Workday, Inc.

Netflix Inc.

The Honest Company, Inc.

Mohammad Sabah

Parag Avinash Namjoshi

Vijay Bharadwaj

Shuangshuang Jiang

Sasi Parthasarathy

Siddharth Angrish

Mohammad Iman Sadreddin

Shafaq Abdullah

Carlos A Gomez-Uribe

Vladimir Giverts

Carlos Alberto Gomez Uribe

Daniel Walter Beck

Adeyemi Ajao

James Fan

Kevin Mun Joun Tham

A system for determining retention risk comprises a grouper, a filter, a normalizer, a feature vector extractor, a model builder, and a predictor. The grouper is for determining a set of time series of transactions where each is associated with one employee. The filter is for filtering the set of time series of transactions based on an employee transition characteristic to determine a subset of time series. The normalizer is for determining a model set of time series by normalizing the subset of time series. The feature vector extractor is for determining a set of feature vectors determined from a time series of the model set of time series. The model builder is for determining one or more models based at least in part on the set of feature vectors. The predictor is for predicting retention risk for a given employee using the one or more models.

Retention risk determiner

A system for identifying address components includes an interface and a processor. The interface is to receive an address for parsing. The processor is to determine a matching model of a set of models based at least in part on a matching probability for each model for a tokenized address, which is based on the address for parsing, and associate each component of the tokenized address with an identifier based at least in part on the matching model, wherein each component of the set of components is associated with an identifier, and wherein probabilities of each component of the set of components are determined using training addresses.

Address parsing system

Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.

Relationship-based search and recommendations

Techniques are disclosed herein for learning latent interests based on metadata of one or more images. An analysis tool associates one or more attributes with each of the objects based on a time and a location described in the metadata of that object. Each of the attributes describes one of a plurality of locations or an event scheduled to occur at one or more of the plurality of locations. The analysis tool identifies one or more concepts from a distribution of the one or more attributes to each of the objects. Each of the one or more concepts includes at least a first attribute in the distribution that co-occurs with a second attribute in the distribution.

Method for learning a latent interest taxonomy from multimedia metadata

Techniques disclosed herein describe inferring user interests based on metadata of a plurality of multimedia objects captured by a plurality of users. An analysis tool receives, for each of the users, metadata describing each multimedia object in the plurality of objects associated with that user. Each multimedia object includes one or more attributes imputed to that object based on the metadata. The analysis tool identifies one or more concepts from the one or more attributes. Each concept includes at least a first attribute that co-occurs with a second attribute imputed to a first multimedia object. The analysis tool associates a first one of the plurality of users with at least one of the concepts based on the attributes imputed to multimedia objects associated with the first one of the plurality of users.

Method for inferring latent user interests based on image metadata

A system for identifying address components includes a training address interface, a training address probability processor, a parsing address interface, and a processor. The training address interface is to receive training addresses. The training addresses are a set of components with corresponding identifiers. The training address probability processor is to determine probabilities of each component of the training addresses being associated with each identifier. The parsing address interface to receive an address for parsing. The processor is to determine a matching model of a set of models based at least in part on a matching probability for each model for a tokenized address, which is based on the address for parsing, and associate each component of the tokenized address with an identifier based at least in part on the matching model.

Growing community of inventors

San Jose, CA, United States of America

Mohammad Sabah