Spotify Ab

Flatiron Health, Inc.

Topos Inc.

Will Shapiro

Ian Anderson

Clay Gibson

Oguz Semerci

Margreth Mpossi

Scott Wolf

Santiago Gil

Nisha Singh

Brett Wittmershaus

Guy Amster

Sharang Phadke

Benjamin Lacker

Michael Waskom

Natalie Roher

John Ritten

Kimberly Wiederkehr

Katherine Harrison

Andrea Ratzlaff

Ashley Allen

Sarina Dass

Mahir Yavuz

Marc Knight

Jeremy Canfield

Stacie Sienicki

Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

Machine learning extraction of clinical variable values for subjects from clinical record data

An electronic device associated with a media-providing service has one or more processors and memory storing instructions for execution by the one or more processors. For each track of a plurality of tracks consumed by a user of the media-providing service, the electronic device receives, over a time window, an indication of user selection of the track to compile a listening history of the user and determines, based on the listening history of the user with the media-providing service, whether the track has previously been consumed by the user. The electronic device determines, for the user, a discovery score corresponding to an average preference of the user for selecting previously unconsumed tracks. In response to determining that an amount of use of the media-providing service by the user does not satisfy a use threshold, the electronic device forgoes recommending tracks to the user based on the discovery score.

Methods and systems for personalizing user experience based on use of service

An electronic device associated with a media-providing service assigns one or more characteristics of media items to at least one respective personality trait of a plurality of personality traits. The media items are provided by the media-providing service. The electronic device assigns one or more user behaviors to a first personality trait and tracks behavior of a user. The electronic device determines that a tracked behavior of the user corresponds to a first user behavior of the one or more user behaviors and assigns the first personality trait to the user based at least in part on determining that the tracked behavior of the user corresponds to the first user behavior. The electronic device provides personalized content to the user in accordance with a determination that the degree to which the tracked behavior of the user corresponds to the first user behavior satisfies a threshold.

Methods and systems for personalizing user experience based on personality traits

According to some aspects, a system is provided comprising at least one computer hardware processor and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the computer hardware processor, cause the computer hardware processor to perform obtaining an image of a map relating to a geographical area, identifying, using at least one automated image processing technique, a set of angles associated with a set of edges in the image, determining, based on the set of angles, at least one of a measure of angular complexity, a measure of angular entropy, and a measure of griddedness for the image, and indicating an urban form of the geographical area based on at least one of the determined measure of angular complexity, the determined measure of angular entropy, and the determined measure of griddedness of the image.

Methods and systems for algorithmically analyzing urban form

An electronic device accesses a listening history of a user of a media-providing service, and for each characteristic of a set of characteristics describing tracks provided by the media-providing service, determines a measure of divergence with respect to listening contexts for the listening history of the user. The electronic device identifies a characteristic that has a respective measure of divergence that satisfies a threshold indicative of a lack of uniformity in the listening history with respect to the listening contexts. The electronic device determines a representative value of the identified characteristic for a listening context corresponding to a current listening context of the user. The electronic device provides personalized content to a client device of the user based on the current listening context and representative value of the identified characteristic.

Methods and systems for personalizing content in accordance with divergences in a user's listening history

A server system divides respective groups of a plurality of groups of sessions for multiple users of the media-providing service into respective pluralities of clusters. The server system tracks user interaction with a client device during a user session and identifies the user session as belonging to a first cluster of the pluralities of clusters based at least in part on the user interaction. The server system personalizes content for the user session using one or more content criteria associated with the first cluster, in accordance with identifying that the user session belongs to the first cluster.

Methods and systems for session clustering based on user experience, behavior, and interactions

An electronic device is associated with a media-providing service. The electronic has one or more processors and memory storing instructions for execution by the one or more processors. For each track of a plurality of tracks consumed by a user of the media-providing service, the electronic device determines, based on a listening history of the user with the media-providing service, whether the track has previously been consumed by the user. The electronic device determines, for the user, a discovery score based on a number of the plurality of tracks determined to not have been previously consumed by the user. The electronic device provides personalized content to the user based on the discovery score.

Methods and systems for personalizing user experience based on discovery metrics

An electronic device associated with a media-providing service has one or more processors and memory storing instructions for execution by the one or more processors. The electronic device accesses a listening history of a user of the media-providing service. Based at least in part on the listening history of the user, the electronic device assigns a first personality trait of a plurality of personality traits to the user. The electronic device provides personalized content to the user based on the first personality trait.

A server system of a media-providing service has one or more processors and memory that stores instructions for execution by the one or more processors. The server system accesses a listening history of a user that specifies tracks consumed by the user. The tracks have respective characteristics that describe the tracks. The server system determines for the user, using the listening history, a diversity score based on variation of the characteristics across the tracks consumed by the user. The server system provides personalized content to the user based on the diversity score.

Methods and systems for personalizing user experience based on diversity metrics

A server system sorts a plurality of sessions for multiple users of a media-providing service into a plurality of groups by applying one or more sorting rules to one or more session characteristics for the plurality of sessions. The server system divides respective groups of the plurality of groups into respective pluralities of clusters using a machine-learning algorithm. The server system tracks user interaction with a client device during a user session and identifies the user session as belonging to a first cluster of the pluralities of clusters based at least in part on the user interaction. In accordance with identifying that the user session belongs to the first cluster, the server system personalizes content for the user session using one or more content criteria associated with the first cluster.

Growing community of inventors

New York, NY, United States of America

Will Shapiro