Patent Description:
In conventional systems, man-machine dialogs between a person and a virtual assistant or other natural language processing systems have a strict turn-taking policy. Such virtual assistants do not accept a new query from a user until the assistant's response for a previous query has been fully communicated to the user. This lack of flexibility results in unnatural communication.

<CIT> discloses the provision of intent prediction for a natural language utterance based on a portion of the natural language utterance prior to a system detection of an end of the natural language utterance.

An invention is set out in the independent claims <NUM> and <NUM>.

The figures depict various embodiments of the present disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following description that other alternative embodiments of the structures and methods illustrated herein can be employed without departing from the principles of the disclosure described herein.

<FIG> shows a system environment in which query processing takes place, according to one embodiment. Users of client devices <NUM> use speech to express their wishes, including requests for action (e.g., "create a calendar appointment for dinner with Karl at <NUM> PM today") and requests for information (e.g., "what is the weather tomorrow in Rome?").

In the embodiment illustrated in <FIG>, the query processing takes place on a query-processing server <NUM> located remotely over a network <NUM>. In other embodiments, however, the query processing is local, taking place directly on the client device <NUM>, e.g., as part of a virtual assistant application installed on the client device. In some embodiments, speech is segmented and transcribed locally on the client device <NUM>, while the processing performed on server <NUM> operates on text segments. In some embodiments, server <NUM> is distributed across a number of physical servers.

The client devices <NUM> are computing devices such as smart phones, tablets, laptop computers, or desktop computers, or a smart appliance or other device (e.g., a car, or a coffee machine, with data processing capabilities), or any other device that can input a natural language query and output a response to the query. The client devices <NUM> have sound input devices <NUM>, such as microphones and A/D converters, that capture sounds and convert them to digital audio data, and sound output devices <NUM>, such as D/A converters and speakers, that convert digital audio data to a corresponding audible output.

The network <NUM> (in embodiments in which a network is employed, such as that of <FIG>) is optionally any suitable communications network for data transmission. In an embodiment such as that illustrated in <FIG>, the network <NUM> uses standard communications technologies and/or protocols and can include the Internet. In another embodiment, the entities use custom and/or dedicated data communications technologies.

The query-processing server <NUM> and the systems of the client devices <NUM> are optionally implemented with various forms of hardware in different embodiments. In all embodiments, the query-processing logic is a set of computer-implemented algorithms, as further described herein. Finally, as will be apparent from the following discussion, the algorithms and processes described herein require implementation on a computer system, and cannot be performed by humans using mental steps in any useful embodiment.

Some embodiments use client devices to capture utterances and servers to process the utterances in order to form and execute queries. Some embodiments perform capture and processing in a single device.

<FIG> illustrates components of the query-processing module <NUM>, according to one embodiment. An input module <NUM> receives as input a digital audio stream. The audio stream is captured by a sound input device <NUM> and in some embodiments it represents a user's spoken query. An "utterance" is a contiguous segment of speech. The input module <NUM> segments input audio into utterances. To do so, it detects a beginning and end for each utterance, based on identifying a pause in the speech, or non-speech audio, or UI events such as a user making a gesture (swiping, pushing a button) or other means. For example, if a particular user says "what is the weather tomorrow", then pauses for some time interval (e.g., <NUM>), then continues with "in Rome", the speech input includes two distinct utterances separated by <NUM> of non-speech.

In the embodiment of <FIG>, input module <NUM> outputs an utterance stream. Each utterance in the utterance stream is processed in turn by processing module <NUM>. The first step in processing module <NUM> is parsing, using the natural language parser <NUM>. In this disclosure, the terms "parse," "parser" and "parsing" are not used in the narrow sense of checking the syntax of a query against a natural language grammar, but in the broader sense of "recognizing" a query in terms of both its form and meaning. The parser <NUM> is thus a semantic parser, and its function is the recognition of queries. Query recognition <NUM>, when successful, includes the construction of a representation of a query's meaning. The recognition of a query builds this representation and adds it to the query queue <NUM>.

The parser <NUM> also rejects queries that are syntactically ill formed or semantically meaningless. The failure of a parsing attempt is handled by different systems in different ways. In some cases, an error message is sent to the user as a response. In other cases, no response is given. Either way, no query is added to query queue <NUM> for subsequent execution.

In an incremental embodiment of processing module <NUM>, successive utterances are parsed as soon as they are available from the input module, and processing always goes forward in the input stream. In such an embodiment, parser <NUM> is an incremental parser. Algorithms for incremental parsing are known to people of ordinary skill in the art. In the context of incremental parsing, "parses" broadly refer to partially completed parses, as opposed to complete parses that make a query. Incremental parsers maintain in parallel all possible parses (or sufficiently likely parses) of the input stream, by updating (partial) parses each time an input token is added from the input stream. A query is recognized as soon as any of the partial parses becomes a complete parse, in both syntactic and semantic terms. When this happens, the incremental parser outputs a corresponding query data structure and adds it to the query queue <NUM>. At this point, the parser resumes processing of the input stream from the current state of parsing. The state of parsing includes its position and direction in the stream, and the state of all parallel partial parses.

