# Patent

## Title
Parsing rule augmentation based on query sequence and action co-occurrence

## Abstract

A language processing system identifies sequential command inputs in user session data stored in logs. Each sequence command input is a first command input followed by a second command input. The system determines user actions in response to each command input. For the second command input, an action was taken at the user device in response to the command input, and there is no parsing rule associated with the action that parses to the first command input. If there is a sufficient co-occurrence of the first and second command inputs and the resulting action in the logs, then a parsing rule for the action may be augmented with a rule for the first command input.

## Background
BACKGROUND
This specification relates to speech recognition and speech understanding systems.
Speech recognition and speech processing systems are prevalent in many consumer electronic devices. Many of these electronic devices now utilize speech command processing techniques to invoke and perform particular operations. For example, a user device, such as a smart phone, can process speech commands to perform specified operations that include searching the web, setting an alarm, calling a particular person, and so on.
A user device uses a speech recognition processing system to recognize and process speech commands. A provider of the speech recognition and processing system develops parsing rules for various commands a user will speak. Upon a successful parse of a command input by a rule, an action associated with the rule is performed (or may be performed subject to user confirmation). Users, however, can articulate commands for a particular action in many different ways. Thus, the provider of the speech recognition and processing system cannot anticipate beforehand all possible commands that will be received, and thus cannot generate all possible parsing rules beforehand. Accordingly, some commands may not parse for a particular action, or may parse to an action that is different from what the user intends.
SUMMARY
In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include the actions of accessing command input logs storing data defining user device sessions; identifying, from the command input logs, user device sessions that each respectively store: a sequence of two or more input sentences, each input sentence being one or more n-grams, and each input sentence having a respective ordinal position in the sequence, and wherein the sequence of two or more input sentences includes at least one pair of a first input sentence that precedes a second input sentence in ordinal position in the sequence; and data indicating a first action performed by a user device from which the user device session occurred, the first action having been performed in response to the second input sentence being successfully parsed by a parsing rule that is associated with the first action; identifying pairs of first and second input sentences from respective user device sessions that are indicative of an augmentation signal; and determining, from the identified pairs of first and second input sentences, first input sentences for which a parsing rule that is associated with the first action is to be generated. Other embodiments of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.
Particular embodiments of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages. The system identifies command input sequences that were used to invoke an action from user logs, and each command input sequence identified includes a command input that did not parse to a particular action. This command input may then be processed and associated with the particular action. Thus, by utilizing crowdsourcing techniques from log data, parse rules for command inputs that are most reflective of user behavior are generated for particular actions. Accordingly, new parses for an existing set of parses for a particular action emerge without requiring detailed studies of commands, or generation of additional command inputs by evaluators. This reduces the overall parse model maintenance costs and maintenance time.
The details of one or more embodiments of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of an environment in which log data are processed to facilitate parsing rule augmentation.
FIG. 2 is a flow diagram of an example process for augmenting parsing rules based on query sequences and action co-occurrence.
FIG. 3 is a flow diagram of an example process for determining whether to generate a parsing rule for a command input based on a co-occurrence measure.
FIG. 4 is a block diagram of an example mobile computing device.



