Google Inc.

Per R Anderson

Daniel John Zigmond

Diane Lambert

April Pufahl

Kaleigh S Smith

Jared L Strawderman

Andrew Watson

Madelaine Plauché

Arun Mathew

Chrysoberyl Francisco

Oren Blasberg

Ian Langmore

Systems, methods, and apparatus for dynamically partitioning items in a list into one or more subsets in a manner tailored to a current modality of a client device is disclosed herein. Multimodal client devices can engage in a variety of interactions across the multimodal spectrum including voice only interactions, voice forward interactions, multimodal interactions, visual forward interactions, visual only interactions, etc. A list can be partitioned into subsets in part based on a currently modality of a multimodal device, one or more list attributes indicating additional information about the list, etc.

Dynamic list composition based on modality of multimodal client device

Systems and methods of leveraging machine learning to predict user generated content are provided. For instance, first entity data associated with an entity can be received. The first entity data can include user specified data associated with an attribute of the entity. The first entity data can be input into a machine-learned content prediction model. Inferred entity data can be received as output of the machine-learned content prediction model. The inferred entity data can include inferred data descriptive of the attribute of the entity.

Leveraging machine learning to predict user generated content

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for estimating demographic compositions of television audiences from audience similarities. In an aspect, pairwise cosine similarities of segment of viewers of rated and unrated television programs are determined, demographic information of rated programs is attributed to the unrated programs from the cosine similarity measures and demographic information for the rated programs.

Estimating demographic compositions of television audiences from audience similarities

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Corona Del Mar, CA, United States of America

Per R Anderson