Abstract: Systems and methods to classify incident report documents are disclosed, comprising inputting, a first type data entry of a document into a deep neural network (DNN); encoding, via the DNN, the first type data entry to output a densely embedded contextual vector representing contents of the first type data entry; generating, a list containing ordered data from a second type data entry of the document; encoding, via a machine learning network, the ordered data into a sparse vector representation of the second type data entry; concatenating, the densely embedded contextual vector with the sparse vector representation to generate a representative vector of the document; and training a gradient-boosted classifier network by using as training inputs the representative vector and a label associated with the document to generate a classification of the document.

Abstract: A system for identifying industrial ergonomics risk root-causes and providing risk control actions, comprising: a computing device configured to obtain video signals of a worker performing a job at a workplace; and a computing server system configured to receive and process the video signals to determine joint locations of the worker, calculate joint angles for each body region of the worker based on the joint locations, calculate, based on the joint angles, a risk score for each body region of the worker in a plurality of risk categories, calculate a risk rating for each body region of the worker based on the risk score, determine ergonomic risk root-causes for each body region of the worker based on the risk rating, and provide ergonomic risk control recommendations to mitigate the ergonomic risk root-causes.

Abstract: Systems and methods for automated indexing and extraction of information in digital documents are disclosed. A method may comprise identifying a page containing targeted information; inputting an image of the page into a visual machine learning network (visual ML), wherein the visual ML is trained to recognize text associated with the targeted information in an image; identifying by the visual ML, a section of the image that contains the targeted information; inputting the digital document, and coordinates of the section into an extraction module; and extracting the targeted information by the extraction module from the section.

Abstract: Systems and Methods are disclosed herein for automatically indexing multiple informational fields in digital data records, the method comprising: identifying, based on rules defining target information fields, for each target field of the target information fields, at least one page in a digital data record comprising content related to the target field; extracting, for each target field, from the identified at least one page, at least one portion of text comprising the content; feeding, for each target field, a pre-processed version of the at least one portion of text into a machine learning (ML) model, wherein the ML model is trained on the target field; determining, for each target field, via the ML model trained on the target field, at least one candidate text comprising the content; and extracting, for each target field, the at least one candidate text.

Abstract: A system for identifying industrial ergonomics risk root-causes and providing risk control actions, comprising: a computing device configured to obtain textual information describing a series of tasks of a job and forces being exerted during the series of tasks; and a computing server system configured to receive and process the textual information to generate a set of textual entry to correspond to a unique identifier of the job, identify nouns and verbs in the set of textual entry via natural language processing techniques, perform dependency parsing and part-of-speech tagging to associate each identified verb in the set of textual entry with a root noun in order to identify action-object pairs and unpaired actions, determine ergonomic risk root-causes based at least upon the action-object pairs and the unpaired actions in the set of textual entry, and provide ergonomic risk control recommendations to mitigate the ergonomic risk root-causes.

Abstract: A system for identifying industrial ergonomics risk root-causes and providing risk control actions, comprising: a computing device configured to obtain textual information describing a series of tasks of a job and forces being exerted during the series of tasks; and a computing server system configured to receive and process the textual information to generate a set of textual entry to correspond to a unique identifier of the job, identify nouns and verbs in the set of textual entry via natural language processing techniques, perform dependency parsing and part-of-speech tagging to associate each identified verb in the set of textual entry with a root noun in order to identify action-object pairs and unpaired actions, determine ergonomic risk root-causes based at least upon the action-object pairs and the unpaired actions in the set of textual entry, and provide ergonomic risk control recommendations to mitigate the ergonomic risk root-causes.

Abstract: Systems and methods for automated indexing and extraction of information in digital documents are disclosed. A method may comprise selecting a page number of a digital document to identify a page containing targeted information; inputting an image of the page into a visual machine learning network (visual ML), wherein the visual ML is trained to recognize text associated with the targeted information in an image; identifying by the visual ML, a section of the image that contains the targeted information; inputting the page number, the digital document, and coordinates of the section into an extraction module; and extracting the targeted information by the extraction module from the section.