Like reference numbers and designations in the various drawings indicate like elements.
DETAILED DESCRIPTION
A language processing system identifies sequential command inputs in user session data stored in logs. Each sequence command input is a first command input followed by a second command input. The system determines user actions in response to each command input. For the second command input, an action was taken at the user device in response to the command input, and there is no parsing rule associated with the action that parses to the first command input. If there is a sufficient co-occurrence of the first and second command inputs and the resulting action in the logs, then a parsing rule for the action may be augmented with a rule for the first command input.
FIG. 1 is a block diagram of an environment 100 in which the command models for corresponding actions are utilized. A computer network 102, such as a local area network (LAN), wide area network (WAN), the Internet, or a combination thereof, provides for data communication between electronic devices and systems. Examples of such electronic device and systems include web sites 104 and user devices 106. The computer network 102 may also be included, or be in data communication with, one or more wireless networks 103.
A website 104 includes one or more resources 105 associated with a domain name and hosted by one or more servers. An example website is a collection of web pages formatted in hypertext markup language (HTML) that can contain text, images, multimedia content, and programming elements, such as scripts. Each website 104 is maintained by a content publisher, which is an entity that controls, manages and/or owns the website 104. A resource 105 is any data that can be provided by the website 104 over the network 102 and that is associated with a resource address.
In addition to providing content, particular websites 104 may also provide one or more services. Example services include a search service provided by a search engine, video serving provided by video hosting servers, a mapping service providing by a map server, and so on.
A user device 106 is an electronic device that is capable of requesting and receiving resources over the network 102, establishing communication channels, e.g., voice communications, with other user devices 106, and also capable of performing other actions. Example user devices 106 include personal computers, mobile communication devices, and other devices that can send and receive data over the network 102. An example mobile user device 106, such as a smart phone, is described with reference to FIG. 4 below. The user devices 106 may communicate over the networks 102 and 103 by means of wired and wireless connections.
As described with reference to FIG. 4, a user device may be able to perform a set of device actions for various programs and capabilities. The actions may differ from each other for each action. For example, a telephone functionality may include the action placing a call in response to the commands of “call” or “phone,” a particular contact, and may also include the action of “texting” the particular contact in response to the commands to “text” or “message” the particular contact; a calendar application may include the actions of “scheduling” a meeting or reminder in response to the command of “schedule,” “accepting” or “declining” an invitation in response to the command to “accept” or “decline,” the invitation; a mapping service may include the actions of “mapping” a location in response to the command to “map” a location; and so on. The actions may be performed entirely by the user device, e.g., in the case of placing a phone call, or the user device may invoke an external service to perform the action, e.g., a map service or a search engine service.
In some implementations, the user device 106 utilizes a voice recognition system that receives voice command inputs, performs language recognition processing, and parses the resulting recognized words and phrases. The parsed words and phrases, which are referred to as n-grams, where each n-gram may constitute one or more words, are then processed to determine whether the voice input invokes a particular action.
As will be described in more detail below, each action A1, A2 . . . An has a corresponding command model CM1, CM2 . . . CMn, as indicated by the phantom box 108. The user device 106 utilizes the command models CMx to determine which, if any, actions are invoked in response to a voice input. As will be described in more detail below, the command models are, in some implementations, grammars that describe sentence structures for particular commands. Other appropriate command models may also be used.
As used in this specification, “command input” or “command input sentence” can be an input sentence, a query, or any other input that has one or more terms. The input does not need to be a grammatically correct and complete sentence, e.g., it need not have both a predicate and a subject. For example, the following input would constitute a command input, but would not constitute a grammatically complete sentence: “Images of lions.” A command input need not have an associated parsing rule, nor have annotations or other data describing terms of the command input. The language processing system, in some implementations, can process command inputs to generate a parsing rule and provide such annotations. In general, any input of one or more terms can be a command input, regardless of whether the particular command input is associated with other language processing data, such as parsing rules, parts of speech tagging, etc.
In some implementations, the command inputs of user devices 106 are stored in log data 116. The log data 116 stores data defining device sessions, command inputs received during the sessions, and actions taken in response to the command inputs. As used in this description, is device session, or simply a session, is defined by one or more command inputs that are received and grouped together to indicate they occurred during a session. The session can be defined by a variety of appropriate ways. For example, a session may be a set of command inputs received during a predefined time period, e.g., 30 minutes; or may be up to N inputs, e.g., up to 50 inputs; or may be a period during which a user maintained interactions with the user device until an inactivity time out (e.g., five minutes) occurred. Other appropriate criteria to define a session can also be used.
For each command input, the log data 116 also stores data describing actions taken in response to the command input. The data describing actions taken in response to the command input can be, for example, data describing service requests and parameters passed to the service; actions taken at the user device, such as the setting of a calendar entry; etc. Furthermore, some of the command inputs may not directly result in action performed by the user device. This may be the result of a command input not being parsed by a rule associated with an action; or being parsed by a rule associated with an action that is not an action the user desires; or for some other reason.
The log data 116 also indicates, for each session, the sequence of command inputs. Ordinal positions of the command inputs for each session can be defined by timestamps, sequence values, or other appropriate data that can be used to derive an ordinal position of a command input relative to other command inputs for a session. The log data 116 can thus be used by the language processing system 110 to determine the respective sequences of command inputs submitted by the user devices, the actions taken in response to the command inputs, and how often the command inputs have been submitted.
In some implementations, a language processing system 110 is used to parse the command input sentence and determine which, if any action, is to be performed. As shown in FIG. 1, the language processing system 110 is separate from the user devices. However, certain features of the language processing system 110 can instead be implemented within each user device, such as the command input sentence parser 140. Furthermore, the example architecture of the language processing system 110 is illustrative only, and other appropriate functional architectures can also be used.
The language processing system 110 receives command input sentences from user devices 106 and provides responsive data back to the user devices 106. Such responsive data may be, for example, a fully parsed sentence, the action invoked, and arguments to be passed to the service that performs the action.
The language processing system 110 includes command models data 132 that associates actions with corresponding command models by use of parsing rules, such as grammars. The command models data 132 store command models for actions specified by action data 114, and are generated by the command model generator 130. In some implementations, the command models are each configured to generate an action score for an action for an input sentence based on, for example, a semantic meaning of the input sentence.
In some implementations, the language processing system 110 augments parsing rules for particular actions by processing command input sequences. In particular, the language processing system includes a sequence identifier 120 and a candidate sentence identifier 122. The sequence identifier 120 identifies command input sequences of first and second input sequences in user sessions, and the candidate sentence identifier 122 identifies first sentences from the identified command input sequences for which a parsing rule that is associated with the action taken in response to the second command input sentence is to be generated. Operation of the sequence identifier 120 and the candidate sentence identifier 122 are described with reference to FIG. 2, which is a flow diagram of an example process 200 for augmenting parsing rules based on query sequences and action co-occurrence.
The sequence identifier 120 accesses command input logs storing data defining user device sessions (202). For example, the sequence identifier 120 accesses the log data 116.
The sequence identifier 120 identifies, from the command input log data 116, user device sessions (204). Each user device session that is identified stores a sequence of two or more command input sentences and data indicating a first action performed by a user device from which the user device session occurred. Each input sentence is one or more n-grams, and has a respective ordinal position in the sequence. The sequence of two or more input sentences includes at least one pair of a first input sentence that precedes a second input sentence in ordinal position in the sequence. For example, a sequence can include only two input sentences, in which the first is in a first ordinal position and a second is in a second ordinal position. Thus, there would only be one pair of first and second input sentences.
By way of another example, a sequence that includes four input sentences may have multiple first and second input sentence pairs. The pairs may, in some implementations, be a sequence of two input sentences with no intervening input sentences. Thus, for the four input sentence sequence, three pairs can be identified: a pair defined by the first and second input sentences, a pair defined by the second and third input sentences, and a pair defined by the third and fourth input sentence. In alternate implementations, the pairs may be a sequence of two input sentences with one or more intervening input sentences. Thus, for the four input sentence sequence, six pairs can be identified: the three described above, and a pair defined by the first and third input sentences, a pair defined by the first and fourth sentences, and a pair defined by the second and fourth input sentences.
The identified session also has data indicating a first action performed by a user device from which the user device session occurred. The first action is an action that was performed in response to the second input sentence being successfully parsed by a parsing rule that is associated with the first action. For example, suppose a session stores data describing the following information in Table 1 below:









