Palo Alto Research Center Inc.

Xerox Corporation

Hoda M A Eldardiry

Robert Roy Price

Matthew Adam Shreve

Sricharan Kallur Palli Kumar

Gaurang R Gavai

Jin Sun

Rui Abreu

Francisco Eduardo Torres

Ryan A Rossi

Ashish V Pattekar

Daniel Gureasko Bobrow

Juan Liu

Bhaskar Saha

Shane Patrick Ahern

Jonathan Rubin

Linxia Liao

Evgeniy Bart

David Garcia

Tomonori Honda

Honglu Du

Embodiments provide a system and method for performing collaborative learning of machine representations of a concept. During operation, the system can receive a user-specified object associated with a user's concept of interest. The system can compute a similarity score between a target feature vector associated with the user-specified object and a respective feature vector for a set of candidate objects. The system can determine, based on the similarity score, a first subset of candidate objects that satisfy a similarity threshold. The system can receive, via a GUI, a first user-feedback associated with a visual representation of the first subset of candidate objects. The first user-feedback can represent an elaboration of a current user's concept of interest. The system can then modify, based on the first user-feedback, the target feature vector and the similarity function, thereby providing an improved model for machine representations of a current user's concept of interest.

System and method for performing collaborative learning of machine representations for a target concept

One embodiment provides a system that facilitates efficient collection of training data. During operation, the system obtains, by a recording device, a first image of a physical object in a scene which is associated with a three-dimensional (3D) world coordinate frame. The system marks, on the first image, a plurality of vertices associated with the physical object, wherein a vertex has 3D coordinates based on the 3D world coordinate frame. The system obtains a plurality of second images of the physical object in the scene while changing one or more characteristics of the scene. The system projects the marked vertices on to a respective second image to indicate a two-dimensional (2D) bounding area associated with the physical object.

System and method using augmented reality for efficient collection of training data for machine learning

A system is provided for facilitating multi-label classification. During operation, the system maintains a set of training vectors. A respective vector represents an object and is associated with one or more labels that belong to a label set. After receiving an input vector, the system determines a similarity value between the input vector and one or more training vectors. The system further determines one or more labels associated with the input vector based on the similarity values between the input vector and the training vectors and their corresponding associated labels.

Method and system for similarity-based multi-label learning

A method and system for generating a personalized health management recommendation for a user. During operation, the system obtains first physiological data generated by a wearable device worn by the user that indicates a physiological condition of the user. The system then generates a prediction model for the user based on the first physiological data. Next, the system obtains real-time physiological data generated by the wearable device. The system may generate a prediction by analyzing the real-time physiological data to determine whether the user's physiological condition exceeds a threshold parameter according to the prediction model. Upon determining that the threshold parameter has been exceeded, the system may select a recommendation and send the recommendation message to the user's mobile device.

Interactive remote patient monitoring and condition management intervention system

A method and system for detecting fault in a machine. During operation, the system obtains control signals and corresponding sensor data that indicates a condition of the machine. The system determines consistent time intervals for each of the control signals. During a consistent time interval the standard deviation of a respective control signal is less than a respective predetermined threshold. The system aggregates the consistent time intervals to determine aggregate consistent intervals. The system then maps the aggregate consistent intervals to the sensor data to determine time interval segments for the sensor data. The system may generate features based on the sensor data. Each respective feature is generated from a time interval segment of the sensor data. The system trains a classifier using the features, and applies the classifier to additional sensor data indicating a condition of the machine over a period of time to detect a machine fault.

Device health estimation by combining contextual information with sensor data

One embodiment of the present invention provides a system for multi-domain clustering. During operation, the system collects domain data for at least two domains associated with users, wherein a domain is a source of data describing observable activities of a user. Next, the system estimates a probability distribution for a domain associated with the user. The system also estimates a probability distribution for a second domain associated with the user. Then, the system analyzes the domain data with a multi-domain probability model that includes variables for two or more domains to determine a probability distribution of each domain associated with the probability model and to assign users to clusters associated with user roles.

Detecting anomalies in work practice data by combining multiple domains of information

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San Carlos, CA, United States of America

Hoda M A Eldardiry