Xerox Corporation

Novity, Inc.

Evgeniy Bart

Matthew Adam Shreve

Margaret Helen Szymanski

Kai F Goebel

Yunda Wang

A method utilizes a target substrate has an array of chips on a carrier with a plurality of vacancies and a plurality of donor coupons are incompletely filled with functional chips. A bounding box is defined that encompasses the vacancies on the target substrate. Outcomes are simulated by overlapping a representation of the bounding box over a representation of each of a plurality of donor coupons at a plurality of translational offsets on a substrate plane to determine matches. An optimal one of the outcomes is found at a selected one or more of the donor coupons corresponding one or more offsets. A parallel transfer of the matching functional chips fills the vacancies on the target substrate using the one or more selected donor coupons and corresponding one or more offsets.

System and method for repairing vacancies resulting from mass transfer of devices

One embodiment provides a system for processing natural-language entries. The system obtains a plurality of historical natural-language entries associated with a first domain and pre-processes the historical natural-language entries to obtain a set of generic terms and a set of domain-specific terms. The system trains a machine learning model in the first domain using the plurality of historical natural-language entries associated with the first domain. The training comprises learning weight values of one or more generic terms, a weight value of a respective generic term indicating likelihood that the generic term is related to a trigger event. The system generalizes the machine learning model trained in the first domain, thereby allowing the model to be applied to a second domain. The system processes a natural-language entry associated with the second domain using the generalized machine learning model to determine whether the natural-language entry associated with the second domain corresponds to the trigger event.

Information extraction from maintenance logs

Digitized media is received that records a conversation between individuals. Cues are extracted from the digitized media that indicate properties of the conversation. The cues are entered as training data into a machine learning module to create a trained machine learning model. The trained machine learning model is used in a processor to detect other misalignments in subsequent digitized conversations.

Growing community of inventors

Santa Clara, CA, United States of America

Evgeniy Bart