TABLE 1





Seq. Pos.
Command Input Sentence
Action







1
Search for grocery stores
Search


2
Jane text I'll be late because I am going to the 
Null



grocery store to get mangos



3
Text Jane I'll be late because I am going to the 
Text Msg



grocery store to get mangos









The sequence position column has row values that define an ordinal position in a sequence. The command input sentence column has row values that list the command input sentence, and the action column has row values that list the action performed in response to the command input sentence, where the null value indicates no action was performed in response to the command input sentence.
The candidate sentence identifier 122 identifies pairs of first and second input sentences that are indicative of an augmentation signal from respective user device sessions (206). An augmentation signal is an indication, derived from underlying data related to command inputs, that a first command input may be indicative of a request to perform an action that is eventually performed in response to the second command input. A pair of command sentence inputs may be determined to be indicative of an augmentation signal by a variety of appropriate ways. For example, in response to the second input sentence being successfully parsed by a parsing rule that is associated with the first action, a confirmation request to perform the first action is presented at the user device. Acceptance of the confirmation request received at the user device results in a determination that the pair of command sentence inputs is indicative of an augmentation signal.
In some situations, the first sentence may be successfully parsed by a parsing rule that is associated with an action that is different from the action performed in response to the parsing of the second sentence. In these situations, one way of determining an augmentation signal is present, is determining that the action associated with the parsing rule that parsed the first sentence was not performed, and that the action associated with the parsing rule that parsed the second sentence was performed.
Yet another way is a “default” condition that is satisfied by any first sentence that is not parsed by any parsing rule, followed by a second sentence that is parsed by a parsing rule. Other conditions can be used to identify pairs of first and second input sentences that are indicative of an augmentation signal.
With respect to Table 1, assume that a sequence of command inputs does not include an intervening command input. The sequence identifier 120 will identify the command inputs (2) and (3) as a pair of command inputs. This is because after the first command input [Jane text I'll be late because I am going to the grocery store to get mangos], the user device did not perform an action. This may be, for example, due to no rules being able to successfully parse the command input, or due to the user not confirming the performance of an action in response to the parsing of the command input (e.g., decline a prompt that confirms a particular action is be performed). However, after the second command input, the user device sends a text message. Accordingly, the command inputs (2) and (3) are identified as a pair of command inputs indicative of an augmentation signal.
Conversely, the command inputs (1) and (2) are not identified as a pair of command inputs indicative of an augmentation signal, as an action was performed after the first command input and no action was performed after the second command input.
The candidate sentence identifier 122 determines, from the proper subset of identified pairs of first and second input sentences, first input sentences for which a parsing rule that is associated with the first action is to be generated (208). For example, the first command input sentence of identified pairs that have a co-occurrence measure that meets a threshold are selected and provided to command model generator 130 for generating a parsing rule associated with the action.
FIG. 3 is a flow diagram of an example process 300 for determining whether to generate a parsing rule for a command input based on a co-occurrence measure. The process 300 is performed in the candidate sentence identifier 122.
For each first input sentence, the candidate sentence identifier 122 determines a respective co-occurrence measure for the first input sentence (302). For example, the candidate sentence identifier 122 determines pairs of first and second input sentences for which a particular action was performed for each second input sentence of the pair. With respect to Table 1 above, if the particular action is a text messaging action, then the sentence “Jane text I'll be late because I am going to the grocery store to get mangos” is selected.
Of these selected first command inputs, similar sentences are then grouped to determine co-occurrence measures. A variety of different ways can be used to group similar sentences. For example, sentences can be generally parsed based on variable classes for various action types. The candidate sentence identifier stores data, or has access to data or services, that describe such parsing terms that are semantically relevant to particular actions. In the case of text messaging, such parsing terms include contact terms and terms that are semantically relevant to invoke a text action, such as “text,” “message,” “SMS,” and so on. Thus, for the command input:
Jane text I'll be late because I am going to the grocery store to get mangos
A generalized parse is:
<Contact><Text_Action><Message>
where
Contact is parameter having user identifier values or contact names;
Text_Action is a parameter having term values that are semantically relevant to texting; and
<Message> is a residual command input sentence.
Assume that after processing the log data 116, the following first command input sentences are identified from first and second sentence pairs for the text message action:








TABLE 2





Action
Command Input







Text Msg
Jane text I'll be late because I am going to the grocery 



store to get mangos


Text Msg
John text Did you get that link I sent you?


Text Msg
Send message want to meet for ribs after work to Joe by text


Text Msg
Page text How was tennis today?


Text Msg
Julia SMS The birdhouse you made is great!









Each sentence above is a first sentence in a pair of first and second input sentences that occurred during respective user sessions. Each respective second sentence (not shown) of each respective pair resulted in a parse to a text message action and a performance of the action by a user device. The example set above is a representative set for illustrative purposes; actual log data may contain millions of identified pairs.
Except for the third sentence in Table 2, each sentence parses to the general form of <Contact><Text_Action><Message>. Thus the sentences have a co-occurrence measure that is proportion to the count of 4. The third sentence, however, parses to the form of:
<Text_Action><Text_Action><Message><Contact>by<Text_Action>
This sentence has a co-occurrence measure that is proportion to the count of 1. The higher the co-occurrence measure, the more likely a sentence should be associated with a particular action. The co-occurrence measure can be interpreted as a signal of how well a sentence expresses a desire to perform an action. The better formed the sentence structure is, the more likely it is that multiple users will use the particular sentence structure.
Other ways of grouping first sentences can also be used. For example, the first sentences can be grouped according to the similarity of respective second sentences, or the first sentences can be grouped according to grouping second input sentences that are successfully parsed by a same rule.
The co-occurrence measure, in some implementations, is the co-occurrence count that measures the cardinality of the first input sentences of the group. In other implementations, the co-occurrence measure can be based on a function that receives the co-occurrence count as input. An example function may be an exponential function or a function that asymptotically approaches a limit.
The candidate sentence identifier 122 determines a parsing rule that is associated with the first action is to be generated for first input sentences having a respective co-occurrence measure that meets a threshold value (304). Depending on the co-occurrence measure used, the threshold value can be a raw count value or some other value. The threshold value is selected so that infrequently occurring first sentences and other “noisy” signals are reduced. For example, for Table 2, assume the threshold value is 3. Because the first, second, fourth and fifth sentences are grouped together, and the cardinality of the group is 4, they are each determined to be associated with a parsing rule for the action of sending a text message.
The sentences that are determined to be associated with a first action are then sent to a command model generator 130 that generates the parsing rules. The command model generator 130 can be implemented as a supervised or unsupervised machine learning process. In an alternate implementation, the sentences can be sent to human rater that decides whether the sentences are to be submitted to the command model generator 130, or, alternatively, that generate the parsing rules manually.
In some implementations, the parsing rules are grammar based rules. For example, a grammar is generated for the above four sentences in the form:














G = <T, N, S, R>;



where:



T = {Text, to, Jane, Maddison, that, I, am, running, late, tonight}



N = {Contact, Text_Action, Message}



S = S



R = {









S → <Contact> <Text_Action> <Message>









Contact → <Contact List>



Text Action → text, SMS



Message → {·}









}









For the non-terminal <Contact>, identifiers from a contact list are used. For the non-terminal Text_Action, the terminals “text” and “SMS” are defined, and for the non-terminal message, an open set of terminals is defined. Thus, any sentence of the form
<Contact><Text_Action><Message>
will successfully parse to the newly generated rule. Because the rule is associated with a text action, a successful parse at a user device will cause the user device to send a text message that includes the residual text after the contact to the user device of the contact.
FIG. 4 is a block diagram of an example mobile computing device. In this illustration, the mobile computing device 610 is depicted as a handheld mobile telephone (e.g., a smartphone, or an application telephone) that includes a touchscreen display device 612 for presenting content to a user of the mobile computing device 610 and receiving touch-based user inputs. Other visual, tactile, and auditory output components may also be provided (e.g., LED lights, a vibrating mechanism for tactile output, or a speaker for providing tonal, voice-generated, or recorded output), as may various different input components (e.g., keyboard 614, physical buttons, trackballs, accelerometers, gyroscopes, and magnetometers).
Example visual output mechanism in the form of display device 612 may take the form of a display with resistive or capacitive touch capabilities. The display device may be for displaying video, graphics, images, and text, and for coordinating user touch input locations with the location of displayed information so that the device 610 can associate user contact at a location of a displayed item with the item. The mobile computing device 610 may also take alternative forms, including as a laptop computer, a tablet or slate computer, a personal digital assistant, an embedded system (e.g., a car navigation system), a desktop personal computer, or a computerized workstation.
An example mechanism for receiving user-input includes keyboard 614, which may be a full qwerty keyboard or a traditional keypad that includes keys for the digits ‘0-9’, ‘*’, and ‘#.’ The keyboard 614 receives input when a user physically contacts or depresses a keyboard key. User manipulation of a trackball 616 or interaction with a track pad enables the user to supply directional and rate of movement information to the mobile computing device 610 (e.g., to manipulate a position of a cursor on the display device 612).
The mobile computing device 610 may be able to determine a position of physical contact with the touchscreen display device 612 (e.g., a position of contact by a finger or a stylus). Using the touchscreen 612, various “virtual” input mechanisms may be produced, where a user interacts with a graphical user interface element depicted on the touchscreen 612 by contacting the graphical user interface element. An example of a “virtual” input mechanism is a “software keyboard,” where a keyboard is displayed on the touchscreen and a user selects keys by pressing a region of the touchscreen 612 that corresponds to each key.
The mobile computing device 610 may include mechanical or touch sensitive buttons 618a-d. Additionally, the mobile computing device may include buttons for adjusting volume output by the one or more speakers 620, and a button for turning the mobile computing device on or off. A microphone 622 allows the mobile computing device 610 to convert audible sounds into an electrical signal that may be digitally encoded and stored in computer-readable memory, or transmitted to another computing device. The mobile computing device 610 may also include a digital compass, an accelerometer, proximity sensors, and ambient light sensors.
An operating system may provide an interface between the mobile computing device's hardware (e.g., the input/output mechanisms and a processor executing instructions retrieved from computer-readable medium) and software. The operating system may provide a platform for the execution of application programs that facilitate interaction between the computing device and a user.
The mobile computing device 610 may present a graphical user interface with the touchscreen 612. A graphical user interface is a collection of one or more graphical interface elements and may be static (e.g., the display appears to remain the same over a period of time), or may be dynamic (e.g., the graphical user interface includes graphical interface elements that animate without user input).
A graphical interface element may be text, lines, shapes, images, or combinations thereof. For example, a graphical interface element may be an icon that is displayed on the desktop and the icon's associated text. In some examples, a graphical interface element is selectable with user-input. For example, a user may select a graphical interface element by pressing a region of the touchscreen that corresponds to a display of the graphical interface element. In some examples, the user may manipulate a trackball to highlight a single graphical interface element as having focus. User-selection of a graphical interface element may invoke a pre-defined action by the mobile computing device. In some examples, selectable graphical interface elements further or alternatively correspond to a button on the keyboard 614. User-selection of the button may invoke the pre-defined action.
The mobile computing device 610 may include other applications, computing sub-systems, and hardware. A voice recognition service 672 may receive voice communication data received by the mobile computing device's microphone 622, and translate the voice communication into corresponding textual data or perform voice recognition. The processed voice data can be input to the command models stored in the command models data 132 to determine whether the voice input used to generate the voice data invokes a particular action for a particular application as described above. One or more of the applications, services and units below may have corresponding actions invoked by such voice commands.
A call handling unit may receive an indication of an incoming telephone call and provide a user the capability to answer the incoming telephone call. A media player may allow a user to listen to music or play movies that are stored in local memory of the mobile computing device 610. The mobile device 610 may include a digital camera sensor, and corresponding image and video capture and editing software. An internet browser may enable the user to view content from a web page by typing in an address corresponding to the web page or selecting a link to the web page.
A service provider that operates the network of base stations may connect the mobile computing device 610 to the network 102 to enable communication between the mobile computing device 610 and other computing systems that provide services 660. The services 660 may be provided over different networks (e.g., the service provider's internal network, the Public Switched Telephone Network, and the Internet). The service provider may operate a server system that routes information packets and voice data between the mobile computing device 610 and computing systems associated with the services 660.
