KR20140034034A - Answering questions using environmental context - Google Patents

Abstract

Disclosed are methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving an utterance and environmental data, obtaining a transcription of the utterance, identifying an entity using the environmental data, submitting a query to a natural language query processing engine, wherein the query includes at least a portion of the transcription and data that identifies the entity, and obtaining one or more results of the query. [Reference numerals] (104) Disambiguation engine; (106) Speech recognition engine; (108) Keyword mapping engine; (110) Content recognition engine; (AA,FF) Utterance; (BB) Environmental data; (CC,GG) Content item data; (DD) Environmental data + Content type; (EE) Content type; (HH) Transcription

Description

Question Answer with Environment Context {ANSWERING QUESTIONS USING ENVIRONMENTAL CONTEXT}

This application is directed to US Provisional Application No. 61 / 698,934, filed September 10, 2012, 61 / 698,949, filed September 10, 2012, US Patent Application No. 13 / 626,439, filed September 25, 2012, Priority is given to US Patent Application No. 13 / 626,351, filed September 25, 2012, and US Patent Application No. 13 / 768,232, filed February 15, 2013, the entire contents of which are incorporated by reference. Included in the specification.

This disclosure relates to identifying results of a query based on natural language query and environmental information, and answers questions using environmental information (eg, context). It is to.

In general, a search query includes one or more terms that submit to the search engine when the user requests the search engine to execute the search. Among other approaches, the user may enter query terms of the search query by typing on the keyboard or by saying query terms to the microphone of the mobile device in the context of the voice query. Speech queries can be processed using speech recognition technology.

According to some innovative aspects of the subject matter described herein, environmental information (eg, ambient noise) can help the query processing system answer natural language queries. For example, a user may ask a question about a television program he is watching (eg, "Who is the hero in this movie?"). The user's mobile device detects user's utterance and environmental information, which may include soundtrack audio of the television program. The mobile computing device encodes speech and environmental information as waveform data, and provides the waveform data to a server-based computing environment.

The computing environment separates the utterance from the environmental data of the waveform data and then obtains a transcription for the utterance. The computing environment further identifies the environmental data and entity data related to the utterance, for example by identifying the name of the movie. From the notation and the entity data, the computing environment can then identify one or more results (eg, results in response to the user's question). In particular, the one or more results may include an answer to the user's question about “What actor is in the movie?” (Eg, actor's name). The computing environment may provide these results to the user of the mobile computing device.

Innovative aspects of the subject matter described herein may be implemented in a method, the method comprising: receiving audio data encoding speech and environmental data, obtaining an indication of the speech, an entity using the environmental data Identifying a query, submitting a query to a natural language query processing engine that includes at least a portion of the notation and data identifying the entity, and obtaining one or more results for the query.

Other embodiments of these aspects include computer programs encoded on corresponding systems, apparatuses, and computer storage devices, configured to perform the operations of the present invention.

These and other embodiments may optionally include one or more of the features described below, respectively. For example, it outputs a representation of at least one result. The entity may be identified further using the utterance. Create the query. Generating the query includes associating the notation with the data identifying the entity. The associating operation includes tagging the notation with the data identifying the entity. Associating further includes replacing a portion of the notation with the data identifying the entity. The replacing operation further includes replacing one or more words of the notation with the data identifying the entity. Receiving the environmental data further includes receiving environmental audio data, environmental image data, or both. Receiving the environmental audio data further includes receiving additional audio data that includes background noise.

According to some innovative aspects of the subject matter described herein, items of media content are identified based on environmental audio data and spoken natural language queries. For example, a user may ask a question about a television program he is watching, such as "What are we watching now?". The question may include keywords (eg, “watching”) that suggest that the question is about a television show and not about other forms of media content. The user's mobile device detects the user's speech and environmental data, which may include background audio of the television program. The mobile computing device encodes the utterance and the environmental data into waveform data and provides the waveform data to a server-based computing environment.

The computing environment separates the utterance from the environmental data of the waveform data and then processes the utterance to obtain a representation for the utterance. From the notation, the computing environment detects any content type-specific keywords (eg, keyword "watching"). The computing environment may then identify items of media content based on the environmental data, and select from the identified items specific items of multi-content that match a particular content type associated with the keywords. A computing environment provides the user of the mobile computing device with a representation of a particular item of multimedia content.

Innovative aspects of the subject matter described herein may be implemented in methods, which include (i) receiving audio data encoding a spoken natural language query and (ii) receiving environmental audio data, the spoken natural Obtaining a representation for a language query, determining a particular content type associated with one or more keywords in the representation, providing at least a portion of the environmental audio data to a content recognition engine, and by the content recognition engine And outputting a content item matching the specific content type.

Other embodiments of these aspects include computer programs encoded in corresponding systems, apparatuses, and computing storage devices and configured to perform the operations of the methods.

These and other embodiments may optionally include one or more of the features described below, respectively. For example, certain content types include movie content types, music content types, television show content types, audio podcast content types, book content types, artwork content types, trailer content types, video podguest content types, and the Internet. Video content type, or video game content type. Receiving the environmental audio data further includes receiving additional audio data including background noise. The background noise is associated with a particular content type. Receive additional environmental data, including video data or image data. The video data or the image data is associated with the specific content type. Providing at least a portion of the environmental audio data to the content recognition engine further includes providing the portion of the environmental audio data to an audio fingerprinting engine. The determining of the specific content type may include identifying, for each of a plurality of content types, at least one of the keywords mapped to at least one of the plurality of content types, using at least one database. It includes more. The plurality of content types includes the specific content type, and the mapping operation further includes an operation in which at least one of the keywords is mapped to the specific content type. Output data identifying the content item.

The features may include, for example, providing data identifying the specific content type to the content recognition engine, and identifying the content item receiving data identifying the content item from the content recognition engine. It includes more. Receiving two or more content recognition candidates from the content recognition system, and identifying the content item further include selecting a specific content recognition candidate based on the specific content type. Each of the two or more content recognition candidates is associated with a ranking score, and the method further includes adjusting a ranking score of the two or more content recognition candidates based on the specific content type. Rank the two or more content recognition candidates based on the adjusted ranking scores.

One or more details of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other potential features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.

1 depicts an example system for identifying content item data based on environmental audio data and spoken natural language queries.
2 depicts a flow chart for an example process for identifying content item data based on environmental audio data and natural language queries retrieved.
3A and 3B depict portions of an example system for identifying a content item.
4 depicts an example system for identifying media content items based on environmental image data and spoken natural language queries.
5 depicts a system for identifying one or more results based on environmental audio data and speech.
6 depicts a flowchart for an example process for identifying one or more results based on environmental data and utterances.
7 depicts a computer system and a mobile computer device that can be used to implement the techniques described herein.
Like reference symbols in the various drawings indicate like elements.

A computing environment that uses spoofed natural language queries to answer spoken natural language queries using the environment information as context can process the queries using a plurality of processes. In the example of some processes shown in FIGS. 1-4, the computing environment may identify the media content based on environmental information, such as ambient noises. In the example of the other processes shown in FIGS. 5 and 6, the computing environment is contextually uttered natural derived from environmental information, such as data identifying media content, to provide a more satisfactory answer to uttered natural language queries. You can increase the language query.

More specifically, FIG. 1 illustrates a system 100 for identifying content item data based on environmental audio data and spoken natural language queries. Briefly, system 100 may identify content item data that is based on environmental audio data and associates and matches a particular content type with a spoken natural language query. The system 100 includes a mobile computing device 102, a disambiguation engine 104, a speech recognition engine 106, a keyword mapping engine 108, and a content recognition engine 110. Mobile computing device 102 communicates with disambiguation engine 104 via one or more networks. Mobile device 110 may include a microphone, camera, or other detection mechanisms for detecting utterances from user 112 and / or environmental data associated with user 112.

