John Snow Labs Inc.

Veritone Alpha, Inc.

David Talby

Wolf Kohn

Michael Luis Sandoval

Jonathan Cross

Vishnu Vettrivel

Jason Knox

Mike Lazarus

Julio Bonis Sanz

Veysel Kocaman

Jose Alberto Pablo Andreotti

Saif Addin Ellafi

Jose Pablo Andreotti

Techniques are described for performing automated operations related to identifying and using repair and maintenance information, such as summarizing and encoding such information for a number of problems, identifying specific repair or maintenance instructions in response to natural language queries, and using the identified instructions in further automated manners in some situations (e.g., to automatically initiate repair or maintenance actions on a particular target computing device). Identifying of specific instructions in response to a particular natural language query may include initially identifying a group of candidate groups of content that satisfy a defined similarity threshold to an encoded version of the natural language query, providing and using a trained validation model to evaluate each candidate content group and validate if it includes a responsive answer to the natural language query, and then further analyzing one or more validated candidate content groups to determine the actual responsive answer.

Determining repair instructions in response to natural language queries

Techniques are described for performing automated operations related to identifying and using repair and maintenance status information, such as summarizing and encoding such information for one or more repair areas or other target domains, identifying specific repair or maintenance status information in response to natural language queries, and using the identified repair status information in further automated manners in some situations (e.g., to automatically initiate repair or maintenance actions on a particular target computing device). Identifying of specific repair status information in response to a particular natural language query may include initially identifying one or more candidate data groupings that match an encoded version of the natural language query (e.g., extracting encoded data groupings that match a generated version of the query), and optionally further analyzing one or more matching candidate data groupings as part of determining the actual response.

Determining repair status information using unstructured textual repair data in response to natural language queries

A text extraction computing method that comprises calculating an estimated character pixel height of text from a digital image. The method may scale the digital image using the estimated character pixel height and a preferred character pixel height. The method may binarizes the digital image. The method may remove distortions using a neural network trained by a cycle GAN on a set of source text images and a set of clean text images. The set of source text images and clean text images are unpaired. The source text images may be distorted images of text. Calculating the estimated character pixel height may include summarizing the rows of pixels into a horizontal projection, and determining a line-repetition period from the projection, and quantifying the portion of the line-repetition period that corresponds to the text as the estimated character pixel height. The method may extract characters from the digital image using OCR.

Preprocessing images for OCR using character pixel height estimation and cycle generative adversarial networks for better character recognition

A computer process for entity resolution of natural language records including training a semantic embedding function on a corpus of unlabeled training materials. The semantic embedding function can take a word and represent it as a vector, where the vector represents the word as it relates to the semantic information of the corpus of unlabeled training materials. The process may transform a list of normalized descriptions using the semantic embedding function into a list of vector representations of the descriptions. The process may transform words from a natural language record to a vector representation of the natural language record using the semantic embedding function, and may use a named entity recognizer. The process may find a best match description from the list of normalized descriptions using the list of vector representations of the descriptions and the vector representation of the natural language record, and may include using word mover distance.

Using unsupervised machine learning for automatic entity resolution of natural language records

Techniques are described for implementing automated control systems that manipulate operations of specified target systems, such as by modifying or otherwise manipulating inputs or other control elements of the target system that affect its operation (e.g., affect output of the target system). An automated control system may in some situations have a distributed architecture with multiple decision modules that each controls a portion of a target system and operate in a partially decoupled manner with respect to each other, such as by each decision module operating to synchronize its local solutions and proposed control actions with those of one or more other decision modules, in order to determine a consensus with those other decision modules. Such inter-module synchronizations may occur repeatedly to determine one or more control actions for each decision module at a particular time, as well as to be repeated over multiple times for ongoing control.

Determining control actions of decision modules

Techniques are described for implementing automated control systems that manipulate operations of specified target systems, such as by modifying or otherwise manipulating inputs or other control elements of the target system that affect its operation (e.g., affect output of the target system). An automated control system may in some situations have a distributed architecture with multiple decision modules that each controls a portion of a target system, and may further have one or more components that interacts with one or more users to obtain a description of the target system, including restrictions related to the various elements of the target system, and one or more goals to be achieved during control of the target system. The component(s) then perform various automated actions to generate, test and deploy one or more executable decision modules to use in performing the control of the target system based on the user-specified information.

Growing community of inventors

Mercer Island, WA, United States of America

David Talby