An application store 666 may provide a user of the mobile computing device 610 the ability to browse a list of remotely stored application programs that the user may download over the network and install on the mobile computing device 610. The application store 666 may serve as a repository of applications developed by third-party application developers. An application program that is installed on the mobile computing device 610 may be able to communicate over the network with server systems that are designated for the application program. For example, a VoIP application program may be downloaded from the application store 666, enabling the user to communicate with the VoIP service 664.
The mobile computing device 610 may access content on the internet 668 through network 102. For example, a user of the mobile computing device 610 may invoke a web browser application that requests data from remote computing devices that are accessible at designated universal resource locations. In various examples, some of the services 660 are accessible over the internet.
The mobile computing device may communicate with a personal computer 670. For example, the personal computer 670 may be the home computer for a user of the mobile computing device 610. Thus, the user may be able to stream media from his personal computer 670. The user may also view the file structure of his personal computer 670, and transmit selected documents between the computerized devices.
The mobile computing device 610 may communicate with a social network 674. The social network may include numerous members, some of which have agreed to be related as acquaintances. Application programs on the mobile computing device 610 may access the social network 674 to retrieve information based on the acquaintances of the user of the mobile computing device. For example, an “address book” application program may retrieve telephone numbers for the user's acquaintances. In various examples, content may be delivered to the mobile computing device 610 based on social network distances from the user to other members in a social network graph of members and connecting relationships. For example, advertisement and news article content may be selected for the user based on a level of interaction with such content by members that are “close” to the user (e.g., members that are “friends” or “friends of friends”).
The mobile computing device 610 may access a personal set of contacts 676 through the network. Each contact may identify an individual and include information about that individual (e.g., a phone number, an email address, and a birthday). Because the set of contacts is hosted remotely to the mobile computing device 610, the user may access and maintain the contacts 676 across several devices as a common set of contacts.
The mobile computing device 610 may access cloud-based application programs 678. Cloud-computing provides application programs (e.g., a word processor or an email program) that are hosted remotely from the mobile computing device 610, and may be accessed by the computing device 610 using a web browser or a dedicated program.
Mapping service 680 can provide the mobile computing device 610 with street maps, route planning information, and satellite images. The mapping service 680 may also receive queries and return location-specific results. For example, the mobile computing device 610 may send an estimated location of the mobile computing device and a user-entered query for “pizza places” to the mapping service 680. The mapping service 680 may return a street map with “markers” superimposed on the map that identify geographical locations of nearby “pizza places.”
Turn-by-turn directions service 682 may provide the mobile computing device 610 with turn-by-turn directions to a user-supplied destination. For example, the turn-by-turn service 682 may stream to device 610 a street-level view of an estimated location of the device, along with data for providing audio commands and superimposing arrows that direct a user of the device 610 to the destination.
Various forms of streaming media 684 may be requested by the mobile computing device 610. For example, computing device 610 may request a stream for a pre-recorded video file, a live television program, or a live radio program.
A micro-blogging service 686 may receive from the mobile computing device 610 a user-input post that does not identify recipients of the post. The micro-blogging service 686 may disseminate the post to other members of the micro-blogging service 686 that agreed to subscribe to the user.
A search engine 688 may receive user-entered textual or verbal queries from the mobile computing device 610, determine a set of internet-accessible documents that are responsive to the query, and provide to the device 610 information to display a list of search results for the responsive documents. In examples where a verbal query is received, the voice recognition service 672 may translate the received audio into a textual query that is sent to the search engine.
These and other services may be implemented in a server system 690. A server system may be a combination of hardware and software that provides a service or a set of services. For example, a set of physically separate and networked computerized devices may operate together as a logical server system unit to handle the operations necessary to offer a service to hundreds of computing devices. A server system is also referred to herein as a computing system.
In various implementations, operations that are performed “in response to” or “as a consequence of” another operation (e.g., a determination or an identification) are not performed if the prior operation is unsuccessful (e.g., if the determination was not performed). Operations that are performed “automatically” are operations that are performed without user intervention (e.g., intervening user input). Features in this document that are described with conditional language may describe implementations that are optional. In some examples, “transmitting” from a first device to a second device includes the first device placing data into a network for receipt by the second device, but may not include the second device receiving the data. Conversely, “receiving” from a first device may include receiving the data from a network, but may not include the first device transmitting the data.
“Determining” by a computing system can include the computing system requesting that another device perform the determination and supply the results to the computing system. Moreover, “displaying” or “presenting” by a computing system can include the computing system sending data for causing another device to display or present the referenced information.
Embodiments of the subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially-generated propagated signal. The computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices).
The operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.
The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures.
A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user device in response to requests received from the web browser.
Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can 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”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).
The computing system can include users and servers. A user and server are generally remote from each other and typically interact through a communication network. The relationship of user and server arises by virtue of computer programs running on the respective computers and having a user-server relationship to each other. In some embodiments, a server transmits data (e.g., an HTML page) to a user device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the user device). Data generated at the user device (e.g., a result of the user interaction) can be received from the user device at the server.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous.