In some examples, user 112 is watching a TV program. In the example shown, the user 112 wants to know who supervised the currently displayed TV program. In some examples, user 112 may not know the name of the TV program currently being displayed, and may ask, "Who has directed this show?" Mobile computing device 102 detects this utterance with environmental audio data associated with the environment of user 112.

In some examples, the environmental audio data associated with the environment of the user 112 may include background noise of the environment of the user 112. For example, the environmental audio data may include sounds of a TV program. In some examples, the environmental audio data associated with the currently displayed TV program may include audio of the currently displayed TV program (eg, a dialog of the currently displayed TV program, a soundtrack audio associated with the currently displayed TV program, and the like). It may include.

In some examples, mobile computing device 102 may detect environmental audio data after detecting an utterance, detect environmental audio data concurrently with detection of the utterance, or both. During operation (A), the mobile computing device 102 processes the detected speech and environmental audio data to generate waveform data 114 representing the detected speech and environmental audio data and generate the waveform data 114 (eg, For example, via the network) to the disambiguation engine 104. In some examples, environmental audio data is streamed from mobile computing device 110.

Disambiguation engine 104 receives waveform data 114 from mobile computing device 102. During operation (B), disambiguation engine 104 processes waveform data 114, including separation (or extraction) of speech from other portions of waveform data 114, and processes speech (eg, via a network). ) Is transmitted to the speech recognition engine 106. For example, the disambiguation engine 104 separates the utterances ("Who oversaw this show?") From the background noise of the user 112's environment (eg, the audio of the TV program currently being displayed).

In some examples, the disambiguation engine 104 may use speech to enable separation of speech from background noise by identifying a portion of waveform data 114 that includes voice activity or voice activity associated with a user of computing device 102. Use a detector. In some examples, the utterance is related to a query (eg, a query related to the TV currently being displayed). In some examples, waveform data 114 includes detected utterances. Correspondingly, disambiguation engine 104 may request environmental audio data related to speech from mobile computing device 102.

The speech recognition engine 106 receives a portion of the waveform data 114 corresponding to the speech from the disambiguation engine 104. During operation (C), speech recognition engine 106 obtains a transcription of the speech and provides that representation to keyword mapping engine 108. Specifically, the speech recognition engine 106 processes the speech received from the speech recognition engine 106. In some examples, the processing of the speech by the speech recognition engine 106 includes generating a representation of the speech. Generating the notation of a utterance includes marking the utterance with data related to text or text. In other words, the speech recognition system 106 may provide a verbal representation of the verbal form of the utterance.

For example, the speech recognition system 106 notifies the utterance and generates the notation "Who oversaw the show?" In other embodiments, the speech recognition system 106 provides notation of two or more utterances. For example, the speech recognition system 106 indicates a utterance, "Who was in charge of this show?" And "Who oversaw this shoe?"

The keyword mapping engine 108 receives the notation from the speech recognition engine 106. During operation (D), keyword mapping engine 108 identifies one or more keywords in the notation associated with a particular content type and provides the specific content type to disambiguation engine 104. In some embodiments, one or more content types are 'movie', 'music', 'TV show', 'audio podcast', 'image', 'artwork' , “Book”, “magazine”, “trailer”, “video podcast”, “internet video”, or “video game”.

For example, the keyword mapping engine 108 identifies the keyword "directed" from the notation "who directed this show?". The keyword "supervised" is associated with the "TV show" content type. In some embodiments, the notational keyword identified by keyword mapping engine 108 is associated with two or more content types. For example, the keyword "directed" is associated with "TV show" and "movie" content types.

In some embodiments, keyword mapping engine 108 identifies two or more keywords in the notation associated with a particular content type. For example, keyword mapping engine 108 identifies the keywords "overseeed" and "show" associated with a particular content type. In some embodiments, two or more keywords identified are associated with the same content type. For example, the identified keywords "supervised" and "show" are both associated with a 'TV show' content type. In some embodiments, two or more keywords identified are associated with different content types. For example, the identified keyword "supervised" is associated with the "movie" content type, and the identified keyword "show" is associated with the "TV show" content type. The keyword mapping engine 108 sends the particular content type to the disambiguation engine 108 (eg, via a network).

In some embodiments, the keyword mapping engine 108 uses one or more databases to map at least one of the keywords to at least one of the plurality of content types for each of the plurality of content types. Identifies one or more keywords in the notation associated with. Specifically, keyword mapping engine 108 includes (or communicates with) one database (or a plurality of databases). The database includes or is associated with a mapping between keywords and content types. In particular, the database is responsible for linking (eg, mapping) between keywords and content types, such as allowing the keyword mapping engine 108 to identify one or more keywords in the notation associated with particular content types. to provide.

In some embodiments, one or more mappings between keywords and content types are unidirectional (eg, one-way) mappings (ie, keywords to content types). Mapping). In some embodiments, one or more mappings between keywords and content types are bidirectional (eg, one-way) mapping (ie, mapping from keywords to content types). And mapping from content types to keywords. In some embodiments, one or more databases map one or more keywords to two or more content types.

For example, the keyword mapping engine 108 uses one or more databases to map the keyword "overwatched" to "movie" and "TV show" content types. In some embodiments, the mapping between keywords and content types includes a mapping between a plurality of varying versions (ie, word families) and content types for a root keyword. Other forms of keywords may include other grammatical categories such as tense (eg, past, present, future) and parts of speech (eg, nouns, verbs). For example, a database may be associated with one or more content types of the word family of "root" root words, such as "directors", "direction", or "directed". May include a mapping.

Disambiguation engine 104 receives data from keyword mapping engine 108 that identifies a particular content type associated with the representation of the utterance. Moreover, as discussed above, disambiguation engine 104 receives waveform data 114, including environmental audio data associated with speech, from mobile computing device 102. During operation (E), the disambiguation engine 104 then provides environmental audio data and the specific content type to the content recognition engine 110.

For example, the disambiguation engine 104 may display a currently displayed TV that includes audio of the currently displayed TV program (eg, a dialog of the currently displayed TV program, a soundtrack audio of the currently displayed TV program, and the like). Sends specific content type (eg, 'TV show' content type) of the environmental audio data related to the program and the representation of the utterance to the content recognition engine 110.

In some embodiments, disambiguation engine 104 provides a portion of environmental audio data to content recognition engine 110. In some examples, the portion of the environmental audio data may include background noise detected by mobile computing device 102 after detecting an utterance. In some examples, the portion of the environmental audio data may include background noise detected by mobile computing device 102 concurrently with detection of speech.

In some embodiments, the background noise (of waveform data 114) is associated with a particular content type associated with the keyword of the notation. For example, the keyword "who supervised this show?" Refers to the "TV show" content type and includes background noise (e.g., environmental audio data related to the TV program currently being displayed). ) Is also associated with the 'TV Show' content type.

The content recognition engine 110 receives environmental audio data and specific content types from the disambiguation engine 104. During operation (F), the content recognition engine 110 identifies the content item data based on the environmental audio data and matches the specific content type, and provides the content item data to the disambiguation engine 104. Specifically, the content recognition engine 110 properly processes the environmental audio data to identify content item data associated with the environmental audio data (eg, the name of a TV show, the name of a song, etc.). Moreover, content recognition engine 110 matches the identified content item data with a particular content type (eg, the content type of the notation of the utterance). The content recognition engine 110 sends the identified content item data to the disambiguation engine 104 (eg, via a network).

For example, content recognition engine 110 is based on environmental audio data related to the currently displayed TV program, and further identifies content item data that matches the 'TV show' content type. To this end, the content recognition engine 110 may, depending on the portion of the environmental audio data received by the content recognition engine 110, a content item based on a dialogue of the currently displayed TV program, or a soundtrack audio associated with the currently displayed TV program. Identifies the data.

