Physera, Inc.

Facebook, Inc.

Lawrence Livermore National Security, LLC

Daniel Matthew Merl

Yigal Dan Rubinstein

Cameron Marlow

Jonathan D Chang

Shane Patrick Ahern

Todd Riley Norwood

Fei Huang

Amaç Herdagdelen

Aditya Pal

Stanislav Funiak

Smriti Bhagat

Brian James Gallagher

Seyoung Park

Michael Lee Develin

Brian Michael Kelley

Viet An Nguyen

Carlos Gregorio Diuk Wasser

Indrasis Chakraborty

In an industrial control system (ICS), latent vectors are generated to represent identity or behaviors of host devices coupled to the ICS. A computing system captures communications transmitted by a host device across a network associated with the ICS. A set of values are extracted from one or more respective fields in the communication, then applied to a trained neural network. Values of a first set of fields are applied at an input layer of the trained neural network, while values of a second set of fields are applied at an output layer of the neural network. Based on the application of the neural network to the values extracted from the communication, the computing system generates a latent vector.

Industrial control system device classification

An exercise feedback system receives exercise data such as images or video captured by client devices of users performing exercises. The exercise feedback system may access a machine learning model trained using image of a population of users. The images used for training may be labeled, for example, as having proper or improper musculoskeletal form. The exercise feedback system may determine a metrics describing the musculoskeletal form of a user by applying the trained machine learning model to images of the user as input features. The exercise feedback system may generate feedback for a certain exercise using the metrics based on output predictions of the model. The feedback can be provided to a client device of the user or a physical therapist for presentation.

Classification of musculoskeletal form using machine learning model

An exercise feedback system receives exercise data captured by client devices of users performing musculoskeletal exercises. The exercise feedback system may provide captured images to a client device of a physical trainer (PT) who remotely provides feedback on the users' exercise performances, for example, by labeling images as indicative of proper or improper musculoskeletal form. A PT may track multiple users using a central feed, which includes content displayed in an order based on ranking of users by a model. Additionally, the exercise feedback system may provide an augmented reality (AR) environment. For instance, an AR graphic indicating a target musculoskeletal form for an exercise is overlaid on a video feed displayed by a client device. Responsive to detecting that a user's form is aligned to the AR graphic, the exercise feedback system may notify the user and trigger the start of the exercise.

Augmented reality guided musculoskeletal exercises

Systems, methods, and non-transitory computer readable media are configured to apply a machine learning model to predict an age division for a user based on user information. An age bracket within the age division including a largest number of connections of the user can be determined. The determined age bracket can be assigned for the user.

Systems and methods for machine learning based age bracket determinations

Customizing content for musculoskeletal exercise feedback

Exercise feedback system for musculoskeletal exercises

A system for identifying language(s) for content items is disclosed. The system can identify different languages for content item words segments by identifying segment languages that maximize a probability across the segments. The probability can be a combination of: an author's likelihood for the language identified for the first word; a combination of transition frequencies for selected languages identified for words, the transition frequencies indicating likelihoods that a transition occurred to the selected language from the previous word's language; and a combination of observation probabilities indicating, for a given word in the content item, a likelihood the given word is in the identified language. For an in-vocabulary word, the observation probabilities can be based on learned probability for that word. For an out-of-vocabulary word, the probability can be computed by breaking the word into overlapping n-grams and computing combined learned probabilities that each n-gram is in the given language.

Identifying multiple languages in a content item

Growing community of inventors

Livermore, CA, United States of America

Daniel Matthew Merl