registered trademarks can be generated by a goods and/or services similarity engine and a presentation of at least a subset of the set of trademarks can be generated that includes graphics emphasizing the registered trademarks in the subset based, at least in part, on the plurality of goods and/or services similarity values.

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Landegem, Belgium

Peter Keyngnaert