Catachi Co.

100.co, LLC

Juntos, Inc.

Sansa Al Inc.

Compliance.ai

Christopher Walker

Robert J Munro

Tyler J Schnoebelen

Brendan D Callahan

Gary C King

Jason Brenier

Paul A Tepper

Edgar Nunez

Sarah K Luger

Ross Mechanic

Haley Most

Andrew Gilchrist-Scott

Brendan Callahan

Kayvan Alikhani

Schuyler D Erle

Jessica D Long

Tripti Saxena

Danielle Deibler

James B Robinson

Veena Basavaraj

Dante Emilio Cassanego

Michelle Casbon

Ujjwal Sarin

Aneesh Nair

Martha G Hinrichs

Danielle Lee Deibler

Disclosed herein is a method for facilitating the classification of documents. Accordingly, the method may include receiving, using a communication device, documents from at least one user device, analyzing, using a processing device, the documents based on a filter, classifying, using the processing device, the documents into a topic based on the analyzing of the documents, transmitting, using the communication device, the documents and a topic identifier associated with the topic to a user device, receiving, using the communication device, a judgment data from the user device, analyzing, using the processing device, the judgment data, modifying, using the processing device, the filter based on the analyzing of the judgment data, generating, using the processing device, a modified filter based on the modifying, and storing, using a storage device, the modified filter.

Methods and systems for facilitating classification of documents

Disclosed is a method of facilitating searching of regulatory content. The method may include receiving, using a communication device, a search request from a user device. Further, the search request may be associated with a user account. Further, the method may include querying, using a storage device, at least one database based on the search request. Further, the method may include receiving, using a processing device, at least one regulatory content from the storage device based on the querying. Further, the method may include retrieving, using the storage device, at least one user characteristic associated with the user account. Further, the method may include generating, using the processing device, at least one personalized regulatory content based on each of the at least one regulatory content and the at least one user characteristic. Further, the method may include transmitting the at least one personalized regulatory content to the user device.

Methods and systems for facilitating searching of regulatory content

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

An optimal sample size for experiments targeting units having specific static criteria is determined, based on one or more iterations of a performed experiment. The sample size to use for subsequent experiments targeting units having the specific static criteria is calculated based on target effect of the performed experiment. Multiple iterations can be performed to refine the sample size. Each iteration uses the previously calculated sample size as a parameter. The calculated sample size is then used to determine an optimal treatment. Separate experiments using separate treatments are performed on separate group sets of the calculated sample size. For each separate performed experiment, a score is calculated for the corresponding specific treatment, taking into account actual effect size, statistical significance and homogeneity of effect. Depending upon the score, the given treatment can be accepted for production usage or discarded, or the experiment can be replicated to validate effect.

Determining efficient experimental design and automated optimal experimental treatment delivery

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

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San Francisco, CA, United States of America

Christopher Walker