Rovi Guides, Inc.

Adeia Guides Inc.

Rovi Product Corporation

Rov Guides, Inc.

Manik Malhotra

Sashikumar Venkataraman

Ahmed Nizam Mohaideen P

Prabhat Gupta

Bryan Keith James

Charles Bonfield

Jon Wayne Heim

Thomas Page Odom

Jeffry Copps Robert Jose

Mario Dan Sanchez

Carla Mack

Phillip Teich

Systems and methods are described to address shortcomings in a conventional conversation system via a novel technique utilizing artificial neural networks to train the conversation system whether or not to continue context. In some aspects, an interactive media guidance application determines a type of conversation continuity in a natural language conversation comprising first and second queries. The interactive media guidance application determines a first token in the first query and a second token in the second query. The interactive media guidance application identifies entity data for the first and second tokens. The interactive media guidance application retrieves, from a knowledge graph, graph connections between the entity data for the first and second tokens. The interactive media guidance application applies this data as inputs to an artificial neural network. The interactive media guidance application determines an output that indicates the type of conversation continuity between the first and second queries.

Systems and methods for determining context switching in conversation

Systems and methods are disclosed herein for training a model to accurately determine whether two phrases are conversationally connected. A media guidance application may detect a first phrase and a second phrase, translate each phrase to a string of word types, append each string to the back of a prior string to create a combined string, determine a degree to which any of the individual strings matches any singleton template, and determine a degree to which the combined string matches any conversational template. Based on the degrees to which the individual and combination strings match the singleton and conversational templates, respectively, strengths of association are correspondingly updated.

Systems and methods for training a model to determine whether a query with multiple segments comprises multiple distinct commands or a combined command

Systems and methods provide a media guidance application that recognizes a plurality of natural language search queries for identifying a set of search results. For example, a user may want to determine when the Yankees are playing their next baseball game. The user may structure their query in multiple ways, such as, 'When are the Yankees playing?' “What time is the Yankees game?” “When is the next Yankees baseball game?” The user would expect the same result, a description of when the Yankees are playing, regardless of how the query is structured. The systems and methods enable a user to use a plurality of search queries when searching for items or information to get desired results.

Approximate template matching for natural language queries

Methods and systems for identifying a meaning of an ambiguous term in a natural language query. The media guidance application isolates first and second terms from a query received from a user and identifies, in a knowledge graph, first and second pluralities of candidate components associated with the first and second terms. The first and second terms each having multiple candidate components indicates the first and second terms have ambiguous meanings. The media guidance application matches each candidate component of the first and second pluralities of candidate components to form a plurality of pairs and determines strength of association for each pair in the plurality of pairs. The media guidance application filters the plurality of pairs by strength of association for each pair and determines a plurality of possible meanings based on the filtered plurality of pairs. The media guidance application selects a meaning from the plurality of possible meanings.

Systems and methods for identifying a meaning of an ambiguous term in a natural language query

Systems and methods are disclosed herein for identifying a category of a search term and providing search results subject to the identified category. These systems and methods may be performed by a media guidance application. The media guidance application may receive a search term that may correspond to different categories of items, and the media guidance application may identify a category that the search term is most likely related to using a hierarchical structure. The media guidance application may limit the search to the identified category of items.

Systems and methods for identifying a category of a search term and providing search results subject to the identified category

Systems and methods are disclosed herein for training a control system based on prior audio inputs. The disclosed systems and methods receive a non-lexical or interjectional audio input. State change indications are also received and stored by the system within a predefined period of time starting from the time the system received the audio input. The system then receives a subsequent audio input. If the audio inputs of both the audio input and the subsequent audio input match, and contextual information for the audio input and the subsequent audio input match, the system stores a match association, comprising a confidence factor, for the subsequent audio input to the audio input in the associative data structure. If the confidence factor is greater than a preconfigured confidence level, the system executes one or more functions based on stored state change indications.

Systems and methods for training a control system based on prior audio inputs

The methods and systems described herein leveraging acoustic features of a user to generate and present a personalized content to a user. In one example, the method receives a voice query and determines that the query refers to either a first content item or a second content item. The first content item is associated with a first type assigned with a first score and the second content method also determines whether the query is from the second entity type. The method ranks the first and the second content items based on this determination and generates for presentation of the first and the second content items based on the ranking. The method also changes the first or the second scores based on this determination and selects one of the first or the second content item for presentation.

Systems and methods for leveraging acoustic information of voice queries

The methods and systems described herein leveraging acoustic features of a user to generate and present a personalized content to a user. In one example, the method receives a voice query and determines that the query refers to either a first content item or a second content item. The first content item is associated with a first type assigned with a first score and the second content item is associated with a second entity type assigned with a second score. The method also determines whether the query is from the second entity type. The method ranks the first and the second content items based on this determination and generates for presentation of the first and the second content items based on the ranking. The method also changes the first or the second scores based on this determination and selects one of the first or the second content item for presentation.

Method and systems for facilitating communications using application programming interfaces ('APIs') by interpreting a received command based on the command and an image of the user interface that was displayed on a display screen when the command was received.

Methods and systems for facilitating application programming interface communications

Systems and methods provide a media guidance application that recognizes a plurality of natural language search queries for identifying a set of search results. For example, a user may want to determine when the Yankees are playing their next baseball game. The user may structure their query in multiple ways, such as, 'When are the Yankees playing?' 'What time is the Yankees game?' &#x201c;When is the next Yankees baseball game?&#x201d; The user would expect the same result, a description of when the Yankees are playing, regardless of how the query is structured. The systems and methods enable a user to use a plurality of search queries when searching for items or information to get desired results.

The system receives a voice query at an audio interface and converts the voice query to text. The system identifies entities included in the query based on comparison to an information graph, as well as dynamic types based on the structure and format of the query. The system can determine dynamic types by analyzing parts of speech, articles, parts of speech combinations, parts of speech order, influential features, and comparisons of these aspects to references. The system combines tags associated with the identified entities and tags associated with the dynamic types to generate query interpretations. The system compares the interpretations to reference templates, and selects among the query interpretations using predetermined criteria. A search query is generated based on the selected interpretation. The system retrieves content or associated identifiers, updates metadata, updates reference information, or a combination thereof. Accordingly, the system responds to queries that include non-static types.

Systems and methods for identifying dynamic types in voice queries

Systems and methods provided herein for a determining a meaning of an ambiguous term in a text segment based on a context term, a static knowledge graph and a temporal knowledge graph. These systems and methods access a first knowledge graph associated with the context term to determine a potential term that is the meaning of the unknown term. Upon determining that there are multiple potential terms in the first knowledge graph that could be the meaning of the unknown term, the systems and methods take into account the temporal dimension of relationships between entities for disambiguating the meaning of the unknown term. The systems and methods achieve this by determining a time stamp of the text segment and accessing a second knowledge graph associated with the first context term and related to the time stamp to determine the potential term that is the meaning of the first unknown term.

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Bengaluru, India

Manik Malhotra