In some embodiments, content recognition engine 110 is an audio fingerprinting engine that uses a content fingerprint that uses wavelets to identify content item data. In detail, the content recognition engine 110 converts the waveform data 114 into a spectrogram. The content recognition engine 110 extracts spectral images from the spectrogram. The spectral images may be represented by wavelets. For each of the spectral images extracted from the spectrogram, the content recognition engine 110 extracts "top" wavelets based on the size of each of the wavelets. For each spectral image, the content recognition engine 9110 calculates the wavelet signature of the image. In some examples, the wavelet signature is in truncated, quantized form of wavelet decomposition of the image.

For example, in order to describe an m x n image with a wavelet, the m x n wavelets are returned without compression. Moreover, content recognition engine 110 uses a subset of wavelets that characterize most of the song. Specifically, when t << m x n, t "top" wavelets (by size) are selected. Moreover, the content recognition engine 110 uses the MinHash to compute the conciseness of the sparse wavelet-vector described above using, for example, MinHash to compute sub-fingerprints for sparse bit vectors. Create an expression.

In some examples, when the environmental audio data includes soundtrack audio associated with at least the currently displayed TV program, the content recognition engine 110 is based on the soundtrack audio associated with the currently displayed TV program and is also a 'TV show'. Identify content item data that matches the content type. Thus, in some examples, content recognition engine 110 identifies content item data related to the name of the TV program currently being displayed. For example, content recognition engine 110 may be associated with a particular content item (eg, a particular TV show) associated with a theme song (eg, soundtrack audio), and the particular content item (eg, the specific). TV show) may be determined to match the specific content type (eg, 'TV show' content type). Accordingly, content recognition engine 110 is associated with the particular content item (e.g., the currently displayed TV program) based on environmental audio data (e.g., soundtrack audio) and in addition to the particular content type (e.g., For example, data (eg, the name of the particular TV show) that matches the 'TV show' content type may be identified.

Disambiguation engine 104 receives identified content item data from content recognition engine 110. In operation (G), disambiguation engine 104 then provides the identified content item data to mobile computing device 102. For example, disambiguation engine 104 sends identified content item data related to the currently displayed TV program (eg, the name of the currently displayed TV program) to mobile computing device 102.

In some examples, one or more of mobile computing device 102, disambiguation engine 104, speech recognition engine 106, keyword mapping engine 108, and content recognition engine 110 may be mobile computing device 102. ), Disambiguation engine 104, speech recognition engine 106, keyword mapping engine 108, and a subset (or each) of content recognition engine 110. In some embodiments, one or more of disambiguation engine 104, speech recognition engine 106, keyword mapping engine 108, and content recognition engine 110 may be one or more computing servers, distributed computing system. Or one or more computing devices, such as one server farm or cluster.

In some embodiments, as described above, environmental audio data may be streamed from the mobile computing device 110 to the disambiguation engine 104. When the environmental audio data is streamed, the above-described processing (e.g., operations (A)-(H)) is performed when the environmental audio data is received by the disambiguation engine 104 (i.e., is performed gradually). ). In other words, when each portion of the environmental audio data is received (eg, streamed) by the disambiguation engine 104, actions (A)-(H) are performed repeatedly until the content item data is identified. do.

2 shows a flow diagram of an example process 200 for identifying content item data based on environmental audio data and spoken natural language queries. Example process 200 may be executed by one or more computing devices. For example, mobile computing device 102, disambiguation engine 104, speech recognition engine 106, keyword mapping engine 108, and / or content recognition engine 110 execute the example process 200. Can be used to

Audio data and environmental audio data encoding the spoken natural language query are received (202). For example, the visualization engine 104 receives waveform data 114 from the mobile computing device 102. Waveform data 114 includes a user's spoken natural query (eg, "Who directed this show?") And environmental audio data (eg, audio of the currently displayed TV program). The disambiguation engine 104 separates the spoken natural language query ("Who oversaw this show?") From the background noise of the user 112's environment (eg, the audio of the currently displayed TV program).

A representation of the natural language query is obtained (204). For example, the speech recognition system 106 writes a natural language query to generate a notation of the natural language query (eg, “Who oversaw this show?”).

A particular content type associated with one or more keywords in the notation is determined 206. For example, the keyword mapping engine 108 may have one or more keywords within a notation associated with a particular content type (e.g., "TV show" content type) (e.g. "Who oversaw this show?"). (Eg, "supervised"). In some embodiments, keyword mapping engine 108 includes one in the notation using one or more databases that map at least one of the keywords to at least one of the plurality of content types for each of the plurality of content types. Identify more or more keywords. The database provides a link (eg, a mapping) between keywords (eg, “overwatched”) and content types (eg, a “TV show” content type).

At least some of the environmental audio data is provided to the content recognition engine 208. For example, the disambiguation engine 104 provides the content recognition engine 110 with at least a portion of the environmental audio data (eg, audio of the currently displayed TV program) encoded by the waveform data 114. In some examples, disambiguation engine 104 may also be content aware of a particular content type (eg, a 'TV show' content type) associated with one or more keywords (eg, "supervised") in the notation. It is provided to the engine 110.

The content item output by the content recognition engine and matching the specific content type is identified (210). For example, content recognition engine 110 may be content that is based on environmental audio data (eg, audio of a currently displayed TV program) and that matches a particular content type (eg, a 'TV show' content type). Identifies item or content item data.

3A and 3B illustrate portions 300a and 300b of the system for identifying content item data, respectively. In particular, FIGS. 3A and 3B show disambiguation engines 304a and 304b, respectively; And content recognition engines 310a and 310b, respectively. Disambiguation engines 304a, 304b are similar to disambiguation engine 104 of system 100 shown in FIG. 1; The content recognition engines 310a, 310b are similar to the content recognition engine 110 of the system 100 shown in FIG. 1.

3A shows a portion 300a that includes a content recognition engine 310a. The content recognition engine 310a may identify content items that match a particular content type based on the environmental data. In other words, the content recognition engine 310a may properly process the environmental data based on the environmental data to identify the content item, and further select one or more identified content item data such that the selected content item data matches a particular content type. have.

Specifically, during operation (A), disambiguation engine 304a provides environmental data and specific content type to content recognition engine 310a. In some embodiments, disambiguation engine 304a provides a portion of environmental data to content recognition engine 310a.

The content recognition engine 310a receives environmental data and specific content types from the disambiguation engine 304a. During operation (B), the content recognition engine 310a then identifies the content item data that matches the particular content type based on the environmental data and provides the identified content item data to the disambiguation engine 304a. Specifically, the content recognition engine 310a identifies the content item data (eg, the name of the TV show, the song title, etc.) based on the environmental data. The content recognition engine 310a then selects one or more of the identified content item data that matches the particular content type. In other words, the content recognition engine 310a filters the identified content item data based on the specific content type. The content recognition engine 310a sends the identified content item data (eg, via a network) to the disambiguation engine 304a.

In some examples, as described above with respect to FIG. 1, if the environmental data includes soundtrack audio associated with at least the currently displayed TV program, the content recognition engine 310a may be configured to include the currently displayed TV program. Identifies content item data based on the associated soundtrack audio. The content recognition engine 310a then filters the identified content item data based on the “TV Show” content type. For example, the content recognition engine 310a identifies the “theme song names” and “TV show names” associated with the soundtrack audio. The content recognition engine 310a then filters the identified content item data such that the identified content item data also matches the TV show content type. For example, the content recognition engine 310a selects "TV show name" identification data and transmits "TV show name" identification data to the disambiguation engine 304a.