## Claims

1. A computer-implemented method performed by a data processing apparatus, the method comprising:
accessing command input logs storing data defining user device sessions;
identifying, from the command input logs, user device sessions that each respectively store:
a sequence of two or more input sentences, each input sentence being one or more n-grams, and each input sentence having a respective ordinal position in the sequence, and wherein the sequence of two or more input sentences includes at least one pair of a first input sentence that precedes a second input sentence in ordinal position in the sequence; and
data indicating a first action performed by a user device from which the user device session occurred, the first action having been performed in response to the second input sentence being successfully parsed by a parsing rule that is associated with the first action;

identifying pairs of first and second input sentences from respective user device sessions that are indicative of an augmentation signal, comprising identifying pairs of first and second input sentence for which the first action is not performed in response to the first input sentence and is performed in response to the second input sentence; and
determining, from the identified pairs of first and second input sentences, first input sentences for which a parsing rule that is associated with the first action is to be generated;
wherein the determining first input sentences for which the parsing rule that is associated with the first action is to be generated comprises, for each first input sentence:
determining a co-occurrence measure for the occurrence of first input sentence and the occurrence of the first action for the second input sentence; and
determining a parsing rule that is associated with the first action is to be generated for the first input sentence when co-occurrence measure for the first input sentence and the first action meets a threshold value.

2. The computer-implemented method of claim 1, wherein identifying pairs of first and second input sentences that are indicative of the augmentation signal from respective user device sessions comprises, for each identified pair:
determining that, in response to the second input sentence being successfully parsed by a parsing rule that is associated with the first action, a confirmation request to perform the first action was presented at the user device and a confirmation in response to the confirmation request was received at the user device.

3. The computer-implemented method of claim 2, wherein identifying pairs of first and second input sentences that are indicative of the augmentation signal from respective user device sessions further comprises, for each identified pair:
determining that a second action that is different from the first action was not performed in response to the first input sentence being successfully parsed by a parsing rule that is associated with the second action.

4. The computer-implemented method of claim 2, wherein identifying pairs of first and second input sentences that are indicative of the augmentation signal from respective user device sessions further comprises, for each identified pair:
determining the first input sentence was not successfully parsed by a parsing rule.

5. The computer-implemented method of claim 1, wherein determining the co-occurrence measure for the first input sentence comprises:
grouping first input sentences that are determined to be similar to each other; and
determining that a cardinality of the group of first input sentences meets a threshold cardinality.

6. The computer-implemented method of claim 1, wherein determining the co-occurrence measure for the first input sentence comprises:
grouping second input sentences that are determined to be similar to each other;
grouping, from the identified pairs of first and second input sentences that include a second input sentence belonging to the group of second input sentences, first input sentences that are determined to be similar to each other; and
determining that a cardinality of the group of first input sentences meets a threshold cardinality.

7. The computer-implemented method of claim 6, wherein grouping second input sentences that are determined to be similar to each other comprises grouping second input sentences that are successfully parsed by a same rule.

8. The computer-implemented method of claim 6, wherein grouping second input sentences that are determined to be similar to each other comprises grouping second input sentences that are successfully parsed rules associated with the first action.

9. The computer-implemented method of claim 1, wherein the input sentences are generated from voice command inputs.

10. The computer-implemented method of claim 1, wherein the identified pairs of first and second input sentences are a proper subset of the pairs of first and second input sentences occurring in the user device sessions.

