Netapp, Inc.

100.co, LLC

Sansa Ai Inc.

Sansa Al Inc.

Daash Intelligence, Inc.

Gary C King

Robert J Munro

Jason Brenier

Brendan D Callahan

Schuyler D Erle

Jessica D Long

Tripti Saxena

Tyler J Schnoebelen

Paul A Tepper

Stefan Krawczyk

Christopher Walker

Edgar Nunez

Veena Basavaraj

James B Robinson

Rob Voigt

Haley Most

Ross Mechanic

Sarah K Luger

Andrew Gilchrist-Scott

Abraham Benjamin De Waal

Nick Triantos

Michelle Casbon

Ujjwal Sarin

Aneesh Nair

Martha G Hinrichs

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

Methods and systems are disclosed for creating and linking a series of interfaces configured to display information and receive confirmation of classifications made by a natural language modeling engine to improve organization of a collection of documents into an hierarchical structure. In some embodiments, the interfaces may display to an annotator a plurality of labels of potential classifications for a document as identified by a natural language modeling engine, collect annotated responses from the annotator, aggregate the annotated responses across other annotators, analyze the accuracy of the natural language modeling engine based on the aggregated annotated responses, and predict accuracies of the natural language modeling engine's classifications of the documents.

Graphical systems and methods for human-in-the-loop machine intelligence

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

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

A policy framework is maintained on the computing device, and the computing device communicates with a policy server of an enterprise network over a network to receive a set of policy instructions. The policy instructions are executed through the policy framework in order to implement one or more policies that control the mobile computing device's access to resources of the enterprise network.

Implementing policies for an enterprise network using policy instructions that are executed through a local policy framework

System and methods for managing content modification messages are provided. In some embodiments, a request to send a modification message to a second user is received from a first user. The request to send the modification message pertains to a modification made to a first user-specific version corresponding to the first user and represented by a first delta. The modification message comprising a second delta is generated. The modification message includes second instructions to modify a second user-specific version to include the modification within the second user-specific version by resolving the first delta and the first user-specific version to the second user-specific version. A message indicator for the modification message is provides to a client device of the second user. The message indicator identifies the first user as the sender of the modification message.

Systems and methods for managing user-specific modifications to shared content on an individual basis

Growing community of inventors

Los Altos, CA, United States of America

Gary C King