In some examples, content recognition engine 310a selects a corpus (or index) based on the content type (eg, the “TV Show Content Type”). In detail, the content recognition engine 310a may have access to the first index associated with the “TV Show” content type and the second index associated with the “Movie” content type. The content recognition engine 310a selects the first index appropriately based on the TV show content type. Thus, by selecting the first index (and not selecting the second index), the content recognition engine 310a can more effectively identify content item data (eg, the name of the TV show).

Disambiguation engine 304a receives content item data from content recognition engine 310a. For example, the disambiguation engine 304a receives &quot; TV show name &quot; identification data from the content recognition engine 310a. Disambiguation engine 304a then provides identification data to a third party (eg, mobile computing device 102 of FIG. 1) during operation (C). For example, the disambiguation engine 304a provides the data “TV Show Name” identification data to a third party.

3B illustrates a portion 300b that includes a content recognition engine 310b. The content recognition engine 310b may identify content item data based on the environment data. In other words, the content recognition engine 310b properly processes the environment data to identify the content item data based on the environment data, and provides the content item data to the disambiguation engine 304b. The content recognition engine 310b selects at least one of the identified content item data so that the selected content item data matches the specific content type.

Specifically, during operation (A), disambiguation engine 304b provides environmental data to content recognition engine 310b. In some embodiments, disambiguation engine 304b provides a portion of environmental data to content recognition engine 310b.

The content recognition engine 310b receives environmental data from the disambiguation engine 304b. Thereafter, during operation (B), the content recognition engine 310b identifies the content item data based on the environmental data and provides the identified content item data to the disambiguation engine 304b. Specifically, content recognition engine 310b identifies content item data associated with two or more content items (eg, the name of a TV show, the title of a song, etc.) based on the environmental data. The content recognition engine 310b sends to the disambiguation engine 304b two or more candidates representing the identified content item data (eg, via a network).

In some examples, as described above with respect to FIG. 1, if the environmental data includes at least soundtrack audio associated with the currently displayed TV program, the content recognition engine 310b may include a soundtrack associated with the currently displayed TV program. Identifies content item data associated with two or more content items based on audio. For example, the content recognition engine 310b identifies the “theme song names” and “TV show names” associated with the soundtrack audio and clarifies the “theme song names” and “TV show names” identification data (304b). To send.

Disambiguation engine 304b receives two or more candidates from content recognition engine 310b. For example, disambiguation engine 304b receives “theme song name” and “TV show name” candidates from content recognition engine 310b. Thereafter, during operation (C), the disambiguation engine 304b selects one of the two or more candidates based on the particular content type and selects the selected candidate from a third party (eg, the mobile computing device 102 of FIG. 1). To provide. Specifically, as described above with respect to FIG. 1, disambiguation engine 304b may receive a particular content type (eg, associated with a utterance) in advance. Disambiguation engine 304b selects a particular candidate from two or more candidates based on the particular content type. Specifically, the disambiguation engine 304b selects a particular candidate that matches a particular content type of the two or more candidates. For example, the disambiguation engine 304b selects the "TV show name" candidate because the "TV show name" candidate matches the "TV show" content type.

In some embodiments, two or more candidates from content recognition engine 310b are associated with a ranking score. The ranking score may be associated with any score metric determined by the disambiguation engine 304b. Disambiguation engine 304b may also adjust the ranking scores of two or more candidates based on the particular content type. Specifically, disambiguation engine 304b may increase the ranking score of one or more candidates when each candidate matches a particular content type. For example, the ranking score of the candidate “TV show name” may increase as it matches the “TV show” content type. In addition, disambiguation engine 304b may reduce the ranking score of one or more candidates if each candidate does not match a particular content type. For example, the ranking score of the candidate “Theme Song Name” may decrease as it does not match the “TV Show” content type.

In some embodiments, two or more candidates may be ranked based on ranking scores each adjusted by disambiguation engine 304b. For example, the disambiguation engine 304b is a “TV show name” candidate as the “TV show name” candidate has a higher adjusted ranking score compared to the adjusted ranking score of the “theme song name” candidate. Can be ranked higher than the candidate for the theme song name. In some examples, disambiguation engine 304b selects the highest ranked candidate (eg, having the highest adjusted ranking score).

4 illustrates a system 400 for identifying content item data based on environmental image data and spoken natural language queries. In sum, the system 400 may identify content item data that matches a particular content type associated with the spoken natural language query based on the environmental data. System 400 includes a mobile computing device 102, disambiguation engine 104, speech recognition engine 106, keyword mapping engine 108, and content recognition engine 110 of system 100 shown in FIG. 1. Similarly, each includes a mobile computing device 402, a speech recognition engine 406, a keyword mapping engine 408, and a content recognition engine 410.

In some examples, user 112 is looking at the CD album cover of the soundtrack of the movie. In the example shown, the user 112 wants to know what songs are included in the soundtrack. In some examples, user 112 may not know the name of the movie soundtrack, and thus may ask, “What song is here?” Or What song is played in the movie? . The mobile computing device 402 senses this utterance as well as environmental image data associated with the environment of the user 112.

In some examples, the environment image data associated with the environment of the user 112 may include image data of the environment of the user 112. For example, the environmental image data includes an image of a CD album cover depicting images associated with a movie (eg, an image of a movie poster of an associated movie). In some examples, mobile computing device 402 utilizes a camera of mobile computing device 402 to capture an image (or video) of a CD album cover to sense environmental image data.

During operation (A), the mobile computing device 402 processes the sensed speech to generate waveform data 414 representing the sensed speech and transmit the waveform data 414 and environmental image data (eg, via a network). ) To the disambiguation engine 404.

During operation (B), the disambiguation engine 404 receives waveform data 414 and environmental image data from the mobile computing device 402. The disambiguation engine 404 processes the waveform data 414 and sends an utterance to the speech recognition engine 406 (eg, via a network). In some examples, the utterance is associated with a query (eg, a query associated with a movie soundtrack).

The speech recognition engine 406 receives the speech from the disambiguation engine 404. During operation (C), speech recognition engine 406 obtains a transcription of the speech and provides the representation to keyword mapping engine 408. Specifically, the speech recognition engine 406 processes the speech received from the speech recognition engine 406 by generating a representation of the speech.

For example, the speech recognition engine 406 transcribes the utterance to produce the notation "What song is here?" In some embodiments, speech recognition engine 406 provides two or more notations of speech. For example, the speech recognition engine 406 textifies an utterance, what songs are on this? And what sinks are on this? Generate notations

The keyword mapping engine 408 receives the notation from the speech recognition engine 406. During operation (D), the keyword mapping engine 408 identifies one or more keywords in the notation associated with the particular content type and provides the specific content type to the disambiguation engine 404.

For example, the keyword mapping engine 408 identifies the keyword "song" from the notation "What song is here?". The keyword “song” is associated with a “music” content type. In some embodiments, the notational keyword identified by the keyword mapping engine 408 is associated with two or more content types. For example, the keyword “song” is associated with “music” and “singer” content types. The keyword mapping engine 408 sends the specific content type (eg, over a network) to the disambiguation engine 408.

In some embodiments, similar to the foregoing, the keyword mapping engine 408 may, for each of the plurality of content types, map one or more keywords to at least one of the plurality of content types and the one or more keywords. Databases are used to identify one or more keywords associated with a particular content type in the notation. For example, the keyword mapping engine 408 uses one or more databases that map “songs” to “music” and “singer” content types.

Disambiguation engine 404 receives a particular content type associated with the representation of the utterance from keyword mapping engine 408. In addition, as described above, the disambiguation engine 404 receives environment image data associated with the utterance. During operation (E), the disambiguation engine 404 provides the environment recognition data and the specific content type to the content recognition engine 410.

For example, the disambiguation engine 404 may identify environmental image data (eg, an image of a movie poster CD album cover) associated with a movie soundtrack and a particular content type (eg, “music” content type) in the notation of speech. Send to the content recognition engine 410.

