Abstract: Systems and methods for handling multilingual queries are provided. One example method includes receiving, at a computing device, an input, wherein the input comprises a multi-lingual query comprising at least a first source language and a second source language. The multi-lingual query is translated, word for word, into a destination language to produce a monolingual query, with the word order of the multilingual query and the word order of the monolingual query being the same. The monolingual query is processed using natural language processing to map the mono-lingual query to a natural language query in the destination language.

Abstract: Systems and methods are described for modifying a phonetic search index based on a use frequency associated with phonetic representations of text terms included in metadata of a media item. A first phonetic representation of a text term of the metadata, pronounced as a word, may be generated. A second phonetic representation of the text term may be generated by concatenating a phonetic representation of each letter in the text term. A database may be queried to determine use frequencies of the first and second phonetic representations, one of which may be selected based on a comparison of the use frequencies. A phonetic search index may be modified by including an entry for the selected phonetic representation. A voice query related to the media item may be received, and a reply to the voice query may be generated for output by performing a lookup in the modified phonetic search index.

Abstract: A frequency of occurrence for each term in a training data set is determined in relation to the entire training data set. A relational data structure is generated that associates each term in the training data with its respective frequency. Any term that has a frequency below a threshold frequency is then added to a list of relevant words. When a natural language search query is received, a plurality of terms in the natural language search query are identified and compared with the list of relevant words. If any term of the natural language search query is included in the relevant words list, that term is identified as a keyword. The natural language search query is then interpreted based on any identified keywords.

Abstract: Systems and methods are described herein for interpreting natural language search queries that account for contextual relevance of words of the search query that would ordinarily not be processed, including, for example, processing each word of the query. Each term or phrase is associated with a respective part of speech, and a frequency of occurrence of a combination of adjacent terms or phrases public domain is determined. A relevance of each term is then determined based on its respective type of term and frequency of occurrence in the public domain. The natural language search query is then interpreted based on the importance or relevance of each term.

Abstract: Systems and methods are described herein for interpreting natural language search queries that account for contextual relevance of words of the search query that would ordinarily not be processed, including, for example, processing each word of the query. Each term is associated with a respective part of speech, and a frequency of occurrence of each term in content metadata is determined. A relevance of each term is then determined based on its respective part of speech and frequency. The natural language search query is then interpreted based on the importance or relevance of each term.

Abstract: Disclosed is a first device and a second device each sending BFD echo request packets in an initial stage of establishing communication between the two devices. A method can include determining that a certain mode is detected, such as a low bandwidth mode. The method can be practiced by one or both of the devices or a separate network controller. Based on the detection of the certain mode or the communication link being in a certain mode, the method includes the first device or the second device electing to be a master or a slave. Upon establishing that one of the devices is the master and the other of the devices is a slave, only the master will send BFD echo request packets.

Abstract: Systems and methods are described herein for processing negation in a natural language search query. The natural language search query is parsed to identify a plurality of portions of the natural language search query, and a first portion is determined to be a negation portion. A second portion to which the negation portion applies and a third portion to which the negation portion does not apply are then identified, and a content query is generated which includes a negation parameter corresponding to the second portion, and at least one other parameter corresponding to at least the third portion. Search results of the content query are then generated for output to the user.

Abstract: Systems and methods are described herein for interpreting natural language search queries that account for contextual relevance of words of the search query that would ordinarily not be processed, including, for example, processing each word of the query. Each term is associated with a respective part of speech, and a frequency of occurrence of each term in content metadata is determined. A relevance of each term is then determined based on its respective part of speech and frequency. The natural language search query is then interpreted based on the importance or relevance of each term.

Abstract: Systems and methods are described for modifying a phonetic search index based on a use frequency associated with phonetic representations of text terms included in metadata of a media item. A first phonetic representation of a text term of the metadata, pronounced as a word, may be generated. A second phonetic representation of the text term may be generated by concatenating a phonetic representation of each letter in the text term. A database may be queried to determine use frequencies of the first and second phonetic representations, one of which may be selected based on a comparison of the use frequencies. A phonetic search index may be modified by including an entry for the selected phonetic representation. A voice query related to the media item may be received, and a reply to the voice query may be generated for output by performing a lookup in the modified phonetic search index.

Abstract: Systems and methods are described herein for interpreting natural language search queries that account for contextual relevance of words of the search query that would ordinarily not be processed, including, for example, processing each word of the query. Each term or phrase is associated with a respective part of speech, and a frequency of occurrence of a combination of adjacent terms or phrases public domain is determined. A relevance of each term is then determined based on its respective type of term and frequency of occurrence in the public domain. The natural language search query is then interpreted based on the importance or relevance of each term.

