An apparatus is provided for selecting an electronic calendar from a plurality of available electronic calendars for saving of an event for a user. The apparatus comprises a data storage storing user-specific inferences pertaining to the user; and a calendar suggestion module. The calendar suggestion module is configured to: apply semantic analysis to an indication of an event received at the apparatus; access the data storage to identify a user-specific inference that is relevant to the received event based on the semantic analysis; and using the relevant user-specific inference, select a calendar from the plurality of calendars to which the event should be stored. A corresponding method and computer program are also provided.

An in-vehicle speech interaction method and a device are provided. The method includes: obtaining user speech information; determining a user instruction based on the user speech information; determining, based on the user instruction, whether response content to the user instruction is privacy-related; and determining, based on whether the response content is privacy-related, whether to output the response content in a privacy protection mode, to protect privacy from being leaked.

Systems, methods and non-transitory computer readable media for analyzing objects data to generate a textual content reporting events are provided. An indication of an event may be received. An indication of a group of one or more objects associated with the event may be received. For each object of the group of one or more objects, data associated with the object may be received. The data associated with the group of one or more objects may be analyzed to select an adjective. A particular description of the event may be generated. The particular description may be based on the group of one or more objects. The particular description may include the selected adjective. A textual content may be generated. The textual content may include the particular description. The generated textual content may be provided.

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating topic divergence classifications for digital videos based on words from the digital videos and further based on a digital text corpus representing a target topic. Particularly, the disclosed systems utilize a topic-specific knowledge encoder neural network to generate a topic divergence classification for a digital video to indicate whether or not the digital video diverges from a target topic. In some embodiments, the disclosed systems determine topic divergence classifications contemporaneously in real time for livestream digital videos or for stored digital videos (e.g., digital video tutorials). For instance, to generate a topic divergence classification, the disclosed systems generate and compare contextualized feature vectors from digital videos with corpus embeddings from a digital text corpus representing a target topic utilizing a topic-specific knowledge encoder neural network.

The disclosure relates to AI-based machine-learning and natural language modeling to identify semantic similarities between sets of content having natural language text. For example, a system may generate a relevance classification that indicates whether content such as articles are non-specifically relevant to charities without identifying a particular charity. If the content is non-specifically relevant to charities, the system may apply a natural language model to generate sentence embeddings based on the content and determine a level similarity between the sentence embeddings and a query embedding generated from a charity query. The charity query may itself be generated from a full description of the charity through an encoder-decoder architecture with reinforcement learning.

The disclosure relates to AI-based machine-learning and natural language modeling to identify semantic similarities between sets of content having natural language text. For example, a system may generate a relevance classification that indicates whether content such as articles are non-specifically relevant to charities without identifying a particular charity. If the content is non-specifically relevant to charities, the system may apply a natural language model to generate sentence embeddings based on the content and determine a level similarity between the sentence embeddings and a query embedding generated from a charity query. The charity query may itself be generated from a full description of the charity through an encoder-decoder architecture with reinforcement learning.

Disclosed are methods and systems for providing audible flight information to an operator of an aircraft. A method, for example, may include receiving flight information detected by one or more sensors positioned on the aircraft, causing an image to be displayed on a display device, the image including a plurality of text items corresponding to the flight information, receiving a first operator selection indicative of one or more of the text items, parsing the one or more text items to generate a set of intermediate data, synthesizing audio data based on the intermediate data, and causing audible content corresponding to the audio data to be emitted by one or more audio emitting devices, wherein the audible content includes speech corresponding to the flight information.

Systems, methods, and applications for mobile intelligent outside sales assistance are provided. Embodiments include receiving speech for recognition of an outside sales call; converting the speech for recognition to text; parsing the converted text into outside sales triples; storing the outside sales triples in an enterprise knowledge graph of a semantic graph database; generating real-time outside sales insights in dependence upon the speech of the outside sales call and the stored outside triples in the enterprise knowledge graph; and presenting the real-time outside sales insights to an outside sales agent.

Methods, systems, and devices for automatically converting a static electronic file format and its various elements into a dynamic digital form with executable elements that can be customized before being used. The resulting digital form is compatible with digital workflows and processes. The disclosed systems, methods, and devices go beyond simply extracting data from the original electronic file format and instead enable users to, without using code, convert the source form into a dynamic, malleable digital form while still retaining the source form's original purpose and functionality.

In an example, a first set of text may be received from a client device. A set of content items may be selected from among content items based upon the first set of text and a plurality of sets of content item text associated with the content items. A set of terms may be determined based upon the first set of text and the set of content items. A similarity profile associated with the set of terms may be generated. The similarity profile is indicative of similarity scores associated with similarities between terms of the set of terms. Relevance scores associated with the set of terms may be determined based upon the similarity profile. One or more search terms may be selected from among the set of terms based upon the relevance scores. A search may be performed based upon the one or more search terms.