The content recognition engine 410 receives environment image data and a specific content type from the disambiguation engine 404. During operation (F), the content recognition engine 410 then identifies the content item data based on the environmental image data and matches the particular content type and provides the identified content item data to the disambiguation engine 404. Specifically, the content recognition engine 410 processes the environment image data appropriately to identify content item data (eg, the name of the content item). In addition, the content recognition engine 410 matches the identified content item with a particular content type (eg, content type in spoken notation). The content recognition engine 408 sends the identified content item data (eg, via a network) to the disambiguation engine 408.

For example, the content recognition engine 410 is based on environmental image data associated with the image of the movie poster CD album cover and further identifies data that matches the "Music" content type.

In some examples, where the environmental image data includes at least a movie poster associated with a CD album cover, content recognition engine 410 is based on the movie poster associated with the CD album cover and also matches the “music” content type. Identifies item data. Thus, in some examples, content recognition engine 410 identifies content item data related to the title of the movie soundtrack. For example, the content recognition engine 410 may have certain content items (eg, certain movie soundtracks) associated with movie posters, and certain content items (eg, certain movie soundtracks) specific content types (eg For example, it can be determined that the "music" content type). Thus, the content recognition engine 410 is based on environmental image data (e.g., an image of a CD album cover) and further matches a particular content item (e.g., "music" content type), e.g. For example, data related to a particular movie soundtrack (eg, a title of a particular movie soundtrack) may be identified.

Disambiguation engine 404 receives identified content item data from content recognition engine 410. In operation G, the disambiguation engine 404 then provides the identified content item data to the mobile computing device 402. For example, the disambiguation engine 404 sends the identified content item data (eg, the title of the movie soundtrack) associated with the movie soundtrack to the mobile computing device 402.

As mentioned above, FIGS. 1-4 illustrate various exemplary processes within a computing environment that can identify media content (or other content) based on environmental information such as ambient noise. Other processes for identifying content can also be used. In general, FIGS. 5 and 6 illustrate a spoken natural language query for a context in which a computing environment, such as data identifying media content, is used to provide a more satisfactory answer to a spoken natural language query. An example process that can be increased is shown.

In more detail, FIG. 5 shows a system 500 for identifying one or more results based on environmental audio and speech. In some examples, the one or more results may represent one or more answers to a natural language query. System 500 includes mobile computing device 502, coordination engine 504, speech recognition engine 506, content identification engine 508, and natural language query processing engine 510. The mobile computing device 502 communicates with the coordination engine 504 via one or more networks. Mobile device 510 may include a microphone, camera, or other sensing mechanism for sensing utterance from environmental data associated with user 512 and / or user 512.

Similar to the system 100 of FIG. 1, the user 512 is watching a television program. In the example shown, the user 512 wants to know who oversaw the television program (e.g., entity) that is currently playing. In some instances, the user 512 may not know the name of the television program currently playing and thus may ask the question "Who has directed this show?" The mobile computing device 502 senses this utterance, as well as environmental data associated with the user 512 environment.

In some examples, the environmental data associated with the environment of user 512 may include background noise of the environment of user 512. For example, environmental data includes television program (eg, entity) sound. In some examples, the environmental data associated with the currently displayed television program may include audio of the currently displayed television program (eg, the dialogue of the currently displayed television program, the soundtrack audio associated with the current display television program, and the like). Can be. In some examples, the environmental data may include environmental audio data, environmental image data, or both. In some examples, the mobile computing device 502 detects environmental audio data after detecting the utterance; Detect utterance while simultaneously detecting environmental audio data; Or both. During operation (A), the mobile computing device 502 may generate the sensed speech and environmental data to generate waveform data 514 representing the sensed speech and sensed environmental audio data (eg, sound of a television program). Process and transmit waveform data 514 to coordination engine 504 (eg, over a network).

Coordination engine 504 receives waveform data 514 from mobile computing device 502. During operation (B), the coordination engine 504 processes the waveform data 514 and includes waveform data corresponding to the waveform, including separating (or extracting) utterances from other portions of the waveform data 514. Portion of 514 is sent (eg, over a network) to speech recognition engine 506. For example, the coordination engine 504 separates the utterance ("Who oversaw this show?") From the background noise (eg, audio of the currently displayed television program) of the user 512 environment. In some examples, coordination engine 504 may utilize a voice detector to identify a portion of waveform data 514 that includes voice activity to facilitate separation of speech from background noise. Can be. In some instances, the utterance is associated with a query (eg, a query related to the currently displayed television program).

The speech recognition engine 506 receives a portion of the waveform data 514 corresponding to the speech from the coordination engine 504. During operation (C), the coordination engine 506 obtains a notation of speech and provides the notation to the coordination engine 504. In particular, the speech recognition engine 506 appropriately processes a portion of the waveform data 514 corresponding to the speech received from the coordination engine 504. In some examples, processing the portion of the waveform data 514 corresponding to the speech by the speech recognition engine 506 includes generating a representation of the speech. Generating the representation of the utterance may include marking the utterance as text or text-related data. In other words, the speech recognition engine 506 may provide a language representation of the written form of the utterance.

For example, the speech recognition engine 506 transcribes the utterance to produce the notation "Who oversaw the show?" In some embodiments, speech recognition engine 506 provides two or more notations of the speech. For example, speech recognition engine 506 may generate the notation "Who directed this show?" And "Who directed this shoe?" The utterance can be indicated.

Coordination engine 504 receives an indication of speech from speech recognition engine 506. Further, as mentioned above, the coordination engine 504 receives waveform data 514 from the mobile computing device 502, including environmental audio data associated with speech. Coordination engine 504 now uses the environmental data to identify an entity. In particular, the coordination engine 504 obtains data identifying the entity from the content identification engine 508. To do so, during operation (D), the coordination engine 504 provides (eg, over a network) the content identification engine 508 with a portion of waveform data 514 corresponding to environmental data and speech.

For example, the coordination engine 504 may display a currently displayed television that includes audio of the currently displayed television program (eg, a dialog of the currently displayed television program, a soundtrack audio associated with the currently displayed television program, etc.). The portion of waveform 514 corresponding to the environmental data and utterances ("Who oversaw this show?") Associated with the program (eg, entity) is sent to content identification engine 508.

In some embodiments, coordination engine 504 provides a portion of environmental data to content identification engine 508. In some examples, some of the environmental data may include background noise sensed by the mobile computing device 502 after speech detection. In some examples, some of the environmental data may include background noise sensed by mobile computing device 502 concurrently with speech detection.

The content identification engine 508 receives a portion of the waveform 514 corresponding to environmental data and speech from the coordination engine 504. During operation (E), content identification engine 508 identifies data identifying an entity (eg, content item data) based on environmental data and utterances, and sends the data identifying entity to coordination engine 504. Provide (eg over a network). In particular, the content identification engine 508 is configured to identify data that identifies an entity (eg, content item data) associated with the environment data (eg, the name of a television show, the name of a song, etc.). A portion of the waveform 514 corresponding to the utterance is appropriately processed.

For example, content identification engine 508 processes environmental audio data to identify content item data associated with the currently displayed television program. In some embodiments, content identification engine 508 is system 100 of FIG. 1.

Coordination engine 504 receives data identifying an entity (eg, content item data) from content identification engine 508. Further, as described above, the coordination engine 504 receives the notation from the speech recognition engine 506. During operation (F), the coordination engine 504 now provides data to the natural language query processing engine 510 (eg, over a network) that identifies the entity and the query that includes the notation. For example, coordination engine 504 provides query and content item data ('TV show name') to natural language query processing engine 510 that includes a notation of utterance ("Who directed this show?"). do.

