Avodah, Inc.

Avodah Labs, Inc.

Intellisafe LLC

Trevor Chandler

Michael Menefee

Dallas Nash

Michael J Brown

Joshua K Goetz

Joshua Eng-Morris

Michael Brown

Disclosed are methods, devices and systems for translating a document using neural networks and leveraging the hierarchical structure of the document. Embodiments of the disclosed technology use bi-level structures that incorporate both the unique meanings of words and unique grammatical rules on matching grammatical word types in a document to train a neural network. The trained neural network is used to translate documents in an automated and efficient manner. The disclosed embodiments advantageously use manual (or human) analysis on a small portion of text to identify the unique means and grammatical rules, which is then leveraged to translate a much larger corpus of text with increased reliability and accuracy.

Translating documents using neural networks and hierarchical structuring of documents

Methods, apparatus and systems for recognizing sign language movements using multiple input and output modalities. One example method includes capturing a movement associated with the sign language using a set of visual sensing devices, the set of visual sensing devices comprising multiple apertures oriented with respect to the subject to receive optical signals corresponding to the movement from multiple angles, generating digital information corresponding to the movement based on the optical signals from the multiple angles, collecting depth information corresponding to the movement in one or more planes perpendicular to an image plane captured by the set of visual sensing devices, producing a reduced set of digital information by removing at least some of the digital information based on the depth information, generating a composite digital representation by aligning at least a portion of the reduced set of digital information, and recognizing the movement based on the composite digital representation.

Automated sign language translation and communication using multiple input and output modalities

Disclosed are methods, apparatus and systems for gesture recognition based on neural network processing. One exemplary method for identifying a gesture communicated by a subject includes receiving a plurality of images associated with the gesture, providing the plurality of images to a first 3-dimensional convolutional neural network (3D CNN) and a second 3D CNN, where the first 3D CNN is operable to produce motion information, where the second 3D CNN is operable to produce pose and color information, and where the first 3D CNN is operable to implement an optical flow algorithm to detect the gesture, fusing the motion information and the pose and color information to produce an identification of the gesture, and determining whether the identification corresponds to a singular gesture across the plurality of images using a recurrent neural network that comprises one or more long short-term memory units.

Automated gesture identification using neural networks

Disclosed are methods, apparatus and systems for real-time gesture recognition. One exemplary method for the real-time identification of a gesture communicated by a subject includes receiving, by a first thread of the one or more multi-threaded processors, a first set of image frames associated with the gesture, the first set of image frames captured during a first time interval, performing, by the first thread, pose estimation on each frame of the first set of image frames including eliminating background information from each frame to obtain one or more areas of interest, storing information representative of the one or more areas of interest in a shared memory accessible to the one or more multi-threaded processors, and performing, by a second thread of the one or more multi-threaded processors, a gesture recognition operation on a second set of image frames associated with the gesture.

Real-time gesture recognition method and apparatus

Methods, devices and systems for training a pattern recognition system are described. In one example, a method for training a sign language translation system includes generating a three-dimensional (3D) scene that includes a 3D model simulating a gesture that represents a letter, a word, or a phrase in a sign language. The method includes obtaining a value indicative of a total number of training images to be generated, using the value indicative of the total number of training images to determine a plurality of variations of the 3D scene for generating of the training images, applying each of plurality of variations to the 3D scene to produce a plurality of modified 3D scenes, and capturing an image of each of the plurality of modified 3D scenes to form the training images for a neural network of the sign language translation system.

Visual sign language translation training device and method

Integrated dual display sensor

Mask

The present disclosure relates to a protective mask to deactivate damaging agents. In some examples, the mask includes a filter element that traps particles of the damaging agent. A sanitizing emitter permeates the filter element with a sanitizing agent to deactivate the damaging agent. In some examples, the sanitizing agent is ultraviolet light configured to deactivate a virus such as SARS-CoV-2.

Protective mask

Methods, apparatus and systems for a sign language recognition are disclosed. One example of a sign language recognition device includes a primary display facing a first direction and a secondary display facing a second direction. One or more cameras are positioned adjacent the secondary display and face the second direction, wherein an image captured by the one or more cameras is displayed on at least a portion of the primary display.

Growing community of inventors

Thornton, CO, United States of America

Trevor Chandler