The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

The present disclosure provides a question answering method and apparatus for query information. The method may include: receiving query information input by a user, and analyzing a query target comprised in the query information; recalling candidate answers from a pre-generated knowledge graph based on the query target, where the knowledge graph is constructed based on inherent data in a map database and dynamic data of historical users, and the dynamic data includes at least one of comment data, search data, or spatiotemporal big data; and returning, in response to that there is a target answer whose matching degree with the query target exceeds a preset threshold in the candidate answers, the target answer to the user.

The present disclosure provides a question answering method and apparatus for query information. The method may include: receiving query information input by a user, and analyzing a query target comprised in the query information; recalling candidate answers from a pre-generated knowledge graph based on the query target, where the knowledge graph is constructed based on inherent data in a map database and dynamic data of historical users, and the dynamic data includes at least one of comment data, search data, or spatiotemporal big data; and returning, in response to that there is a target answer whose matching degree with the query target exceeds a preset threshold in the candidate answers, the target answer to the user.

A vehicle-user interaction system for a vehicle includes: an editor configured to receive one or more inputs from a user of the vehicle and edit the one or more inputs into a rule script, the rule script including a user-defined rule based on the one or more inputs, the user-defined rule defining a trigger condition and a vehicle operation performed when the trigger condition is satisfied; a parser configured to create a list of monitoring elements and a list of functional elements based on the rule script, the list of monitoring elements including sensor elements that directly or indirectly describe the trigger condition, and the list of functional elements including functional elements that are associated with the vehicle operation; and an actuator configured to monitor sensor detection information associated with the sensor elements, determine whether the trigger condition is satisfied, and execute the functional elements to implement the user-defined rule in the vehicle.

A vehicle-user interaction system for a vehicle includes: an editor configured to receive one or more inputs from a user of the vehicle and edit the one or more inputs into a rule script, the rule script including a user-defined rule based on the one or more inputs, the user-defined rule defining a trigger condition and a vehicle operation performed when the trigger condition is satisfied; a parser configured to create a list of monitoring elements and a list of functional elements based on the rule script, the list of monitoring elements including sensor elements that directly or indirectly describe the trigger condition, and the list of functional elements including functional elements that are associated with the vehicle operation; and an actuator configured to monitor sensor detection information associated with the sensor elements, determine whether the trigger condition is satisfied, and execute the functional elements to implement the user-defined rule in the vehicle.

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 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.

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.

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 as described herein generate an outstanding check alert. An alert generating server may receive transaction records associated with a plurality of checking accounts. The alert generating server may user a first machine learning classifier to determine a transaction pattern indicating a merchant has failed to process outstanding checks for a period of time. The alert generating server may receive sequential check information comprising at least one missing check number associated with a particular checking account. The alert generating server may user a second machine learning classifier to determine at least one outstanding check associated with the particular checking account. The alert generating server may send an alert indicating the at least one outstanding check to a user device.