Orion Labs, Inc.

Sansa Ai Inc.

100.co, LLC

Sansa Al Inc.

Idibon, Inc.

100.co Technologies, Inc.

Daash Intelligence, Inc.

Schuyler D Erle

Robert J Munro

Brendan D Callahan

Jason Brenier

Jessica D Long

Gary C King

Greg Albrecht

Tyler J Schnoebelen

Dan Phung

Paul A Tepper

Tripti Saxena

Edgar Nunez

Stefan Krawczyk

Ellen Juhlin

Andy Isaacson

Nelson Carpentier

Veena Basavaraj

James B Robinson

Rob Voigt

Christopher Walker

Sarah K Luger

Andrew Gilchrist-Scott

Haley Most

Ross Mechanic

Michelle Casbon

Ujjwal Sarin

Aneesh Nair

Martha G Hinrichs

Secure data transfers between communication nodes is performed using a group encryption key supplied by a remote management system. A first node transmits a request for secure communications with a second node to the remote management system using a control channel. The remote management system generates and encrypts a group encryption key usable by the first and second nodes and forwards the encrypted group encryption key to the first and second nodes using one or more control channels. The first and second communication nodes decrypt the group encryption key and use it to encrypt data transmitted between the nodes using a data transport network. In some implementations the securely communicating nodes may use encryption keys and/or techniques that prevent the remote management system from eavesdropping on the nodes' communications.

Encrypted group communications

Systems, methods, and apparatuses are presented for a novel natural language tokenizer and tagger. In some embodiments, a method for tokenizing text for natural language processing comprises: generating from a pool of documents, a set of statistical models comprising one or more entries each indicating a likelihood of appearance of a character/letter sequence in the pool of documents; receiving a set of rules comprising rules that identify character/letter sequences as valid tokens; transforming one or more entries in the statistical models into new rules that are added to the set of rules when the entries indicate a high likelihood; receiving a document to be processed; dividing the document to be processed into tokens based on the set of statistical models and the set of rules, wherein the statistical models are applied where the rules fail to unambiguously tokenize the document; and outputting the divided tokens for natural language processing.

Intelligent system that dynamically improves its knowledge and code-base for natural language understanding

Systems and methods are presented for the automatic placement of rules applied to topics in a logical hierarchy when conducting natural language processing. In some embodiments, a method includes: accessing, at a child node in a logical hierarchy, at least one rule associated with the child node; identifying a percolation criterion associated with a parent node to the child node, said percolation criterion indicating that the at least one rule associated with the child node is to be associated also with the parent node; associating the at least one rule with the parent node such that the at least one rule defines a second factor for determining whether the document is to also be classified into the parent node; accessing the document for natural language processing; and determining whether the document is to be classified into the parent node or the child node based on the at least one rule.

Methods and systems for modeling complex taxonomies with natural language understanding

Methods, apparatuses and computer readable medium are presented for generating a natural language model. A method for generating a natural language model comprises: selecting from a pool of documents, a first set of documents to be annotated; receiving annotations of the first set of documents elicited by first human readable prompts; training a natural language model using the annotated first set of documents; determining documents in the pool having uncertain natural language processing results according to the trained natural language model and/or the received annotations; selecting from the pool of documents, a second set of documents to be annotated comprising documents having uncertain natural language processing results; receiving annotations of the second set of documents elicited by second human readable prompts; and retraining a natural language model using the annotated second set of documents.

Optimization techniques for artificial intelligence

Systems, methods, software and apparatus enable linking of a wearable end user communication device (EUD) to an intermediate communication device (ICD) utilizing optical symbol sequence matching. Optical symbol reference data corresponding to an optical symbol sequence displayed on the EUD is obtained from the EUD by the ICD. Optical symbol input data is also acquired by the ICD (e.g., via user inputs, EUD device proximity data, image acquisition). The devices are linked if the optical symbol reference data and optical symbol input data match. The optical symbol reference data can be displayed, allowing user confirmation of a match with the optical symbol sequence displayed on the EUD. An ICD user interface touchscreen can present users with selectable color inputs to replicate the optical symbol sequence displayed on the EUD, for example using an LED array. Communications between the devices before and after linking can utilize Bluetooth low energy.

Proximity-based linking for wearable group communication device

Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Methods for generating natural language processing systems

Systems, methods, and apparatuses are presented for a trained language model to be stored in an efficient manner such that the trained language model may be utilized in virtually any computing device to conduct natural language processing. Unlike other natural language processing engines that may be computationally intensive to the point of being capable of running only on high performance machines, the organization of the natural language models according to the present disclosures allows for natural language processing to be performed even on smaller devices, such as mobile devices.

Methods and systems for providing universal portability in machine learning

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San Francisco, CA, United States of America

Schuyler D Erle