In some embodiments, recognizing a query requires the presence of an end-of-utterance boundary at the end of the query. In other embodiments (which, without prejudice to the protection conferred by the present European patent/application, are not in accordance with the wording of the claims), query recognition does not require an end-of-utterance boundary to complete a query. In the latter embodiment, after receiving "what is the weather tomorrow" from the utterance stream, incremental parser <NUM> is able to recognize a first query, "what is the weather," which it adds to the query queue <NUM>, while maintaining the state of parsing. Next, "what is the weather tomorrow" is recognized as a second query. (In an embodiment where the end-of-utterance is needed, only the second query would be recognized. ) The incremental nature of the parser gives it the ability to be sensitive to segmentation factors, such as the duration of a pause between "what is the weather" and "tomorrow," to recognize one or two queries in the input "what is the weather tomorrow. " The extended input "what is the weather tomorrow in Rome" (with "in Rome" following "what is the weather tomorrow") yields an additional query. In this case, three successive queries are recognized.

In embodiments, not encompassed by the wording of the claims but considered as useful for understanding the invention, using a non-incremental parser, a similar output is achieved at the cost of some additional processing. In effect, a non-incremental parser is restarted on concatenations of recent utterances. (In this disclosure, the operator "+" is used to denote the concatenation of audio segments. ) For example, if utterance U1 is the speech segment "what is the weather," U2 is "tomorrow" and U3 is "in Rome", a non-incremental parser must be restarted in turn (from a cold start) to take as input each of the three utterances U1, U1 +U2, and U1 +U2+U3. While, in some embodiments, additional complexity is involved, non-incremental embodiments of the techniques presented are possible, which offer query recognition capabilities similar to those of incremental embodiments.

The shared query recognition capability is as follows. Given as input a stream of utterances {Ui, i=<NUM> to N} a parser <NUM> (be it incremental or not) can generate a stream of queries {Qk, k=<NUM> to M} where query Qk is recognized from an utterance, Ui, or from a concatenation of utterances, Ui+.

In some embodiments, input module <NUM> performs automatic speech recognition (ASR) and transcribes every identified utterance into a text segment, which is then presented as input to parser <NUM>. In this case, the stream received as input by parser <NUM> is not made of audio segments, but of text segments, or perhaps of words. Parser <NUM> operates accordingly, and one skilled in the art would readily adapt query-processing module <NUM> to work in this manner.

When parser <NUM> successfully recognizes a query, it creates as output a query data structure that includes notably the meaning of the query, expressed as an actionable data structure. The query data structure encodes not only the meaning of the query, in a form suitable for subsequent execution, but any additional data generated by parser <NUM> and useful for purposes other than execution. For convenience, the term "executing a query" will stand for "executing the query data structure" or alternatively, for "executing the actionable meaning of the query. " For example, in response to the query "what is the weather tomorrow", parser <NUM> produce as output a query data structure that encodes the core question "what is the weather" and a qualifier list including the qualifier "tomorrow. " It creates a query data structure of type "weather query" with a field "when" set to the value of "tomorrow" in the current context, time_1, and a field "where" set by default to the current location, such as city name and a latitude-longitude pair, lat_long_1. To answer the query, this command data structure might be executed by way of a procedure call WEATHER_DATA(lat_long_1, time_1) that accesses a web-based weather service.

Executing certain queries involves performing actions (e.g., creating a calendar appointment for a specified time with a specified person) or looking up information. For example, executing a weather query accesses a web-based weather service. In the example scenario above, the weather service describes tomorrow's weather as a textual description "partly cloudy, with highs in the <NUM> and lows in the <NUM>" and builds associated visuals. In some embodiments, fulfillment of a query by execution module <NUM> is performed on the same system as the query-processing module <NUM>. In some embodiments, fulfillment of a query involves a call to a remote service (e.g., a flight reservation system) through service API module <NUM>.

Fulfillment of a query might, in some cases, require a significant amount of time. In this case, processing queries in parallel is desirable. Processing module <NUM> supports the execution of multiple queries in parallel, by using multiple instances of execution module <NUM>. Of course, there is no opportunity for parallel execution of two queries Q1 and Q2 when Q1 has been answered before the start of Q2's processing. A query is called "active" if the query exists (it has been recognized by parser <NUM>, and the corresponding query data structure created) and its execution module is not complete. Completing execution produces a result record that is entered in the results queue <NUM>. The candidates for parallel execution are queries that are simultaneously active. When queries are simultaneously active, the decision to allow their parallel execution is made by the query serializer module <NUM>.