In some examples, coordination engine 504 generates a query. In some examples, coordination engine 504 obtains a query (eg, from a third server). For example, the coordination engine 504 submits a representation of the utterance and data identifying the entity to a third server and returns a query based on the representation and the data identifying the entity.

In some embodiments, generating the query by the coordination engine 504 may include associating a notation of the utterance with data identifying an entity (eg, content item data). In some examples, associating the notation of the utterance with content item data may include tagging the data identifying the entity in the notation. For example, the coordination engine 504 may tag the television show name or other identifying information (eg, identification (ID) number) associated with the content item data in the notation "Who oversaw the show?" have. In some instances, associating the notation of the utterance with data identifying the entity includes replacing a portion of the notation with data identifying the entity. For example, the coordination engine 504 may replace a portion of the notation "Who oversaw the show?" With data identifying 'TV show name' or 'TV show name'. In some examples, replacing a portion of the notation with data identifying the entity may include replacing one or more words of the notation of the utterance with data identifying the entity. For example, the coordination engine 504 may replace data identifying 'TV show name' or 'TV show name' in the notation "Who oversaw the show?" For example, the substitution may include "Who directed 'television show name'" or "Who directed 'ID number')" May result.

Natural language query processing engine 510 receives from coordination engine 504 data identifying the query and entity (eg, content item data) that includes the notation. During operation (G), the natural language query processing engine 510 properly processes the query and provides one or more results to the coordination engine 504 (eg, over a network) based on the processing. In other words, the coordination engine 510 obtains one or more results for the query (eg, from the natural language query processing engine 510).

In particular, the natural language query processing engine 510 obtains information resources (notation of speech and content item data) related to the query (from the collection of information resources). In some examples, natural language query processing engine 510 matches the query against database information (eg, text document, image, audio, video, etc.) and how well each entity in the database matches the query. The score is calculated. The natural language query processing engine 510 identifies one or more results based on the matched entities (eg, those with scores above the critical score).

For example, the natural language query processing engine 510 receives a query that includes the notation "Who directed this show?" And a "TV show name" (or other identifying information). The natural language query processing engine 510 matches the query against database information and provides one or more results that match the query. The natural language query processing engine 510 calculates the score of each matched entity.

Coordination engine 504 receives one or more results from natural language query processing engine 510. In operation H, the coordination engine 504 now provides the one or more results to the mobile computing device 502 (eg, via a network). For example, the coordination engine 504 sends one or more results (eg, the name of the director of the television show) to the mobile computing device 502.

In some examples, one or more mobile computing device 502, coordination engine 504, speech recognition engine 506, content identification engine 508, and natural language query processing engine 510 may be mobile computing device 502. , A coordination engine 504, a speech recognition engine 506, a content identification engine 508, and a subset (or each) of the natural language query processing engine 510. In some embodiments, one or more coordination engine 504, speech recognition engine 506, content identification engine 508, and natural language query processing engine 510 may be one or more computing servers, distributed computing systems, or server farms. It may be implemented by one or more computing devices, such as server farms or clusters.

6 shows a flowchart of an example process 600 for identifying one or more results based on environmental data and speech. The example process 600 can be executed using one or more computing devices. For example, mobile computing device 502, coordination engine 504, speech recognition engine 506, content identification engine 508, and / or natural language query processing engine 510 execute example process 600. It can be used to.

Audio data encoding utterance and environmental data is received (602). For example, coordination engine 504 receives waveform data 514 from mobile computing device 502. Waveform data 514 includes a user's speech (eg, “Who directed the show?”) And environmental data (eg, audio of the currently displayed television program). In some examples, receiving environmental data may include receiving environmental audio data, environmental image data, or both. In some examples, receiving environmental data includes receiving additional audio data that includes background noise.

A notation of speech is obtained (604). For example, the coordination engine 504 uses the speech recognition engine 506 to obtain a representation of the speech. Speech recognition engine 506 transcribes the utterance to produce a representation of the utterance (eg, "Who has directed this show?").

The entity is identified 606 using the environmental data. For example, the coordination engine 504 uses the content identification engine 508 to obtain data identifying the entity. The content identification engine 508 uses environment data (e.g., to identify data that identifies an entity (e.g., content item data) associated with environmental data (e.g., the name of a television show, the title of a song, etc.). For example, environmental audio data associated with the displayed television program can be properly processed. In some examples, content identification engine 508 may further process waveform 514 corresponding to the utterance (simultaneously or following processing of environmental data) to identify the entity.

In some examples, coordination engine 504 generates a query. In some examples, the step in which the query is generated by the coordination engine 504 may include associating the notation of the utterance with data identifying the entity. In some examples, associating the notation of the utterance with the content item data may include replacing a portion of the notation with data identifying the entity. In some examples, replacing a portion of the notation with data identifying the entity may include replacing one or more words of the notation of the utterance with data identifying the entity.

The query is submitted to the natural language processing engine (608). For example, coordination engine 504 submits a query to natural language processing engine 510. The query may include data identifying at least a portion of the notation and an entity (eg, content item data). For example, coordination engine 504 provides query and content item data ('TV show name') to natural language query processing engine 510 that includes a notation of utterance ("Who directed this show?"). do.

One or more results for the query are obtained (610). For example, the coordination engine obtains one or more results for the query (eg, the name of the director of the television show) from the natural language query processing engine 510. In some examples, coordination engine 504 now provides the one or more results to mobile computing device 502.

7 illustrates one example of a generic computing device 700 and a generic mobile computing device 750 in which the techniques described herein may be employed. Computing device 700 is used to represent various forms of digital computers, such as laptops, desktops, workstations, PDAs, servers, blade servers, mainframes, and other suitable computers. Mobile computing device 750 is used to represent various forms of mobile devices such as PDAs, cell phones, smartphones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and do not imply limitations on the embodiments of the technology described or claimed herein.

Computing device 700 stores with processor 702, memory 704, storage device 706, high speed interface 708 and high speed expansion port 710, and low speed bus 714 that connect to memory 704. A low speed interface 712 that connects to the device 706. Each component 702, 704, 706, 708, 710, and 512 is connected to each other using a variety of buses and may be mounted in a general motherboard or in other ways as appropriate. The processor 702 may process instructions for execution within the computing device 700, which may include graphical information for the GUI on an external input / output device, such as a display 716 connected to the high speed interface 708. For display, instructions stored in memory 704 or storage device 706 are included. In other embodiments, multiple processors and / or multiple busses may be used with multiple memories and memory types, where appropriate. In addition, multiple computing devices 700 may be connected in a form (e.g., a server bank, a group of blade servers, or a multiprocessor system) in which each device provides a portion of the required operation.

Memory 704 stores information in computing device 700. In one embodiment, memory 704 is a volatile memory unit or units. In yet another embodiment, the memory 704 is a nonvolatile memory unit or units. In addition, the memory 704 may be another type of computer readable medium, such as a magnetic or optical disk.

The storage device 706 may provide mass storage for the computing device 700. In one embodiment, storage device 706 is in a floppy disk device, hard disk device, optical disk device, or tape device, flash memory or other similar solid state memory device, or storage area network or other configuration. It may be a device array including a device. The computer program product may be tangibly embodied in an information carrier. In addition, the computer program product, when executed, may include instructions to perform one or more of the methods as described above. The information carrier is a computer or machine readable medium such as memory 704, storage device 706, memory on processor 702.

The low speed controller 712 manages lower bandwidth-intensive operations, while the high speed controller 708 manages band-intensive operations for the computing device 700. The arrangement of these functions is merely exemplary. In one embodiment, the high speed controller 708 is connected to a memory 704, a display 716 (eg, via a graphics processor or accelerator) and capable of accommodating various expansion cards (not shown). Is connected to an expansion port 710. In some embodiments, slow control 712 is coupled to storage device 706 and slow expansion port 714. A slow expansion port, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet), may be connected to one or more input / output devices such as a keyboard, pointing device, scanner, To a networking device such as a switch or router.