11. The computer-implemented method of claim 1, wherein the parsing rule is a grammar based rule.

12. A non-transitory computer readable storage medium storing instructions executable by a data processing apparatus and that upon such execution causes the data processing apparatus to perform operations comprising:
accessing command input logs storing data defining user device sessions;
identifying, from the command input logs, user device sessions that each respectively store:
a sequence of two or more input sentences, each input sentence being one or more n-grams, and each input sentence having a respective ordinal position in the sequence, and wherein the sequence of two or more input sentences includes at least one pair of a first input sentence that precedes a second input sentence in ordinal position in the sequence; and
data indicating a first action performed by a user device from which the user device session occurred, the first action having been performed in response to the second input sentence being successfully parsed by a parsing rule that is associated with the first action;

identifying pairs of first and second input sentences from respective user device sessions that are indicative of an augmentation signal, comprising identifying pairs of first and second input sentence for which the first action is not performed in response to the first input sentence and is performed in response to the second input sentence; and
determining, from the identified pairs of first and second input sentences, first input sentences for which a parsing rule that is associated with the first action is to be generated;
wherein the determining first input sentences for which the parsing rule that is associated with the first action is to be generated comprises, for each first input sentence:
determining a co-occurrence measure for the occurrence of first input sentence and the occurrence of the first action for the second input sentence; and
determining a parsing rule that is associated with the first action is to be generated for the first input sentence when co-occurrence measure for the first input sentence and the first action meets a threshold value.

13. The non-transitory computer readable storage medium of claim 12, wherein identifying pairs of first and second input sentences that are indicative of the augmentation signal from respective user device sessions comprises, for each identified pair:
determining that, in response to the second input sentence being successfully parsed by a parsing rule that is associated with the first action, a confirmation request to perform the first action was presented at the user device and a confirmation in response to the confirmation request was received at the user device.

14. The non-transitory computer readable storage medium of claim 13, wherein identifying pairs of first and second input sentences that are indicative of the augmentation signal from respective user device sessions further comprises, for each identified pair:
determining that a second action that is different from the first action was not performed in response to the first input sentence being successfully parsed by a parsing rule that is associated with the second action.

15. The non-transitory computer readable storage medium of claim 13, wherein identifying pairs of first and second input sentences that are indicative of the augmentation signal from respective user device sessions further comprises, for each identified pair:
determining the first input sentence was not successfully parsed by a parsing rule.

16. The non-transitory computer readable storage medium of claim 12, wherein determining the co-occurrence measure for the first input sentence comprises:
grouping first input sentences that are determined to be similar to each other; and
determining that a cardinality of the group of first input sentences meets a threshold cardinality.

17. The non-transitory computer readable storage medium of claim 12, wherein determining the co-occurrence measure for the first input sentence comprises:
grouping second input sentences that are determined to be similar to each other;
grouping, from the identified pairs of first and second input sentences that include a second input sentence belonging to the group of second input sentences, first input sentences that are determined to be similar to each other; and
determining that a cardinality of the group of first input sentences meets a threshold cardinality.

18. The non-transitory computer readable storage medium of claim 17, wherein grouping second input sentences that are determined to be similar to each other comprises grouping second input sentences that are successfully parsed by a same rule.

19. The non-transitory computer readable storage medium of claim 17, wherein grouping second input sentences that are determined to be similar to each other comprises grouping second input sentences that are successfully parsed rules associated with the first action.

20. A system, comprising:
a data processing apparatus; and
a non-transitory computer readable storage medium in data communication with the data processing apparatus storing instructions executable by the data processing apparatus and that upon such execution causes the data processing apparatus to perform operations comprising:
accessing command input logs storing data defining user device sessions;
identifying, from the command input logs, user device sessions that each respectively store:
a sequence of two or more input sentences, each input sentence being one or more n-grams, and each input sentence having a respective ordinal position in the sequence, and wherein the sequence of two or more input sentences includes at least one pair of a first input sentence that precedes a second input sentence in ordinal position in the sequence; and
data indicating a first action performed by a user device from which the user device session occurred, the first action having been performed in response to the second input sentence being successfully parsed by a parsing rule that is associated with the first action;

identifying pairs of first and second input sentences from respective user device sessions that are indicative of an augmentation signal, comprising identifying pairs of first and second input sentence for which the first action is not performed in response to the first input sentence and is performed in response to the second input sentence; and
determining, from the identified pairs of first and second input sentences, first input sentences for which a parsing rule that is associated with the first action is to be generated;
wherein the determining first input sentences for which the parsing rule that is associated with the first action is to be generated comprises, for each first input sentence:
determining a co-occurrence measure for the occurrence of first input sentence and the occurrence of the first action for the second input sentence; and
determining a parsing rule that is associated with the first action is to be generated for the first input sentence when co-occurrence measure for the first input sentence and the first action meets a threshold value.