Abstract: Aspects of present disclosure relate to real-time image generation. An exemplary apparatus for real time image generation includes at least an optical system, a slide port configured to hold a slide, an actuator mechanism mechanically connected to a mobile element, a user interface comprising an input interface and an output interface, at least processor configured to: using the at least an optical system, capture a first image of the slide at a first position, modify the first image, using the output interface, display the first image to a user, using the input interface, receive a parameter set from the user.

Abstract: A method of disambiguating user intent in conversational interactions for information retrieval is disclosed. The method includes providing access to a set of content items with metadata describing the content items and providing access to structural knowledge showing semantic relationships and links among the content items. The method further includes providing a user preference signature, receiving a first input from the user that is intended by the user to identify at least one desired content item, and determining an ambiguity index of the first input. If the ambiguity index is high, the method determines a query input based on the first input and at least one of the structural knowledge, the user preference signature, a location of the user, and the time of the first input and selects a content item based on comparing the query input and the metadata associated with the content item.

Abstract: Methods and systems are disclosed herein for accurately training a machine learning model with a reduced training data set. A large number of data records may be parsed. Each record may be reduced to a set of symbols representing the composition of each record. A user may assign a classification to each symbol within each record. Records with identical arrangements and classifications of symbols may be grouped together, and a representative sample of data records from each group may be fed into the model as the reduced training data set.

Abstract: A method of disambiguating user intent in conversational interactions for information retrieval is disclosed. The method includes providing access to a set of content items with metadata describing the content items and providing access to structural knowledge showing semantic relationships and links among the content items. The method further includes providing a user preference signature, receiving a first input from the user that is intended by the user to identify at least one desired content item, and determining an ambiguity index of the first input. If the ambiguity index is high, the method determines a query input based on the first input and at least one of the structural knowledge, the user preference signature, a location of the user, and the time of the first input and selects a content item based on comparing the query input and the metadata associated with the content item.

Abstract: Methods and systems are disclosed herein for accurately training a machine learning model with a reduced training data set. A large number of data records may be parsed. Each record may be reduced to a set of symbols representing the composition of each record. A user may assign a classification to each symbol within each record. Records with identical arrangements and classifications of symbols may be grouped together, and a representative sample of data records from each group may be fed into the model as the reduced training data set.

Abstract: Systems and methods are described herein for updating links between keywords associated with a trending topic. Control circuitry may identify a plurality of communications associated with a plurality of users, the plurality of communications being related to a topic. The communications may comprise, for example, emails, text messages, online social media posts, websites or portions of a website, or any other online communication. The control circuitry may determine whether the topic is trending and increase a strength of association between a first symbol associated with the topic and a second symbol associated with the topic such that when a search query is received that includes the first symbol, search results related to the second symbol have a higher probability of being returned.

Abstract: A method of and system for enhanced local-device content discovery is disclosed. A method includes providing a local collection of content items and associated metacontent on a device. The metacontent describes the informational content of the corresponding content items. A remote catalog of metacontent items and information describing relationships between the metacontent items is provided. Input is received from the user for finding at least one desired content item and a search query is formed including at least one metacontent item of the remote catalog based on matching the input from the user with metacontent items of the catalog and further based on the information describing relationships between the metacontent items. A subset of content items of the local collection associated with metacontent that at least partially matches the search query is selected and presented on a display device based on at least one ranking criterion.

Abstract: Systems and methods are described herein for updating links between keywords associated with a trending topic. Control circuitry may identify a plurality of communications associated with a plurality of users, the plurality of communications being related to a topic. The communications may comprise, for example, emails, text messages, online social media posts, websites or portions of a website, or any other online communication. The control circuitry may determine whether the topic is trending and increase a strength of association between a first symbol associated with the topic and a second symbol associated with the topic such that when a search query is received that includes the first symbol, search results related to the second symbol have a higher probability of being returned.

Abstract: Methods and systems are disclosed herein for accurately training a machine learning model with a reduced training data set. A large number of data records may be parsed. Each record may be reduced to a set of symbols representing the composition of each record. A user may assign a classification to each symbol within each record. Records with identical arrangements and classifications of symbols may be grouped together, and a representative sample of data records from each group may be fed into the model as the reduced training data set.

Abstract: A method of and system for enhanced local-device content discovery is disclosed. A method includes providing a local collection of content items and associated metacontent on a device. The metacontent describes the informational content of the corresponding content items. A remote catalog of metacontent items and information describing relationships between the metacontent items is provided. Input is received from the user for finding at least one desired content item and a search query is formed including at least one metacontent item of the remote catalog based on matching the input from the user with metacontent items of the catalog and further based on the information describing relationships between the metacontent items. A subset of content items of the local collection associated with metacontent that at least partially matches the search query is selected and presented on a display device based on at least one ranking criterion.

Abstract: A method of disambiguating user intent in conversational interactions for information retrieval is disclosed. The method includes providing access to a set of content items with metadata describing the content items and providing access to structural knowledge showing semantic relationships and links among the content items. The method further includes providing a user preference signature, receiving a first input from the user that is intended by the user to identify at least one desired content item, and determining an ambiguity index of the first input. If the ambiguity index is high, the method determines a query input based on the first input and at least one of the structural knowledge, the user preference signature, a location of the user, and the time of the first input and selects a content item based on comparing the query input and the metadata associated with the content item.