The computing device 700 may be implemented in a plurality of different forms, as shown in the figures. For example, computing device 700 may be implemented in standard server 720 or may be implemented multiple times in a group of such servers. In addition, computing device 700 may be implemented as part of a rack server system 724. In addition, computing device 700 may be implemented in a personal computer, such as laptop computer 722. Optionally, components from computing device 700 may be combined with other components in a mobile device (not shown), such as device 750. Each of these devices includes one or more computing devices 700, 750, and the entire system may be comprised of multiple computing devices 700, 750 that communicate with each other.

Computing device 750 includes a processor 752, a memory 764, an input / output device such as a display 754, a communication interface 766, and a transceiver 768, among other components. Also, a storage device such as a microdrive or other device may be provided to the device 750 to provide additional storage. Each of the components 750, 752, 764, 754, 766, and 568 are connected to each other using a variety of buses, some of which may be mounted on a conventional motherboard or in other ways as appropriate.

Processor 752 executes instructions within computing device 750, which include instructions stored in memory 764. The processor may be implemented as a chip set of chips, including individual and multiple analog and digital processors. The processor may provide coordination between other components of the device 750, such as, for example, control of the user interface, applications executed by the device 750, and wireless communication by the computing device 750 have.

The processor 752 may communicate with the user via a control interface 758 and a display interface 756 coupled to the display 754. The display 754 may be, for example, a thin-film-tansistor liquid crystal display (TFT LCD) or organic light emitting diode (OLED) display, or other suitable display technology. Display interface 756 may include suitable circuitry to drive display 754 to present graphics and other information to the user. The control interface 758 receives instructions from the user and translates those instructions for submission to the processor 752. Moreover, extension interface 762 may be provided for communication with processor 752 to enable near field communication between device 750 and other devices. The extension interface 762, for example, provides wired communication in some embodiments and wireless communication in other embodiments, and multiple interfaces may also be used.

Memory 764 stores information in computing device 750. The memory 764 may be embodied as one or more of computer readable media or media, volatile memory units or units, or non-volatile memory units or units. An expansion memory 774 is also provided and may be connected to the device 750 via an expansion interface 774 that includes, for example, a Single In Line Memory Module (SIMM) card interface. This expansion memory 774 can provide extra storage space for the device 750 and can also store other information for the application or device 750. In particular, the extended memory 774 includes instructions for executing or assisting the above-described processes, and may also include security information. Thus, for example, the extended memory 774 may be provided as a security module for the device 750 and may be programmed with instructions to enable safe use of the device 750. [ Moreover, the security application may be provided via a SIMM card with additional information such as placing the identification information on the SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and / or NVRAM memory, as discussed below. In one embodiment, the computer program product is tangibly embodied in an information carrier. The computer program product, when executed, comprises instructions for performing one or more methods as described above. The information carrier may be a computer- or machine such as a memory 764, an expansion memory 774, a memory on the processor 752, or a transmitted signal that may be received, for example, via the transceiver 768 or the expansion interface 762. It is a readable medium.

Device 750 may communicate wirelessly via communication interface 766, which optionally includes digital signal processing circuitry. Communications interface 766 may provide communication under various modes or protocols such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, Such communication may be performed, for example, via a radio-frequency transceiver 768. [ Short range communications may also be performed using, for example, Bluetooth, WiFi, or other such transceivers (not shown). In addition, the Global Position System (GPS) receiver module 770 may provide additional navigation- and position-related wireless data to the device 750. This wireless data may be suitably used by an application running on device 750.

The device 750 can also communicate audibly with an audio codec 760 that receives spoken information from the user and converts the spoken information into usable digital information . In addition, the audio codec 760 generates voices audible to the user, such as through a speaker in the handset of device 750, for example. These voices may include voices from voice telephone calls and may include recorded voices (e.g., voice messages, music files, etc.) and also voices generated by applications running on device 750 . &Lt; / RTI &gt;

The computing device 750 may be implemented in a number of different forms, as shown in the figure. For example, the computing device 750 may be implemented as a cellular telephone 780. In addition, the computing device 750 may be implemented as part of a smart phone 782, PDA, or other similar mobile device.

Various embodiments of the various systems and methods described herein may be realized with digital electronic circuits, integrated circuits, application specific integrated circuits (ASICs) designed for specific purposes, computer hardware, firmware, software, and / have. Embodiments include embodiments of one or more computer programs that are executable and / or interpretable on a programmable system, and a programmable system is coupled to a storage system for transmitting and receiving instructions At least one programmable processor, at least one input device, and at least one output device.

Such computer programs (also referred to as programs, software, software applications, or code) may include machine instructions for a programmable processor and may be implemented in a high-level procedure and / or object-oriented programming language, and / have. As used herein, the terms “machine-readable medium”, “computer-readable medium” are any computer program product, apparatus, and / or used to provide machine instructions and / or data to a programmable processor. Or a device (eg, magnetic disk, optical disk, memory, programmable logic device (PLD)), and includes machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide instructions and / or data to a programmable processor.

To provide for interaction with a user, the systems and methods described herein include a display device (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor) that displays information to a user, May be implemented on a computer with a keyboard and a pointing device (e.g., a mouse or trackball) for input to a computer. Other categories of devices may also be used to provide interactions with the user. For example, the feedback provided to the user may be any form of the perceptual feedback (visual, auditory, or tactile feedback), and the input from the user may be any form of acoustic, Lt; / RTI &gt;

The various systems and methods described herein may be implemented with a backend component (e.g., a data server), or a middleware component (e.g., an application server) or a frontend component (e.g., A client computer with a graphical user interface (GUI) or web browser that a user can use to interact with embodiments of the described systems and methods), or any combination of such backend, middleware, or frontend components And the like. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), wide area network ("WAN"), and the Internet.

The computing system may include a client and a server. Clients and servers are generally separate from each other and interact through a communications network. The relationship between a client and a server is generated by computer programs running on each computer and having a client-server relationship to each other.

Although the specification includes several features, these should not be construed as limiting the scope of the disclosed or claimed subject matter, but rather as a description of features of exemplary embodiments of the disclosed subject matter. Certain features that are described in the context of separate embodiments herein may be provided in combination in one embodiment. Conversely, various features described in the context of one embodiment may be provided in multiple embodiments of separate or any suitable subcombination. Furthermore, even if features operate under certain conditions or even are raised as above, one or more features from the raised combinations may in some cases be deleted from the combination, and the raised combination may be a subcombination or a variation of the subcombination. Can be indicated.

Similarly, although the actions are shown in a particular order in the figures, it is understood that such actions must be performed in the specific order or time series order shown, or that all illustrated actions are performed to achieve the desired result. It should not be. In certain circumstances, multitasking and parallel processing may be advantageous. Furthermore, the division of various system components of the embodiments described above should not be construed as requiring such division in all embodiments, and the described program components and systems are generally integrated into one software product or packaged into multiple software products. It should be understood as possible.

That is, certain embodiments of the invention have been described. Other embodiments are also within the scope of the following claims. For example, the actions recited in the claims may be performed in a different order and still achieve the desired result. A number of embodiments have been described. Nevertheless, it will be understood that various modifications may be made without departing from the scope and spirit of the invention. For example, various types of flows shown above with re-ordered, added, removed steps may be used. Accordingly, other embodiments are also within the scope of the following claims.

