A method for performing a query based on a natural language voice input is provided. The method includes receiving, via a microphone, a voice input of a user, and converting the voice input into a first text data object. The method further includes converting the first text data object into a first technical language object using AI, and submitting a query based on the first technical language object. A query result in a second technical language object is retrieved in response to the query, and the query result is converted into a second text data object using AI. The method further converts the second text object into a voice data object indicating the query result, and outputs a voice signal to provide the information of the query result in a natural language form to the user.

A system that provides for the accessing and playing of media files having differing associated rights such as non-DRM media files, purchased and downloaded media files, subscription download files such as tethered downloads, and subscription streamed DRM files. The system also provides a method and user interface for sharing a media collection among computing devices in communication via a network. The system allows access and playback, from each computing device on a network, of all media files in a media collection, regardless of their associated rights.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate a dynamic cross-platform ask interface and utilize a cross-platform language processing model to provide platform-specific, contextually based responses to natural language digital text queries. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to extract registered intents from digital text queries to identify platform-specific configurations associated with the registered intents. Utilizing the platform-specific configurations, the disclosed systems can generate tailored platform-specific requests for information, as well as customized end-user search results that cause client devices to efficiently, accurately, and flexibly render platform-specific search results.

A system and method for advanced document redaction are disclosed. According to one embodiment, a system comprises a parser that analyzes documents to identify structured, semi-structured, and unstructured data from a document. A candidates generator generates a list of words for redaction from the structured, semi-structured, and unstructured data. A replacement engine replaces one or more words from the list of words with one or more of a replacement word, random characters, and random numbers.

Embodiments of the present disclosure provide a method and apparatus for outputting information. A specific embodiment of the method includes: in response to receiving a query, detecting whether there is an entity slot in the query; in response to there being an entity slot in the query, adding the detected entity slot to a candidate slot; detecting, in the query, a relationship-determinative word of an entity; searching in a preset knowledge graph for a peripheral knowledge graph of the candidate slot; and inferring on the basis of the peripheral knowledge graph according to the relationship-determinative word, and outputting an entity word matching the relationship-determinative word.

Systems and methods are disclosed for utilizing a comment moderation bot for detecting and normalizing neologisms in social media. One method comprises transmitting, by a neologism normalization system, a comment moderation bot for detecting neologisms on an online platform maintained by one or more publisher systems. The comment moderation bot may aggregate data related to user comments and transmit the aggregated data to the neologism normalization system for further processing. The neologism normalization system implements unsupervised machine learning models for detecting neologisms in the aggregated data through tokenization and filtering; and normalizing the neologisms through similarity analysis and lattice decoding.

Techniques are described herein for assembling/evaluating automated assistant responses for privacy concerns. In various implementations, a free-form natural language input may be received from a first user and may include a request for information pertaining to a second user. Multiple data sources may be identified that are accessible by an automated assistant to retrieve data associated with the second user. The multiple data sources may collectively include sufficient data to formulate a natural language response to the request. Respective privacy scores associated with the multiple data sources may be used to determine an aggregate privacy score associated with responding to the request. The natural language response may then be output at a client device operated by the first user in response to a determination that the aggregate privacy score associated with the natural language response satisfies a privacy criterion established for the second user with respect to the first user.

A system and method for extracting search terms for corresponding data elements from a natural language document identifies meaningful words within the context; identifies and structures the keywords; expounds on the keywords to optimize the search results; and captures the most relevant data elements from the corresponding database. Predetermined demographic characteristics and short (one- or two-word) search phrases that capture descriptors of behavioral characteristics are structured in the process. The result of the completed process yields a parameter set naming demographic and behavioral characteristics along with a structure that is optimized for search within a database comprising a large number of data elements.

The present invention discloses numerous implementations of system and method which receives a user request and, using methods of natural language processing including part of speech tagging, analyses the user request to generate a query to a database of information. Based on the machine understanding, the system presents an interactive representation of the uttered request back to the user. This provides context to the user, which explains the machine understanding of the request and acts as an interface to iteratively refine or adjust the machine understanding by altering specific elements of the uttered language. The methods of altering specific elements of the uttered language may vary depending on the element and a variety of user selectable interfaces may be used to display one or more queried elements along with alternative elements pertaining to the queried element. The user could select an alternative element and change the database query.

A system for extracting latent information from data includes obtaining or generating components of the data, where the data components include scores indicating how the component relates to the data. Memory is allocated for the components and the components are stored in the allocated memory. The components are then transformed into documents using a suitable transformation function, and the documents are analyzed using natural language processing, to extract latent information contained in the data.