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.

The relationship between the smell or taste of an object and the expression of the smell or taste can be grasped, and the user can grasp what smell or taste they prefer. In the single-sample display mode, display control means displays a group of expressions relating to the olfactory sense stimulated by sample identified by sample identifying means, and if any expression is selected by the user, displays a relationship image indicating the relationship between the selected expression and other samples corresponding to the olfactory sense associated with the expression. In addition, in the multiple-sample display mode, display control means displays, for each of plural samples specific by sample identifying means, a group of expressions relating to the sense of smell stimulated by each sample, and displays the expression common to plural samples among the group of expressions to be distinguishable from the expression common to plural samples.

The present disclosure provides a summary generation model training method and apparatus, a device and a storage medium, and relates to the field of computer technologies, and in particular, to the field of artificial intelligence such as natural language processing and deep learning. The summary generation model training method includes: acquiring a document representation corresponding to a document sample; constructing, based on the document representation, a summary representation corresponding to the document representation, the summary representation including a positive summary representation and a negative summary representation; and constructing a total contrastive loss function based on the document representation, the positive summary representation and the negative summary representation, and training a summary generation model based on the total contrastive loss function. The present disclosure may improve accuracy of the summary generation model.

An apparatus and computer implemented method that include obtaining, into a computer, text of a patent, automatically finding and extracting, using the computer, a set of claim text from the patent text, identifying, using the computer, text of independent claims from the set of claim text, displaying in a first row on a computer monitor the text of the independent claims, automatically determining a plurality of preliminary scope-concept phrases from the text of the independent claims, displaying in a second row on the computer monitor the text of the plurality of preliminary scope-concept phrases, eliciting and receiving user input to specify a first one of the plurality of preliminary scope-concepts phrases, and highlighting each occurrence of the specified first one of the plurality of preliminary scope-concept phrases in a plurality of the independent claims displayed in the first row. A scope concept builder tool is also provided.

Systems and methods are provided herein for enabling a computing system to search and interact with service records containing natural language text to aid in the analysis of those records by: displaying in a user interface an image of a complex system; receiving, from the user interface, a high-level selection of criteria of the complex system; querying issue maps associated with individual natural language service records of a corpus of natural language service records based on the high-level selection, wherein the issue maps specify at least one term related to the complex system and a location on the complex system associated with the at least one term; and returning at least one issue map, wherein the at least one issue map returned specifies a term or location correlated to the criteria of the complex system indicated by the high-level selection.

Systems and methods are provided herein for enabling a computing system to search and interact with service records containing natural language text to aid in the analysis of those records by: displaying in a user interface an image of a complex system; receiving, from the user interface, a high-level selection of criteria of the complex system; querying issue maps associated with individual natural language service records of a corpus of natural language service records based on the high-level selection, wherein the issue maps specify at least one term related to the complex system and a location on the complex system associated with the at least one term; and returning at least one issue map, wherein the at least one issue map returned specifies a term or location correlated to the criteria of the complex system indicated by the high-level selection.

A mechanism for estimating the relevance or importance of sections of unstructured (medical) textual data. Different sections are visually emphasized responsive to a user input, and one or more characteristics of when the sections are visually emphasized are monitored. Other characteristics of the sections may also be monitored or determined. These characteristics are then processed to predict or determine a relevance of each section.

Embodiments may provide a document system that receives a responsiveness call from a user through the task/queue framework regarding a machine call document. Theses responsiveness calls may be used to refining the scoring algorithm used by the document system of to generate a desired confidence score for the document system.

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

Programs, systems, and methods for generating a support tree for automated resolution of user issues. In some embodiments, historical data may be obtained from a history of support interactions including historical issues and solutions to the historical issues. Most used solutions may be determined and stored with associated historical issues. Clusters of issues and solutions may be generated and labeled for generation of support tree nodes. Furthermore, solutions may be correlate such that a plurality of solutions may be provided for the resolving user issues. In some embodiments, issue data indicative of user issues may be provided by users. As the user provides issue data, deflections between nodes on the support tree may narrow a field of potential solutions to one or more solutions to be provided to the user.