In some embodiments, the execution of a query produces a response that will be displayed to the user. For example, a request for information (e.g., "what is the weather tomorrow") inherently calls for a response to the user. In some embodiments, all queries result in a response (e.g., a request for action also produces a response that indicates whether the action succeeded or not). In the embodiment of <FIG>, this happens in two stages. First, execution of a query by an instance of execution module <NUM> creates a result record as output. Second, output manager <NUM> creates an actual response to be presented to the user, based on a result record from the results queue <NUM>. In the embodiment of <FIG>, result records created by instances of execution module <NUM> are entered into a results queue <NUM>, and output manager <NUM> pulls result records out of results queue <NUM>. The results queue <NUM> is an inter-process communication queue, and it performs a parallel to serial conversion for the generated result records.

The output manager <NUM> outputs the response data (if any) to the user. In some embodiments, the response data is output in different forms, depending on the nature of the data or of the capabilities of the client device <NUM> on which the response will ultimately be given to the user. For example, the output manager <NUM> could cause the response to be output in spoken form (e.g., via text-to-speech algorithms), or in visual form (assuming that the client device <NUM> has visual user interface capabilities). Further, each component optionally has a short form and a long form, to be used under different circumstances. In some embodiments, the output manager <NUM> also decides the order in which responses are displayed.

The service API module <NUM> contains logic that supports the execution of particular queries using API requests for network-based services. For example, the query "what is the weather tomorrow?" is executed by accessing a third-party web-based weather service. The service API module <NUM> would map the query to a URL, including parameters representing the specified weather characteristics (e.g., &time=tomorrow). In some embodiments, a choice is made among competing services with similar functionality, such as one provider of weather services,\ vs. another, for reasons including availability and price.

In the embodiment illustrated in <FIG>, queries are stored in a query queue <NUM>. Queries are entered into query queue <NUM> as soon as they are created by parser <NUM>. The query serializer module <NUM> determines which queries can be executed in parallel, and which cannot; it is discussed later. Queries are removed from queue <NUM> when their execution by execution module <NUM> has been completed. The execution of a query is complete when the corresponding result record been added to a results queue <NUM>. The results queue <NUM> is processed by output manager <NUM>.

In one embodiment, the parallel execution of queries is allowed to the largest extent possible. In such an embodiment, processing module <NUM> executes queries in parallel by running multiple instances of the execution module <NUM>. In some embodiments, two queries are executed asynchronously if possible, that is: (<NUM>) the queries are both active at the same time (the query queue <NUM> contains all active queries); and (<NUM>) there is no serial constraint to prevent the parallel execution of the two queries. The query serializer module <NUM> determines the presence of serial constraints among the active queries, as discussed below. These definitions support the parallel execution of a number of active queries. After the serial constraints among all active queries are determined, sequencing choices (deciding which queries are eligible for immediate execution) are simple: any query that has no serial dependency on a previous query is eligible to run. For example, suppose queries Q1, Q2, Q3 are active in the queue, and a serial constraint specifies that Q1 must be processed before Q3. There are no other serial constraints. In this case, queries Q1 and Q2 (that have no incoming constraints) are eligible for parallel execution. Q3 cannot run until Q1 has completed its execution. Once Q1 finishes, Q3 can execute. If Q2 is still active at that point in time, Q3 will be executing in parallel with Q2. In some embodiments, the degree of parallelism during the execution of queries is limited by other factors, such as resource limitations, that prevent parallelism from being exercised to the fullest extent possible.

Parallel execution can be implemented in many ways. In one embodiment, multiple computers are used to support the parallelism needed to execute queries from a query stream; this leads to a heavy use of computational resources. In one embodiment, multiple "processes" (in the sense of the Linux OS) are used to support parallelism in query processing. In one embodiment, a multithreaded process (again, in the sense of the Linux OS) supports multiple threads (also called light-weight processes) to enable parallelism. In another embodiment, parallelism is adequately simulated without threads, interrupts or timers, through cooperative scheduling. In a cooperative scheduler, a task (once made active) is allowed to run until it returns control voluntarily. The cooperative scheduling approach, when applicable, has the advantage of a low overhead. It requires no hardware support and can be used in small embedded systems, but query execution is usually too complex to qualify for a thread-less scheduler.

The query serializer module <NUM> has the responsibility to decide if two queries Q1 and Q2 have a dependency relationship (a serial constraint) that determines the relative order in which processing module <NUM> should process the queries. Two active queries Q1 and Q2 will be allowed to execute in parallel, unless query serializer module <NUM> determines that, say, Q1 must be executed before Q2.