104: disambiguation engine
106: speech recognition engine
108: Keyword Mapping Engine
110: Content Aware Engine

Claims (52)

Receiving utterance and environmental data;
Obtaining a transcription for said utterance;
Identifying an entity using the environment data;
Submitting a query to a natural language query processing engine that includes at least a portion of the notation and data identifying the entity; And
Obtaining one or more results for the query. The method according to claim 1,
And outputting a representation of the at least one result. The method according to claim 1,
And the entity is identified using the speech further. The method according to claim 1,
Generating the query. The method of claim 4,
Generating the query includes associating the notation with the data identifying the entity. The method according to claim 5,
The associating further comprises tagging the notation with the data identifying the entity. The method according to claim 5,
The associating further comprises substituting a portion of the notation with the data identifying the entity. The method of claim 7,
And the replacing step further comprises replacing one or more words of the notation with the data identifying the entity. The method according to claim 1,
Receiving the environmental data further comprises receiving environmental audio data, environmental image data, or both. The method of claim 9,
Receiving the environmental audio data further comprises receiving additional audio data comprising background noise. A system comprising one or more computers and one or more storage devices for storing instructions, wherein the instructions, when executed by the one or more computers, cause the one or more computers to execute.
Receiving speech and environmental data;
Obtaining a notation for the utterance;
Identifying an entity using the environment data;
Submitting a query to a natural language query processing engine that includes at least a portion of the notation and data identifying the entity; And
Enable to perform an operation to obtain one or more results for the query. The method of claim 11,
Further includes generating a query,
Generating the query comprises associating the notation with the data identifying the entity. The method of claim 12,
The associating further comprises tagging the notation with the data identifying the entity. The method of claim 12,
The associating further comprises replacing a portion of the notation with the data identifying the entity. The method according to claim 14,
And the replacing operation further comprises replacing one or more words of the notation with the data identifying the entity. The method of claim 11,
Receiving the environmental data further comprises receiving environmental audio data, environmental image data, or both. 18. The method of claim 16,
Receiving the environmental audio data further comprises receiving additional audio data comprising background noise. A computer-readable medium storing software containing instructions executable by one or more computers, wherein the instructions, when executed by the one or more computers, cause the one or more computers to execute.
Receiving audio data encoding utterance and environmental data;
Obtaining a notation for the utterance;
Identifying an entity using the environment data;
Submitting a query to a natural language query processing engine that includes at least a portion of the notation and data identifying the entity; And
And perform an operation of obtaining one or more results for the query. 19. The method of claim 18,
Further includes generating a query,
Generating the query comprises associating the notation with the data identifying the entity. The method of claim 19,
And the associating further comprises tagging the notation with the data identifying the entity. The method of claim 19,
And the associating further comprises replacing a portion of the notation with the data identifying the entity. 23. The method of claim 21,
And the replacing operation further comprises replacing one or more words of the notation with the data identifying the entity. (Iii) receiving audio data encoding a spoken natural language query and (ii) environmental audio data;
Obtaining a notation for the spoken natural language query;
Determining a specific content type associated with one or more keywords in the notation;
Providing at least a portion of the environmental audio data to a content recognition engine; And
Identifying a content item that has been output by the content recognition engine and that matches the particular content type. 24. The method of claim 23,
The specific content type may be a movie content type, a music content type, a television show content type, an audio podcast content type, a book content type, an artwork content type, a trailer content type, a video podcast content type, or an internet video content type. , Or a computer implemented method that is a type of video game content. 24. The method of claim 23,
Receiving the environmental audio data further comprises receiving additional audio data comprising background noise. 24. The method of claim 23,
And receiving additional environmental data comprising video data or image data. 24. The method of claim 23,
Outputting data identifying the content item. 24. The method of claim 23,
Providing at least a portion of the environmental audio data to the content recognition engine further comprises providing the portion of the environmental audio data to an audio fingerprinting engine. 24. The method of claim 23,
The determining of the specific content type may include identifying, for each of the plurality of content types, one or more keywords in which at least one of the keywords is mapped to at least one of the plurality of content types using one or more databases. Computer implemented method further comprising. 29. The method of claim 29,
The plurality of content types includes the specific content type,
Wherein said mapping further comprises mapping at least one of said keywords to said particular content type. 24. The method of claim 23,
The providing step further comprises providing data identifying the specific content type to the content recognition engine, and
Identifying the content item further comprises receiving data identifying the content item from the content recognition engine. 24. The method of claim 23,
Receiving at least two content recognition candidates from the content recognition system,
Identifying the content item further comprises selecting a particular content recognition candidate based on the specific content type. 33. The method of claim 32,
Each of the two or more content recognition candidates is associated with a ranking score,
And adjusting a ranking score of the two or more content recognition candidates based on the specific content type. 34. The method of claim 33,
Ranking the two or more content recognition candidates based on the adjusted ranking scores. A system comprising one or more computers and one or more storage devices for storing instructions, wherein the instructions, when executed by the one or more computers, cause the one or more computers to execute.
(Iii) receiving audio data encoding a spoken natural language query and (ii) environmental audio data;
Obtaining a notation for the spoken natural language query;
Determining a particular content type associated with one or more keywords in the notation;
Providing at least a portion of the environmental audio data to a content recognition engine; And
And identify the content item that has been output by the content recognition engine and that matches the particular content type. 36. The method of claim 35,
Receiving the environmental audio data further comprises receiving additional audio data comprising background noise. 36. The method of claim 35,
Receiving further environmental data comprising video data or image data. 26. The method of claim 25,
Providing at least a portion of the environmental audio data to the content recognition engine further comprises providing the portion of the environmental audio data to an audio fingerprinting engine. 36. The method of claim 35,
The determining of the specific content type may include identifying, for each of a plurality of content types, at least one of the keywords mapped to at least one of the plurality of content types, using at least one database. More including the system. 42. The method of claim 39,
The plurality of content types includes the specific content type,
The mapped operation further includes the operation of mapping at least one of the keywords to the specific content type. 36. The method of claim 35,
The providing operation further comprises providing data identifying the specific content type to the content recognition engine, and
Identifying the content item further comprises receiving data identifying the content item from the content recognition engine. 36. The method of claim 35,
Receiving at least two content recognition candidates from the content recognition system,
Identifying the content item further comprises selecting a particular content recognition candidate based on the specific content type. 43. The method of claim 42,
Each of the two or more content recognition candidates is associated with a ranking score,
Adjusting a ranking score of the two or more content recognition candidates based on the specific content type. The method of claim 43,
Ranking the two or more content recognition candidates based on the adjusted ranking scores. A non-transitory computer-readable medium storing software that includes instructions executable by one or more computers, the instructions that when executed by the one or more computers cause the one or more computers to execute.
(Iii) receiving audio data encoding a spoken natural language query and (ii) environmental audio data;
Obtaining a notation for the spoken natural language query;
Determining a particular content type associated with one or more keywords in the notation;
Providing at least a portion of the environmental audio data to a content recognition engine; And
And identify an item of content output by the content recognition engine that matches the particular content type. 46. The method of claim 45,
Providing at least a portion of the environmental audio data to the content recognition engine further comprises providing the portion of the environmental audio data to an audio fingerprinting engine. 46. The method of claim 45,
The determining of the specific content type may include identifying, for each of a plurality of content types, one or more keywords in which at least one of the keywords is mapped to at least one of the plurality of content types by using one or more databases. A computer readable medium further comprising. 48. The method of claim 47,
The plurality of content types includes the specific content type,
The mapped operation further comprises the operation of mapping at least one of the keywords to the specific content type. 46. The method of claim 45,
And outputting data identifying the content item. 46. The method of claim 45,
The providing operation further comprises providing data identifying the specific content type to the content recognition engine, and
Identifying the content item further comprises receiving data identifying the content item from the content recognition engine. 46. The method of claim 45,
Receiving at least two content recognition candidates from the content recognition system,
Identifying the content item further comprises selecting a particular content recognition candidate based on the specific content type. 54. The method of claim 51,
Each of the two or more content recognition candidates is associated with a ranking score,
And adjusting a ranking score of the two or more content recognition candidates based on the specific content type.

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