The presently disclosed embodiments may include a computer readable medium including instructions that when executed by one or more processing devices cause the one or more processing devices to perform a method. The method may include: analyzing an electronic document text; identifying in the electronic document text a first drafted text element to be modified; causing the first drafted text element to be highlighted to a user on a display; generating at least one text output option that conveys a meaning associated with the first drafted text element but includes one or more changes relative to the first drafted text element; and causing the at least one text output option to be shown to the user via the display.

A method for rendering context based information on a user interface includes receiving a user request to extract the context based information from a database. The database includes a plurality of documents and the request includes at least one search criteria required to determine a context of the user request. The method includes generating a list of documents corresponding to the context of the user request and rendering on a viewing portion of the user interface the list of documents corresponding to the context of the user request.

Embodiments of the present disclosure provide methods, apparatus, systems, computer program products for transferring a performance of a procedure found in technical documentation for an item via an interactive electronic technical manual system (IETM) configured to provide electronic and credentialed access to the technical documentation. In one embodiment, a method is provided comprising: providing the steps of the procedure in an order in which the steps are to be carried out; and while a user is participating in the performance of the procedure: causing a particular step that is being carried out to be highlighted; receiving input of a selection of a transfer mechanism and in response: causing an indication to be displayed between the particular step and a next step to be carried out identifying where the performance has been suspended; providing a transfer window displaying transfer information; and recording the transfer information and an identifier for the indication.

Embodiments of the present disclosure provide methods, apparatus, systems, computer program products for transferring a performance of a procedure found in technical documentation for an item via an interactive electronic technical manual system (IETM) configured to provide electronic and credentialed access to the technical documentation. In one embodiment, a method is provided comprising: providing the steps of the procedure in an order in which the steps are to be carried out; and while a user is participating in the performance of the procedure: causing a particular step that is being carried out to be highlighted; receiving input of a selection of a transfer mechanism and in response: causing an indication to be displayed between the particular step and a next step to be carried out identifying where the performance has been suspended; providing a transfer window displaying transfer information; and recording the transfer information and an identifier for the indication.

This application relates to apparatus and methods for automatically generating item information, such as item descriptions, and providing the item information to customers. For example, the embodiments may generate and provide personalized item descriptions to customers during conversational interactions in speech-based systems. In some examples, the embodiments determine entities (e.g., attributes) from item information, and apply trained machine learning processes to the extracted entities to generate textual data, such as item descriptions. For example, a computing device may apply a trained natural language processing, such as a trained transformer-based machine learning technique, to the extracted entities to generate the item descriptions. In some examples, the computing device applies post processing techniques to the generated textual data. The generated textual data may include descriptive phrases that are user friendly to customers in an e-commerce system. The textual data can be converted to audio and played to customers.

This application relates to apparatus and methods for automatically generating item information, such as item descriptions, and providing the item information to customers. For example, the embodiments may generate and provide personalized item descriptions to customers during conversational interactions in speech-based systems. In some examples, the embodiments determine entities (e.g., attributes) from item information, and apply trained machine learning processes to the extracted entities to generate textual data, such as item descriptions. For example, a computing device may apply a trained natural language processing, such as a trained transformer-based machine learning technique, to the extracted entities to generate the item descriptions. In some examples, the computing device applies post processing techniques to the generated textual data. The generated textual data may include descriptive phrases that are user friendly to customers in an e-commerce system. The textual data can be converted to audio and played to customers.

The present disclosure relates to a system, method, and computer program for testing the accuracy of software that identifies user interface (UI) elements on a webpage (“the auto-identifier software”). The system enables a user to tag UI elements on a webpage with labels. The system creates a normalized specification for the webpage, where the specification includes a mapping of UI elements to normalized labels. The system uses the auto-identifier software to identify UI elements on the webpage. The system evaluates the performance of the auto-identifier software with respect to the webpage using the specification. The system displays diagnostics related to the performance of the auto-identifier software. In certain embodiments, the method is used for testing the accuracy of autofill software on a webform. In certain embodiments, the method is used for testing the accuracy of cart-scraper software on a checkout page.

The present disclosure relates to a system, method, and computer program for testing the accuracy of software that identifies user interface (UI) elements on a webpage (“the auto-identifier software”). The system enables a user to tag UI elements on a webpage with labels. The system creates a normalized specification for the webpage, where the specification includes a mapping of UI elements to normalized labels. The system uses the auto-identifier software to identify UI elements on the webpage. The system evaluates the performance of the auto-identifier software with respect to the webpage using the specification. The system displays diagnostics related to the performance of the auto-identifier software. In certain embodiments, the method is used for testing the accuracy of autofill software on a webform. In certain embodiments, the method is used for testing the accuracy of cart-scraper software on a checkout page.

This disclosure describes a system that analyzes a corpus of text (e.g., a financial article, an audio book, etc.) so that the context surrounding the text is fully understood. For instance, the context may be an environment described by the text, or an environment in which the text occurs. Based on the analysis, the system can determine sentiment, part of speech, entities, and/or human characters at the token level of the text, and automatically generate Speech Synthesis Markup Language (SSML) tags based on this information. The SSML tags can be used by applications, services, and/or features that implement text-to-speech (TTS) conversion to improve the audio experience for end-users. Consequently, via the techniques described herein, more realistic and human-like speech synthesis can be efficiently implemented at larger scale (e.g., for audio books, for all the articles published to a news site, etc.).

Embodiments disclosed herein include systems and methods for generating and editing digital documents, and applying electronic signatures to the documents. The system further performs forensic operations on digital documents for determining the authenticity of the document data or metadata and validating the status of document. The forensic operations may include, for example, security operations, audit trail analysis, and authenticity/verification analysis, which the system performs on an input document being scrutinized for authenticity and a master digital document purportedly corresponding to the input digital document. The system may apply a machine-learning architecture to identify discrepancies otherwise imperceptible to humans or to speed the review process. The system may generate a report for the user who submitted the forensics request indicating the results of the forensic operations. The available forensics operations and level of information about the master digital document are tailored to permissions afforded to the submitting-user.