Abstract: Content distribution servers for routing source content for translation services to translators are disclosed. An exemplary content distribution server includes a processor and memory, where the processor is configured to execute instructions in the memory to: receive source content from one or more clients; extract source content features from the source content, the source content features including a summary of the source content and one or more keywords in the source content; generate extracted source content feature vectors representing the extracted source content features; receive translator profiles and a job profile; extract translators features from the translator profiles; and extract job features from the job profile.

Abstract: Automatic post-editing of machine translated content is disclosed herein. An example method includes presenting machine translated segments of a document and their associated quality estimation scores, invoking an automated post-editing system for segments with unsatisfactory translation quality, inputting the segments into a generative AI model alongside contextual information, producing a revised translation of the segment using the generative AI model and iterating the generative AI process with varying prompts until a quality estimation score of the machine translated segment meets or exceeds a quality estimation score threshold, or a predetermined number of attempts are reached.

Abstract: Automatic post-editing of machine translated content is disclosed herein. An example method includes presenting machine translated segments of a document and their associated quality estimation scores, invoking an automated post-editing system for segments with unsatisfactory translation quality, inputting the segments into a generative AI model alongside contextual information, producing a revised translation of the segment using the generative AI model and iterating the generative AI process with varying prompts until a quality estimation score of the machine translated segment meets or exceeds a quality estimation score threshold, or a predetermined number of attempts are reached.

Abstract: A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

Abstract: Systems and methods of machine learning for digital assets and message creation are provided herein. The present disclosure includes a method comprising means for receiving one or more assets that include textual content, means for performing machine learning on the one or more assets in order to determine relevant words, phrases, and statistics included in the textual content, and means for displaying segments of data on a graphical user interface that also includes an interface that is used to create a message using content of the segments of the textual content that have been extracted from the one or more assets.

Abstract: Automatic post-editing of machine translated content is disclosed herein. An example method includes presenting a user with machine translated segments of a document and their associated quality estimation scores, allowing a user to invoke an automated post-editing system for segments with unsatisfactory translation quality, inputting the segments into a generative AI model alongside contextual information, producing a revised translation of the segment using the generative AI model and iterating the generative AI process with varying prompts until a satisfactory translation is achieved or a predetermined number of attempts are reached.

Abstract: Systems and methods of machine learning for digital assets and message creation are provided herein. The present disclosure includes a method comprising means for receiving one or more assets that include textual content, means for performing machine learning on the one or more assets in order to determine relevant words, phrases, and statistics included in the textual content, and means for displaying segments of data on a graphical user interface that also includes an interface that is used to create a message using content of the segments of the textual content that have been extracted from the one or more assets.

Abstract: A method and system provides the ability to connect steel elements. A design intent model is acquired and consists of a building information model of a skeleton of a building without connections between steel elements that have geometric characteristics. A structural analysis of the structure of the model is performed and generates output consisting of member end forces at intersections of the steel elements. Via a user interface, rules are defined, that associate a potential connection with a structural steel profile. Via a user interface, a script is defined, that dictate where connections should be placed by: identifying second geometric characteristics of the potential connection, identifying forces for steel elements to be connected, comparing the design intent model, the structural analysis output and the rules to the structural steel profile, and autonomously generating and placing, based on the comparison, connections on the BIM model at applicable intersections.

Abstract: A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

Abstract: Systems and methods of machine learning for digital assets and message creation are provided herein. The present disclosure includes mechanisms for receiving one or more assets that include textual content, performing machine learning on the one or more assets in order to determine relevant words, phrases, and statistics included in the textual content, and displaying segments of data on a graphical user interface that also includes an interface that is used to create a message using content of the segments of the textual content that have been extracted from the one or more assets.

Abstract: A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

Abstract: A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

Abstract: A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

Abstract: Systems and methods of machine learning for digital assets and message creation are provided herein. The present disclosure includes mechanisms for receiving one or more assets that include textual content, performing machine learning on the one or more assets in order to determine relevant words, phrases, and statistics included in the textual content, and displaying segments of data on a graphical user interface that also includes an interface that is used to create a message using content of the segments of the textual content that have been extracted from the one or more assets.

Abstract: Systems and methods of machine learning for digital assets and message creation are provided herein. The present disclosure includes mechanisms for receiving one or more assets that include textual content, performing machine learning on the one or more assets in order to determine relevant words, phrases, and statistics included in the textual content, and displaying segments of data on a graphical user interface that also includes an interface that is used to create a message using content of the segments of the textual content that have been extracted from the one or more assets.

Abstract: Methods and systems for disentangling discriminative features of a user of a device from motion signals and authenticating a user on a mobile device are provided. In at least one aspect of the methods and systems, each captured motion signal is divided into segments. The segments are then converted into translated segments using one or more trained translation algorithms. The segments and translated segments are then provided to a machine learning system. Discriminative features of the user are then extracted from the segments and translated segments with the processor using the machine learning system that applies one or more feature extraction algorithms.

Abstract: A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

Abstract: A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.

Abstract: Systems and methods of machine learning for digital assets and message creation are provided herein. The present disclosure includes mechanisms for receiving one or more assets that include textual content, performing machine learning on the one or more assets in order to determine relevant words, phrases, and statistics included in the textual content, and displaying segments of data on a graphical user interface that also includes an interface that is used to create a message using content of the segments of the textual content that have been extracted from the one or more assets.

Abstract: A source content routing system is described for distributing source content received from clients such as documents, to translators for performing translation services on the source content. The routing system extracts source content features, which may be represented as vectors. The vectors may be assembled into an input matrix, which may be processed using an artificial neural network, classifier, perceptron, CRF model, and/or the like, to select a translator such as a machine translation system and/or human. The translator provides translation services translation from a source language to a target language, post translation editing, proof reading, quality analysis of a machine, quality analysis of human translation, and/or the like and returns the product to the content routing system or clients.