Abstract: A method of disambiguating user intent in conversational interactions for information retrieval is disclosed. The method includes providing access to a set of content items with metadata describing the content items and providing access to structural knowledge showing semantic relationships and links among the content items. The method further includes providing a user preference signature, receiving a first input from the user that is intended by the user to identify at least one desired content item, and determining an ambiguity index of the first input. If the ambiguity index is high, the method determines a query input based on the first input and at least one of the structural knowledge, the user preference signature, a location of the user, and the time of the first input and selects a content item based on comparing the query input and the metadata associated with the content item.

Abstract: A method for using speech disfluencies detected in speech input to assist in interpreting the input is provided. The method includes providing access to a set of content items with metadata describing the content items, and receiving a speech input intended to identify a desired content item. The method further includes detecting a speech disfluency in the speech input and determining a measure of confidence of a user in a portion of the speech input following the speech disfluency. If the confidence measure is lower than a threshold value, the method includes determining an alternative query input based on replacing the portion of the speech input following the speech disfluency with another word or phrase. The method further includes selecting content items based on comparing the speech input, the alternative query input (when the confidence measure is low), and the metadata associated with the content items.

Abstract: A method of disambiguating user intent in conversational interactions for information retrieval is disclosed. The method includes providing access to a set of content items with metadata describing the content items and providing access to structural knowledge showing semantic relationships and links among the content items. The method further includes providing a user preference signature, receiving a first input from the user that is intended by the user to identify at least one desired content item, and determining an ambiguity index of the first input. If the ambiguity index is high, the method determines a query input based on the first input and at least one of the structural knowledge, the user preference signature, a location of the user, and the time of the first input and selects a content item based on comparing the query input and the metadata associated with the content item.

Abstract: Methods and systems that improve the ability of a media guidance application to aggregate information from one or more sources and one or more mediums. For example, the media guidance application may automatically collect information based on attributes associated with information of a particular type. Specifically, the media guidance application may determine based on comparison with verified training data that one source or medium typically associates information of a particular type, for example, “Actor,” with one or more path attributes, for example, a location in a directory structure. The media guidance application may then search the source or medium for the one or more path attributes. Upon detecting the one or more path attributes, the media guidance application may designate any sub-set of information associated with the one or more path attributes as a particular type of information.

Abstract: A method of disambiguating user intent in conversational interactions for information retrieval is disclosed. The method includes providing access to a set of content items with metadata describing the content items and providing access to structural knowledge showing semantic relationships and links among the content items. The method further includes providing a user preference signature, receiving a first input from the user that is intended by the user to identify at least one desired content item, and determining an ambiguity index of the first input. If the ambiguity index is high, the method determines a query input based on the first input and at least one of the structural knowledge, the user preference signature, a location of the user, and the time of the first input and selects a content item based on comparing the query input and the metadata associated with the content item.

Abstract: Systems and methods are described for managing local and cloud storage space for a plurality of media assets. The media guidance application may detect that a command was received to delete a first media asset from storage. In response to detecting the command, the media guidance application may determine a viewing pattern for the first media asset associated with a subset of users in a plurality of users, where the subset of users consumed the first media asset. The media guidance application may retrieve viewing commands associated with a second media asset performed by the subset of users, where the second media asset is related to the first media asset. The media guidance application may determine whether the viewing commands satisfy the viewing pattern. And in response to determining that the commands satisfy the viewing pattern, the media guidance application may delete the second media asset from storage.

Abstract: Systems and methods are described for managing local and cloud storage space for a plurality of media assets. The media guidance application may detect that a command was received to delete a first media asset from storage. In response to detecting the command, the media guidance application may determine a viewing pattern for the first media asset associated with a subset of users in a plurality of users, where the subset of users consumed the first media asset. The media guidance application may retrieve viewing commands associated with a second media asset performed by the subset of users, where the second media asset is related to the first media asset. The media guidance application may determine whether the viewing commands satisfy the viewing pattern. And in response to determining that the commands satisfy the viewing pattern, the media guidance application may delete the second media asset from storage.

Abstract: Methods and systems are disclosed herein for accurately training a machine learning model with a reduced training data set. A large number of data records may be parsed. Each record may be reduced to a set of symbols representing the composition of each record. A user may assign a classification to each symbol within each record. Records with identical arrangements and classifications of symbols may be grouped together, and a representative sample of data records from each group may be fed into the model as the reduced training data set.

Abstract: A method for using speech disfluencies detected in speech input to assist in interpreting the input is provided. The method includes providing access to a set of content items with metadata describing the content items, and receiving a speech input intended to identify a desired content item. The method further includes detecting a speech disfluency in the speech input and determining a measure of confidence of a user in a portion of the speech input following the speech disfluency. If the confidence measure is lower than a threshold value, the method includes determining an alternative query input based on replacing the portion of the speech input following the speech disfluency with another word or phrase. The method further includes selecting content items based on comparing the speech input, the alternative query input (when the confidence measure is low), and the metadata associated with the content items.