Query serializer <NUM>, as its name indicates, performs serial constraint recognition on queries, not on utterances. This is because utterances are un-interpreted speech segments; thus, until they are recognized as queries, utterances do not allow the detection of dependency relationships that result in serial constraints. Still, it is convenient to refer to an utterance as if recognized as the corresponding query, and this convention is employed below. For example, it is intuitive (though technically incorrect) to refer in the same way to the utterance "what is the weather" (where the quoted text stands for a segment of speech) and to the query "what is the weather" (where the same quoted text stands for a parsed and interpreted query). Using this definition, it will be convenient to say that utterance U<NUM> is a "continuation" of U<NUM> when (<NUM>) U<NUM> is recognized as a query Q1, and (<NUM>) the concatenation U<NUM> + U<NUM> of the two successive utterances is recognized as a query Q2. According to this definition, the utterance "in Rome" is a continuation of the utterance "what is the weather tomorrow" because the concatenated utterance "what is the weather tomorrow in Rome" can be recognized as a query, according to most grammars. In contrast, the utterance "at a <NUM>% interest rate" is not a continuation of the utterance "what is the weather tomorrow" because the utterance "what is the weather tomorrow at a <NUM>% interest rate" is not recognizable as a query in most grammars.

The detection of serial constraints by query serializer <NUM> is primarily governed by logical dependencies, as explained below, but pragmatic considerations (such as computational cost, processing delays, or cost of accessing APIs) play a role as well. The query "what is the weather in Rome" can execute in parallel with the query "what is the weather" (a question about the local weather) because it is a fair speculation that the weather in Rome is independent of the local weather (in some other part of the world); and while in Rome, it would be surprising if two queries involving the local weather would be issued in succession. Speculating (or verifying by looking at geolocation distances) that the weather in Rome and the local weather do not have a logical dependency, they can be executed in parallel.

For another example of serial constraint recognition, consider the utterance "Find Chinese restaurants near my hotel" followed by the utterance "and sort them by rating. " In this example, the second utterance is a continuation of the first one, but unlike the previous example, the answer to the second query is best seen as based on the answer to the first one, hence a serial constraint is detected, preventing parallel execution. (Although it is possible to execute the two queries "Find Chinese restaurants near my hotel" and "Find Chinese restaurants near my hotel and sort them by rating" in parallel, this is less computationally efficient.

In one embodiment, the query serializer <NUM> is tightly integrated with parser <NUM>, and determines whether an ordering dependency is present between queries Q1 and Q2 based on the state of the parser during query recognition. The previous example shows that the response to a query Q2 can depend on the response to a prior query Q1 whenever Q2 adds a qualifier ("and sort them by rating") to an information-seeking query ("Find Chinese restaurants near my hotel"). For another example, the utterance "with four stars" is a continuation of the prior utterance "Show me hotels in downtown San Jose", and serves as a qualifier to it, in that it selects the subset of hotels in downtown San Jose that have four-star ratings.

In some embodiments, an ordering constraint is detected when query serializer <NUM> determines that a new query depends on the context of previous dialog. In some embodiments, this happens if a new query depends on the answer to the previous query (as in the hotel example given above) or more broadly, a recent query. Another example of a result dependency is the query "what is the temperature in Chicago?" followed by "is it colder in New York?" Here, the term "colder" implicitly refers to a previous temperature, which one expects to find mentioned in the previous dialog. Here, the previous temperature is not known until the first query has been executed. This is a logical (i.e., data flow) dependency, which results in a serial constraint between the queries, preventing parallel execution.

In one embodiment, dependency checking is based on maintaining and accessing a representation of the conversation state (a data structure that holds selected information from the previous dialog). Some queries are dependent on conversation state, but this does not imply a serial constraint. When a new query depends on a recent query, but not on the answer to it, a serial dependency might not exist. For example, in order to determine the meaning of an unresolved query such as "How is the weather there?" one must know what the location there stands for. Co-reference resolution techniques address this problem. The absence of the location information might or might not be grounds for a serial constraint. For example, if one says "Find me a flight to New York on Saturday" followed by "How is the weather there?" there is no serial constraint, because the location can be resolved using the previous dialog data (the conversation state) when parsing the second query. Conversely, if one says "What town was President Obama born in" followed by "How is the weather there?" there is a serial constraint, because the needed location can only be resolved by executing the first query.

To respond to another semantically incomplete query such as "what if the interest rate is <NUM>%?", in some embodiments the recent dialog is examined for a prior query that involves the use of an interest rate, and a previously used formula (such as in a mortgage calculation) is found that depends on a specified interest rate. In some embodiments, the formula is then be re-evaluated with the <NUM>% interest rate substituted for the previous rate. In such a case, there is no serial constraint. Stated more generally, if a dependence of an incomplete query upon recent interactions can be handled at recognition time (e.g., because the result is within the conversation state, or within the cached result of a prior query), as opposed to requiring query execution to obtain a result, there is no serial constraint, and parallelism is allowed at query execution time.

The recognition of a query can determine reliably whether the execution of a query depends on that of previous queries. One way this happen is through shared values. After the query "How is the weather there?" is answered, a temperature is expected as part of the answer. A subsequent query "what is that in Celsius?" calls for using a temperature value from the prior weather query's answer, creating a serial constraint between the corresponding queries.

