Sae International

Cognistic, LLC

Roshan Bhave

Audra Ziegenfuss

Uxue Zurutuza Dorronsoro

Divyesh Gaur

Anna Belova

Sanjay Chopra

Ihor Markevych

Ji Liu

Longxiang Zhang

Tom Bu

Eric Nyberg

One embodiment provides a method for generating a digital standard utilizing a trained machine-learning model, the method including: training at least one machine-learning model to generate digital standards from underlying standards utilizing a schema, wherein the training includes: receiving, for unstructured information within the underlying standards, a plurality of annotated underlying standards including a set of underlying standards having annotations identifying a classification of conceptual units within the set of underlying standards and corresponding to the schema; and teaching, for structured information within the underlying standards, the at least one machine-learning model patterns delineating information as belonging to conceptual units within the schema; and deploying the at least one trained machine-learning model to convert a second set of underlying standards to the digital standards, wherein the second set of underlying standards is different than the set of underlying standards. Other aspects are described and claimed.

Generation of digital standards using machine-learning model

One embodiment provides a method for clustering documents based upon a structure of each of the documents, including: receiving, at a device utilizing the machine-learning model, at least one document, each including a plurality of characters and having a structure; converting, for each of the at least one document, each of the plurality of characters to one of a plurality of character representations, wherein the converting includes identifying an attribute of a character and selecting a character representation corresponding to the attribute; producing at least one array for each of the one or more documents, wherein the at least one array includes the plurality of characters converted to the character representations; and clustering the at least one document into document clusters having similar structures by grouping the at least one arrays into groups of arrays having similarities, wherein each document cluster include documents corresponding to the arrays within one of the groups of arrays.

Document clustering based upon document structure

One embodiment provides a method for generating a digital standard utilizing a trained machine-learning model, the method including: receiving an underlying standard; extracting conceptual units from the underlying standard; classifying, using at least one trained machine-learning model, at least a portion of the extracted conceptual units into one of a plurality of classification groups; storing the classified extracted conceptual units into a data repository as defined by the schema; displaying, within a user interface on a display of an information handling device, a digital standard in a format based upon the schema; and providing, within the user interface, search and filter functions allowing for finding information related to the digital standard. Other aspects are described and claimed.

One embodiment provides a method for generating a digital standard utilizing a trained machine-learning model, the method including: receiving an underlying standard; extracting conceptual units from the underlying standard; classifying, using at least one trained machine-learning model, at least a portion of the extracted conceptual units into one of a plurality of classification groups, wherein each of the classification groups identifies a function of the extracted conceptual units, included within a given classification group, within the underlying standard; wherein the classifying comprises classifying conceptual units from the underlying standard based upon sections of a schema corresponding to a digital standard; and storing the classified extracted conceptual units into a data repository based upon the schema. Other aspects are described and claimed.

One embodiment provides a method for training a machine-learning model to detect a location of a person's appendix, comprising: receiving, at the machine-learning model, a plurality of images, each image being a slice of a body taken by a CT scan; identifying, for each of the plurality of images, features of the appendix, wherein the identifying comprises analyzing a plurality of slices of each of the plurality of images and classifying each of the plurality of slices, into one of a plurality of classification groups, based upon a feature of the appendix within the slice; segmenting each of the plurality of image slices included in the one of the plurality of classification groups that classifies the slice as containing the appendix, thereby identifying probable locations of the appendix, via utilizing a probability mask for each of the probable locations.

Growing community of inventors

Pittsburgh, PA, United States of America

Roshan Bhave