Celtrix Pharmaceuticals, Inc.

Google Inc.

Verizon Patent and Licensing Inc.

Verizon Media Inc.

Yang Zhang

Desmond D Mascarenhas

David R Olsen

Pamela S Olson

Guy Dassa

Yang Jiao

Jing Wang

Atte Lahtiranta

Pedro A Carrillo

Ali Nasiri Amini

Dinah Shender

Ruoyi Jiang

Pedro A Cohen

Zhuo Zhang

Sen Li

Xinlong Bao

Amy Richardson

Mert Dikmen

Junji Takagi

Kimyen B Nguyen

Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.

Contribution incrementality machine learning models

Disclosed are systems and methods for improving interactions with and between computers in computerized security and content monitoring, hosting and providing devices, systems and/or platforms. The disclosed systems and methods provide a novel framework that adaptively distinguishes between known people versus unknown people based on a dynamically applied, anonymous facial recognition methodology. The disclosed framework provides such functionality by recognizing faces within captured images without storing any information or annotations regarding or revealing the captured person's identity. The framework is configured to adaptively learn to distinguish between faces seen for the first time and faces it has previously seen by locally processing a captured image and only sending face embeddings to a network location for future comparisons of subsequently, anonymously captured images.

Computerized system and method for adaptive stranger detection

The invention is directed to the use of a fusion partner that does not contain a leader sequence, as a means to increase the solubility and activity of recombinant polypeptides by facilitating the expression of fusion proteins, which are then transported out of the cytoplasm. The invention includes a nucleic acid encoding a fusion polypeptide comprising a mature interleukin-1-like polypeptide or a leader-deleted-translocating polypeptide, and a polypeptide of interest; as well as host cells comprising such nucleic acids, and fusion proteins so encoded. The invention also encompasses methods of using such nucleic acids to produce recombinant fusion polypeptides, mature polypeptides of interest, and purified compositions thereof.

Expression of fusion polypeptides transported out of the cytoplasm

The invention is directed to the use of IL-1-like fusion polypeptides to increase the solubility and activity of recombinant polypeptides. The invention includes a nucleic acid encoding a fusion polypeptide comprising an interleukin-1-like polypeptide and a polypeptide of interest and the method of using such a nucleic acid to produce recombinant fusion polypeptides.

Polypeptide fusions to polypeptides of the beta-trefoil fold structural

Fusion polypeptides and proteins

Growing community of inventors

Sunnyvale, CA, United States of America

Yang Zhang