Parallel to serial conversion is achieved by way of the results queue <NUM>. The queue receives result records asynchronously from parallel instances of the execution module <NUM>, and the single-threaded output manager <NUM> handles the queue serially. An entry in the results queue <NUM> (a result record) can be complex. In some embodiments, it includes multiple multimedia components, such as a short text response or a long text response for display on a screen, a short text response with text-to-speech (TTS) markup or a long text response with TTS markup to convert the text to speech audio using the client's TTS software, a short speech response or long speech response already converted from text to audio using the server's TTS software. Besides, in some embodiments, it also includes pure audio or music segments, video segments with or without audio, graphic elements, animations, and metadata about the visual or auditory display of all such components. In addition, in some embodiments, a result record entry conveys scripts, rules or constraints that apply to the preferred use of the result record by the output manager <NUM>.

The following is a more detailed description of various embodiments of the output manager <NUM>. In some embodiments, A result record contains multiple content components, such as text for printing, text with markup for conversion to audio by a TTS module, recorded audio to be played; visual elements for display including static images, animations or video to be played; and generally any multimedia content accessible by way of external references such as URLs. In some embodiments, a result record also includes details about what parts of the multimedia data should be displayed under specific circumstances. In some embodiments, output manager <NUM> selects specific multimedia content that users see or hear. In some embodiments, the choice is left in whole or part to a client device.

The output manager <NUM> is primarily responsible for the order of presentation of the content. When processing queries in parallel, result records are received by results queue <NUM> in an order that differs from the original query order. In some embodiments, constraints are specified by the result records so as to constrain the order or timing of delivering multimedia content to the user. The output manager <NUM> has control of a user's audio-visual output devices, notably in terms of sequencing events and screen space management.

The output manager <NUM> is single-threaded, for any single user. This ensures that the user experiences result in a controlled time order. Although result records are obtained asynchronously, results queue <NUM> serializes them. Output manager <NUM> is then able, by reading the results queue <NUM>, to access the entire sequence of active results. This means in particular that the output manager <NUM> does not have to pull result records out of the results queue <NUM> in the order of the queue.

Some embodiments of the results queue <NUM> use shared memory to store the result records themselves, and only store pointers to the shared result records in an inter-process queue. Various ways to implement inter-process queues are known to ordinarily skilled practitioners. Some embodiments are based on pointers and links. Some embodiments use a circular array with a head index and a tail index. This is feasible if the queue elements have a fixed element size (result records generally have varying sizes, but pointers to them have fixed sizes) and the queue has a fixed maximum size, which is typically adequate since a small degree of parallelism is sufficient for most applications. In a circular array embodiment, output manager <NUM> is able to peek at elements other than the head of the queue in order to better inform scheduling choices.

The output manager <NUM> takes scheduling constraints into account. In some embodiments, such constraints are known through global defaults. For example, output audio segments may not overlap in time, unless otherwise specified. The non-overlapping rule applies by default both to audio from TTS and to music audio or other recorded audio. However, some audio (such as soft background music, or special-purpose sounds) may escape the rule and be played while foreground audio is playing. In some embodiments, constraints other than the default constraints are specified as part of the multimedia result records. In some embodiments, the distinction of background audio (not subject to the non-overlapping rule) is specified in a result record.

In some embodiments, scheduling constraints distinguish between two types of events: instantaneous events and continuous events. A frequent type of constraint is a temporal relationship between events. A "time synchronicity" constraint states that a specified point in time (an instantaneous event, or the beginning or end of a continuous event) should be scheduled synchronously with another point in time (similarly defined by reference to events). A "precedence" constraint states that an event must be scheduled before (or after) another event. Continuous events have a time extent. Constraints can state that a continuous event is interruptible, or not interruptible, under certain conditions. A constraint can state the conditions under which a continuous event may be overlapped with other events. For example, the default constraint associated with a TTS audio segment is that it cannot be overlapped with any another audible event, unless said event is background music that is played relatively softly according to some appropriate definition of relative loudness. Continuous events include:.

Instantaneous events are subject to synchronicity and precedence constraints. They include:.

In some embodiments, the set of constraints given to the output manager <NUM> is over-determined or under-determined. In some embodiments, in order to process constraint sets and detect over-determined ones, the output manager <NUM> relies on simple procedures, such as the following sequence of steps:.

In the absence of sufficient constraints from result records to determine a schedule, the output manager <NUM> will add further constraints of its own to complete a schedule. In doing so, it typically falls back on default strategies. In the absence of constraints to the contrary, audio segments (including the audio from TTS) are played in order of the result records in the queue. This is the default strategy. This order is not always identical to the original order of the queries. In the latter case, the output manager <NUM> optionally peeks into the queue, after a short wait, in an attempt to preserve the original ordering.

For managing visual displays, one strategy that the output manager <NUM> can use is to replace a visual element (text or graphic) by another. In this case, the use of timing constraints allows sufficient time for users to read the display. Another strategy is scrolling. It is applicable when the display device has a scrolling area. A display device can be divided into multiple areas, each of which can be designated as scrolling or as non-scrolling. In some embodiments a scrolling area scrolls upwards, or downwards. In some embodiments, a scrolling area scrolls leftward and rightward. A request to display a visual element is aimed at a specific area, which can be scrolling or not. In either case, the request optionally requires that the area be cleared before the display.

