Google Inc.

Dustin Zelle

Rami Al-Rfou

Joseph Catalano

Bryan Perozzi

Bo Lin

Michael Shalai

A solution arranged to build or train a machine learning model (ML model) that can be uploaded to a server arranged to deploy the ML model to communicating devices. The ML model builder can build the ML model and a ML production pipeline. The ML production pipeline can train the ML model, convert the ML model to a web browser compatible format, and upload the converted ML model to the server. The ML model can receive as input a sequence of prior activities on one communicating device in the communicating devices, analyze the sequence of prior activities on the communicating device, predict a next activity on the communicating device based on the analysis of the sequence of prior activities, preemptively search a computer network based on the predicted next activity to find a computer asset, and preload the found computer asset to a storage in the communicating device.

Method and system for activity prediction, prefetching and preloading of computer assets by a client-device

The present disclosure provides computing systems and methods directed to algorithms and the underlying machine learning (ML) models for evaluating similarity between graphs using graph structures and/or attributes. The systems and methods disclosed may provide advantages or improvements for comparing graphs without additional context or input from a person (e.g., the methods are unsupervised). In particular, the systems and methods of the present disclosure can operate to generate respective embeddings for one or more target graphs, where the embedding for each target graph is indicative of a respective similarity of such target graph to each of a set of source graphs, and where a pair of embeddings for a pair of target graphs can be used to assess a similarity between the pair of target graphs.

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Brooklyn, NY, United States of America

Dustin Zelle