Abstract: Systems and methods are described herein for interpreting natural language search queries that account for contextual relevance of words of the search query that would ordinarily not be processed, including, for example, processing each word of the query. Each term or phrase is associated with a respective part of speech, and a frequency of occurrence of a combination of adjacent terms or phrases public domain is determined. A relevance of each term is then determined based on its respective type of term and frequency of occurrence in the public domain. The natural language search query is then interpreted based on the importance or relevance of each term.

Abstract: Systems and methods are described to access a set of reattempt query pairs, where each respective pair comprises an initial query and a reattempt of the initial query, and is associated with an indication of whether a reply generated for output based on the respective query pair was acceptable. In response to determining that a second query received after a first query constitutes a reattempt of the first query, a query pair in the set of reattempt query pairs may be identified that matches at least one of the first query and the second query, and is associated with an indication that a reply generated for output based on the query pair was acceptable. A search may be performed based on the identified query pair in the set of reattempt query pairs, and a reply may be generated for output based on the performed search.

Abstract: Insertion of supplemental content into a virtual environment is automated using a machine learning model. The machine learning model is trained to calculate a confidence value that a candidate virtual object fits into a virtual environment based on an input that includes a candidate virtual object, a list of persistent virtual objects, and a list of temporary virtual objects. The machine learning model is trained using the persistent and temporary objects displayed in the current virtual environment until it predicts that a selected virtual object fits into the current virtual environment. The trained machine learning model is then used to select a virtual object comprising supplemental content to be inserted as a new virtual object in the virtual environment.

Abstract: Systems and methods are presented herein for generating a new language understanding model, based on a user request. A user may input a root language and a locale into an application for generating a student language model. The application may generate the student language model and may identify a teacher language model related to the student language model. The application may compare data from the identified teacher language model to the student language model. The application may determine a subset of data from the teacher language model is not contained in the student language model. If the application determines at least a subset of data from the teacher language model is not in the student language model, the application may add at least the subset of data from the teacher language model to the student language model.

Abstract: A WAN controller can configure a first WAN interface on a first WAN edge device to connect to a first transport network. The WAN controller can configure a second WAN interface on a second WAN edge device to connect to a second transport network. The WAN controller or the first WAN edge device, connected to a first access device of an access network, can determine the first WAN edge device has no Layer 2 connectivity to the second WAN edge device, connected to a second access device of the access network. The WAN controller or the first WAN edge device can establish a tunnel between the first WAN edge device and the second WAN interface. The WAN controller can route a specified class of WAN traffic received by the first WAN edge device from the access network through the tunnel.

Abstract: An ejection system may comprise an ejection seat and a smart deployment system configured to initiate a deployment of the ejection seat. The smart deployment system may comprise a pilot health monitoring system including a plurality of sensors configured to detect a plurality of physiological conditions, and a controller configured to make ejection system decisions based on a pilot health signal received from the pilot health monitoring system and an aircraft health signal output from an aircraft health monitoring system.

Abstract: Systems and methods are described herein for interpreting natural language search queries that account for contextual relevance of words of the search query that would ordinarily not be processed, including, for example, processing each word of the query. Each term or phrase is associated with a respective part of speech, and a frequency of occurrence of a combination of adjacent terms or phrases public domain is determined. A relevance of each term is then determined based on its respective type of term and frequency of occurrence in the public domain. The natural language search query is then interpreted based on the importance or relevance of each term.

Abstract: Systems and methods are disclosed herein for presenting supplemental content in an extended reality (XR) environment. The system may receive fields of view of an XR environment and generate a data stream representing the fields of view, the data stream comprising, for each field of view, a data structure including at least one object identifier corresponding to a visual item appearing on the respective field of view, and field coordinates representing the position of the field of view in the XR environment. The system may compute an importance score using an occurrence number of the object identifier and a view change rate using the field coordinates. In response to the importance score exceeding an importance threshold, the system generates instructions for displaying in the XR environment supplemental content, related to the visual item.

Abstract: Systems and methods for handling multilingual queries are provided. One example method includes receiving, at a computing device, an input, wherein the input comprises a multi-lingual query comprising at least a first source language and a second source language. The multi-lingual query is translated, word for word, into a destination language to produce a monolingual query, with the word order of the multilingual query and the word order of the monolingual query being the same. The monolingual query is processed using natural language processing to map the mono-lingual query to a natural language query in the destination language.

Abstract: Insertion of supplemental content into a virtual environment is automated using a machine learning model. The machine learning model is trained to calculate a confidence value that a candidate virtual object fits into a virtual environment based on an input that includes a candidate virtual object, a list of persistent virtual objects, and a list of temporary virtual objects. The machine learning model is trained using the persistent and temporary objects displayed in the current virtual environment until it predicts that a selected virtual object fits into the current virtual environment. The trained machine learning model is then used to select a virtual object comprising supplemental content to be inserted as a new virtual object in the virtual environment.