In some embodiments, scrolling adds a visual element at the top (or bottom) of a scrolling area, shifting other visual elements away as far as needed to make room for the new element. When the scrolling area gets full, visual elements are scrolled off the area and are no longer visible. Choices pertaining to a scrolling method, such as the direction of scrolling or whether the area is cleared when full, are made globally in some embodiments, and are driven by query-specific constraints in some embodiments. Two main variations of scrolling differ by the retention of scrolled data: in the "forgetting" variant, data that goes offscreen is cleared from internal memory and cannot be retrieved. In the 'remembering' variant, visual material that was scrolled off the screen is stored in a buffer, and can be scrolled back into view by swiping or other means. In some 'remembering' embodiments, the amount of buffering is specified in a constraint, as well as circumstances for clearing buffer data.

As discussed above, audio segments are played in a specific order determined by the output manager <NUM> (whether or not the order of the audio segments matches that of the queries that elicited them as response) but audio segments are played exactly once, in an order that is not specified by the user. An alternative embodiment offers persistent access to audio segments, allowing a user to play a "persistent" audio segment zero or more times. For an audio segment designated as persistent, the output manager <NUM> adds a visual GUI element that is associated with the audio segment. The visual element is a clickable or tappable area of a screen, or equivalent. By clicking or tapping the visual element, a user can trigger the playback of the corresponding audio segment. This is specifically useful when audio has been interrupted, or is played out of order. In some embodiments, such visual elements are displayed in a scrolling area of the forgetting or remembering type, according to an operating mode of the output manager <NUM>. Such modes are optionally driven by defaults corresponding to each multimedia element type (text, audio, video, static graphic, animation) or by constraints attached to result records to a specific query, or a combination of both. In some embodiments, an audio or video segment is only played when a corresponding GUI element is tapped. This is useful after a short response such as written text or TTS audio has already been given, in order to give a user the option to get more details.

<FIG> are diagrams illustrating the handling of user speech over time in different scenarios, according to various embodiments. Time is illustrated along the horizontal axis, and the different operations (listening, user speech, execution, and response) are illustrated in different portions of the vertical axis. The "listening" operation represents the input module <NUM> of <FIG> receiving and segmenting input audio data for inclusion as new speech utterances in the utterance stream. The "user speech" operation represents a user providing spoken input to the client device <NUM>, thereby creating utterance stream input data. The "processing" operation represents the processing of the processing module <NUM> of <FIG> (recognition <NUM> and execution <NUM>). The "response" operation represents the creation of response data by the output manager <NUM>. Multimedia response data can be presented to users in spoken or visual form as well as other forms, but in <FIG> they are depicted as time segments.

<FIG> illustrates a "half-duplex" embodiment, not encompassed by the wording of the claims but considered as useful for understanding the invention, where the input module <NUM> from <FIG> ignores additional input during the processing of a query and while outputting a response. Specifically, at time t<NUM> when a user begins utterance U, the input module <NUM> is listening for audio data defining an utterance. At a time t<NUM>, shortly after the user ends the utterance at time t<NUM>, the input module <NUM> determines that the utterance has ended and accordingly (<NUM>) stops listening for additional utterance stream input data, and (<NUM>) begins processing of the identified utterance U (that is, the audio input data between times t<NUM> and t<NUM>). When processing of the utterance U has ended at time t<NUM>, the output manager <NUM> outputs a response based on the results of processing (e.g., if U is a request for information, the response represents the requested information), such as in audio or visual form. When the output of the response ends at t<NUM>, only then does the input module <NUM> again begin to listen for an additional utterance.

<FIG> illustrates the loss of utterance data resulting from the embodiment of <FIG>. At time t<NUM>' between t<NUM> and t<NUM>, while the processing is taking place, the user begins an additional utterance U<NUM> lasting until t<NUM>' (or, as alternative example, an utterance U<NUM> lasting until t<NUM>'). Since the input module <NUM> does not recommence listening until t<NUM>, all of utterance U<NUM> is lost (or, in the alternative example, all of U<NUM> is lost except the portion between t<NUM> and t<NUM>').

<FIG> illustrates continuous listening, and abortive action with respect to a first utterance in response to a second utterance, according to some embodiments. When the input module has recognized by time t<NUM> that an utterance U<NUM> has ended, the processing module <NUM> begins processing U<NUM>. At a time t<NUM>, while processing of U<NUM> is still taking place, the user begins a second utterance, U<NUM>. (For example, a user says "what is the weather tomorrow" (U<NUM>), pauses, and continues with "in Rome?" (U<NUM>). ) In some embodiments, the processing module <NUM> terminates processing P<NUM> before it completes due to the detection of the beginning of U<NUM>; in other embodiments, the processing module <NUM> completes the processing of U<NUM>, but also continues listening to U<NUM> while processing P<NUM> is taking place, so that it can perform processing P<NUM> on U<NUM> when U<NUM> is complete.

In the embodiment of <FIG>, response R is provided after P<NUM> completes. If, after recognizing U<NUM>, the query serializer <NUM> of <FIG> determines that U<NUM> is a continuation of U<NUM>, the processing module <NUM> executes the query resulting from the concatenation of U<NUM> and U<NUM>, and provides a response R based on this query, discarding any prior response based solely on P<NUM> by refraining from outputting such a prior response.

If, in contrast, the processing module <NUM> determines that U<NUM> was not a continuation of U<NUM>, then the processing module <NUM> processes U<NUM> separately from U<NUM>. Continuing a prior example, if U<NUM> were "what is the weather tomorrow" and U<NUM> were "Create a calendar appointment", then in some embodiments response R includes responses to both U<NUM> and U<NUM> (e.g., a description of the weather, and a statement of whether the calendar appointment creation was successful). In other embodiments (not illustrated in <FIG>), the response manager <NUM> begins outputting a response based on processing P<NUM> before outputting the response R based on P<NUM>, e.g., as soon as processing P<NUM> determines that U<NUM> is not a continuation of U<NUM>.

Due to network latency or operating system inter-process communication latency it is possible that processing of the first utterance will, in some cases, begin after a user begins a second utterance. The system ensures that the second utterance will not be lost. The processing module <NUM> must behave appropriately when it eventually receives the audio for the second utterance. In some cases, an appropriate behavior is to cancel the processing P<NUM>. In some cases, an appropriate behavior is to allow P<NUM> to finish, then discard its result. In some cases, an appropriate behavior is to provide the results of P<NUM> independently of the results of processing P<NUM>.

<FIG> illustrates parallel query processing, according to one embodiment. After the beginning of processing P<NUM> of U<NUM> at t<NUM>, a second utterance (U<NUM>) begins. The processing module <NUM> continues processing P<NUM> of U<NUM>, and after completion of U<NUM> begins processing P<NUM> of U<NUM>. (If the processing module <NUM> had determined that U<NUM> was a continuation of U<NUM>, in some embodiments it would instead have processed the concatenation of U<NUM> and U<NUM>. ) Processing module <NUM> performs steps P<NUM> and P<NUM> in separate threads of execution so that they can occur in parallel, as they do between times t<NUM> and t<NUM> in the example of <FIG>. The response module <NUM> outputs the responses corresponding to U<NUM> and U<NUM> (namely, R<NUM> and R<NUM>, respectively) directly after the respective processing (namely, P<NUM> and P<NUM>) has completed.

<FIG> illustrates out-of-order outputting of query responses based on the order of processing completion, according to one embodiment. Although utterance U<NUM> begins before U2, and processing P<NUM> for U<NUM> accordingly begins before P<NUM> for U<NUM>, P<NUM> completes earlier than P<NUM> (e.g., because P<NUM> is more computationally-intensive, or requires use of an external service with greater latency, or the like). Accordingly, the response R<NUM> for U<NUM> is output earlier (starting at t<NUM>) than the response R<NUM> for U<NUM> (starting at t<NUM>). In the embodiment illustrated in <FIG>, the output manager <NUM> delays beginning providing the response R<NUM> until time t<NUM>, when the outputting of R<NUM> completes, even though the response R<NUM> was ready at earlier time t<NUM>. Such a delay is beneficial where the responses are output in a manner for which outputting overlapping responses would be distracting, such as when the responses are output audibly. In other embodiments (not illustrated in <FIG>), or for situations where the different responses can be output at overlapping times without being distracting to users (e.g., where the responses are output visually in different portions of a visual user interface), responses ready at a later time need not be delayed until earlier responses are fully output.

<FIG> illustrates an embodiment in which the responses are output in an order corresponding to the order in which their corresponding utterances were received. That is, since U<NUM> was received before U<NUM>, corresponding response R<NUM> is output before R<NUM>, with the output manager <NUM> delaying the outputting of any responses until after the processing P<NUM> of the first utterance U<NUM> has completed. Providing results in the same ordering as their corresponding utterances is valuable in some instances, such as when the results are of similar types and not readily distinguishable to the user, e.g., where both U<NUM> and U<NUM> pertain to the state of the weather. As noted above, in some embodiments or scenarios, R<NUM> could be output partially or entirely overlapping with R<NUM> in time. This is acceptable, for example, in situations where the results are of disparate types and hence readily distinguishable to the user, such as when the first result is about the weather and the second result is about a population count.

<FIG> illustrates the delaying of providing a second response based on a dependency between consecutive utterances, according to one embodiment. After the input module <NUM> detects the end of utterance U<NUM>, the processing module <NUM> optionally determines by time t<NUM> (e.g., by successfully attempting to parse the concatenation U<NUM>+U<NUM>) that U<NUM> is a continuation of U<NUM>, and that the response to U<NUM> will depend on the response to U<NUM>, as discussed above with respect to the query serializer. Accordingly, the processing module <NUM> delays the execution phase of processing until P<NUM> has completed (and response R<NUM> has accordingly been computed). Once P<NUM> has completed at time t<NUM>, the processing module performs the execution phase of processing P<NUM>, basing the response R<NUM> on the response R<NUM>, and outputting R<NUM> at t<NUM>, when P<NUM> has completed.

<FIG> illustrates the interruption of a response, according to one embodiment. The parser <NUM> handles utterance U<NUM> creating query Q1, processed in stage P<NUM>, producing response R<NUM> by time t<NUM>. The output manager <NUM> begins to output the response R<NUM> at time t<NUM>. At a later time t<NUM>, the input module <NUM> detects a second utterance U<NUM> and U<NUM>+U<NUM> is parsed as query Q<NUM>. In an embodiment, the logic relationship of Q<NUM> and Q<NUM> is analyzed by query serializer <NUM> before it causes the output manager <NUM> to halt the outputting of response R<NUM> at time t<NUM>, instead processing U<NUM>+U<NUM> starting at time t<NUM> and outputting result R<NUM> at time t<NUM>. Accordingly, the output manager <NUM> halts the outputting of R<NUM>. For example, if U<NUM> were the phrase "what is the weather tomorrow", the output module begins to output a description of tomorrow's forecasted weather for the user's current location, but if U<NUM> were the phrase "in Rome", the weather tomorrow at the user's current location would be irrelevant (assuming that the user is not in or near Rome), and so the output manager <NUM> would cease outputting the description of the local weather.

<FIG> is a high-level block diagram illustrating physical components of a computer <NUM> used as part or all of the query-processing server <NUM> or client device <NUM> from <FIG>, according to one embodiment. Illustrated are at least one processor <NUM> coupled to a chipset <NUM>. Also coupled to the chipset <NUM> are a memory <NUM>, a storage device <NUM>, a keyboard <NUM>, a graphics adapter <NUM>, a pointing device <NUM>, and a network adapter <NUM>. A display <NUM> is coupled to the graphics adapter <NUM>. In one embodiment, the functionality of the chipset <NUM> is provided by a memory controller hub <NUM> and an I/O controller hub <NUM>. In another embodiment, the memory <NUM> is coupled directly to the processor <NUM> instead of the chipset <NUM>.

The storage device <NUM> is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory <NUM> holds instructions and data used by the processor <NUM>. The pointing device <NUM> is optionally a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard <NUM> to input data into the computer <NUM>. The graphics adapter <NUM> displays images and other information on the display <NUM>. The network adapter <NUM> couples the computer <NUM> to a local or wide area network.

As is known in the art, a computer <NUM> can have different and/or other components than those shown in <FIG>. In addition, the computer <NUM> can lack certain illustrated components. In some embodiments, a computer <NUM> acting as a server lacks a keyboard <NUM>, pointing device <NUM>, graphics adapter <NUM>, and/or display <NUM>. Moreover, the storage device <NUM> can be local and/or remote from the computer <NUM> (such as embodied within a storage area network (SAN)).

As is known in the art, the computer <NUM> is adapted to execute computer program modules for providing functionality described herein. As used herein, the term "module" refers to computer program logic utilized to provide the specified functionality. Thus, a module can be implemented in hardware, firmware, and/or software. In one embodiment, program modules are stored on the storage device <NUM>, loaded into the memory <NUM>, and executed by the processor <NUM>.

Reference in the specification to "one embodiment" or to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment. The indefinite article "a" and "an" should be understood to include both the singular and the plural as appropriate for the context; thus "a block" should be understood for example to mean "at least one block".

It should be noted that the process steps and instructions are embodied in software, firmware or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by a variety of operating systems.

The operations herein can also be performed by an apparatus. Furthermore, the computers referred to in the specification optionally include a single processor or optionally are architectures employing multiple processor designs for increased computing capability. It will be appreciated that a variety of programming languages can be used to implement the teachings of the present disclosure as described herein, and any references below to specific languages are provided for disclosure of enablement of the present disclosure.

While the disclosure has been particularly shown and described with reference to a preferred embodiment and several alternate embodiments, it will be understood by persons skilled in the relevant art that various changes in form and details can be made therein without departing from the scope of appended claims.

Claim 1:
A computer-implemented method of generating a response to a spoken input, the method comprising:
obtaining an audio input stream;
detecting in the audio input stream a beginning of a first utterance (U<NUM>);
detecting in the audio input stream an end of the first utterance;
responsive to detecting the end of the first utterance, initiating processing of the first utterance to recognize a first query, wherein recognizing the first query is conditional on the presence of an end-of-utterance boundary at the end of the first query; and
while processing the first utterance:
continuing to receive the audio input stream; and
detecting a beginning of a second utterance (U<NUM>) in the audio stream.