Patent Grant
11361756
The present technology relates to consumer goods and, more particularly, to methods, systems, products, features, services, and other elements directed to voice-assisted control of media playback systems or some aspect thereof.
Options for accessing and listening to digital audio in an out-loud setting were limited until in 2002, when SONOS, Inc. began development of a new type of playback system. Sonos then filed one of its first patent applications in 2003, entitled “Method for Synchronizing Audio Playback between Multiple Networked Devices,” and began offering its first media playback systems for sale in 2005. The Sonos Wireless Home Sound System enables people to experience music from many sources via one or more networked playback devices. Through a software control application installed on a controller (e.g., smartphone, tablet, computer, voice input device), one can play what she wants in any room having a networked playback device. Media content (e.g., songs, podcasts, video sound) can be streamed to playback devices such that each room with a playback device can play back corresponding different media content. In addition, rooms can be grouped together for synchronous playback of the same media content, and/or the same media content can be heard in all rooms synchronously.
Features, aspects, and advantages of the presently disclosed technology may be better understood with regard to the following description, appended claims, and accompanying drawings where:
Features, aspects, and advantages of the presently disclosed technology may be better understood with regard to the following description, appended claims, and accompanying drawings, as listed below. A person skilled in the relevant art will understand that the features shown in the drawings are for purposes of illustrations, and variations, including different and/or additional features and arrangements thereof, are possible.
The drawings are for purposes of illustrating example embodiments, but it should be understood that the inventions are not limited to the arrangements and instrumentality shown in the drawings. In the drawings, identical reference numbers identify at least generally similar elements. To facilitate the discussion of any particular element, the most significant digit or digits of any reference number refers to the Figure in which that element is first introduced. For example, element 103a is first introduced and discussed with reference to
Example techniques described herein involve wake-word engines configured to detect commands. An example network microphone device (“NMD”) may implement such a wake-word engine in parallel with a wake-word engine that invokes a voice assistant service (“VAS”). While a VAS wake-word engine may be involved with nonce wake-words, a command keyword engine is invoked with commands, such as “play” or “skip.”
Network microphone devices may be used facilitate voice control of smart home devices, such as wireless audio playback devices, illumination devices, appliances, and home-automation devices (e.g., thermostats, door locks, etc.). An NMD is a networked computing device that typically includes an arrangement of microphones, such as a microphone array, that is configured to detect sound present in the NMD's environment. In some examples, an NMD may be implemented within another device, such as an audio playback device.
A voice input to such an NMD will typically include a wake word followed by an utterance comprising a user request. In practice, a wake word is typically a predetermined nonce word or phrase used to “wake up” an NMD and cause it to invoke a particular voice assistant service (“VAS”) to interpret the intent of voice input in detected sound. For example, a user might speak the wake word “Alexa” to invoke the AMAZON® VAS, “Ok, Google” to invoke the GOOGLE® VAS, “Hey, Ski” to invoke the APPLE® VAS, or “Hey, Sonos” to invoke a VAS offered by SONOS®, among other examples. In practice, a wake word may also be referred to as, for example, an activation-, trigger-, wakeup-word or -phrase, and may take the form of any suitable word, combination of words (e.g., a particular phrase), and/or some other audio cue.
To identify whether sound detected by the NMD contains a voice input that includes a particular wake word, NMDs often utilize a wake-word engine, which is typically onboard the NMD. The wake-word engine may be configured to identify (i.e., “spot” or “detect”) a particular wake word in recorded audio using one or more identification algorithms. Such identification algorithms may include pattern recognition trained to detect the frequency and/or time domain patterns that speaking the wake word creates. This wake-word identification process is commonly referred to as “keyword spotting.” In practice, to help facilitate keyword spotting, the NMD may buffer sound detected by a microphone of the NMD and then use the wake-word engine to process that buffered sound to determine whether a wake word is present in the recorded audio.
When a wake-word engine detects a wake word in recorded audio, the NMD may determine that a wake-word event (i.e., a “wake-word trigger”) has occurred, which indicates that the NMD has detected sound that includes a potential voice input. The occurrence of the wake-word event typically causes the NMD to perform additional processes involving the detected sound. With a VAS wake-word engine, these additional processes may include extracting detected-sound data from a buffer, among other possible additional processes, such as outputting an alert (e.g., an audible chime and/or a light indicator) indicating that a wake word has been identified. Extracting the detected sound may include reading out and packaging a stream of the detected-sound according to a particular format and transmitting the packaged sound-data to an appropriate VAS for interpretation.
In turn, the VAS corresponding to the wake word that was identified by the wake-word engine receives the transmitted sound data from the NMD over a communication network. A VAS traditionally takes the form of a remote service implemented using one or more cloud servers configured to process voice inputs (e.g., AMAZON's ALEXA, APPLE's SIRI, MICROSOFT's CORTANA, GOOGLE'S ASSISTANT, etc.). In some instances, certain components and functionality of the VAS may be distributed across local and remote devices.
When a VAS receives detected-sound data, the VAS processes this data, which involves identifying the voice input and determining intent of words captured in the voice input. The VAS may then provide a response back to the NMD with some instruction according to the determined intent. Based on that instruction, the NMD may cause one or more smart devices to perform an action. For example, in accordance with an instruction from a VAS, an NMD may cause a playback device to play a particular song or an illumination device to turn on/off, among other examples. In some cases, an NMD, or a media system with NMDs (e.g., a media playback system with NMD-equipped playback devices) may be configured to interact with multiple VASes. In practice, the NMD may select one VAS over another based on the particular wake word identified in the sound detected by the NMD.
One challenge with traditional wake-word engines is that they can be prone to false positives caused by “false wake word” triggers. A false positive in the NMD context generally refers to detected sound input that erroneously invokes a VAS. With a VAS wake-work engine, a false positive may invoke the VAS, even though there is no user actually intending to speak a wake word to the NMD.
For example, a false positive can occur when a wake-word engine identifies a wake word in detected sound from audio (e.g., music, a podcast, etc.) playing in the environment of the NMD. This output audio may be playing from a playback device in the vicinity of the NMD or by the NMD itself. For instance, when the audio of a commercial advertising AMAZON's ALEXA service is output in the vicinity of the NMD, the word “Alexa” in the commercial may trigger a false positive. A word or phrase in output audio that causes a false positive may be referred to herein as a “false wake word.”
In other examples, words that are phonetically similar to an actual wake word cause false positives. For example, when the audio of a commercial advertising LEXUS® automobiles is output in the vicinity of the NMD, the word “Lexus” may be a false wake word that causes a false positive because this word is phonetically similar to “Alexa.” As other examples, false positives may occur when a person speaks a VAS wake word or phonetically similar word in conversation.
The occurrences of false positives are undesirable, as they may cause the NMD to consume additional resources or interrupt audio playback, among other possible negative consequences. Some NMDs may avoid false positives by requiring a button press to invoke the VAS, such as on the AMAZON FIRETV remote or the APPLE TV remote. In practice, the impact of a false positive generated by a VAS wake-word engine is often partially mitigated by the VAS processing the detected-sound data and determining that the detected-sound data does not include a recognizable voice input.
In contrast to a pre-determined nonce wake word that invokes a VAS, a keyword that invokes a command (referred to herein as a “command keyword”) may be a word or a combination of words (e.g., a phrase) that functions as a command itself, such as a playback command. In some implementations, a command keyword may function as both a wake word and the command itself. That is, when a command keyword engine detects a command keyword in recorded audio, the NMD may determine that a command keyword event has occurred and responsively performs a command corresponding to the detected keyword. For instance, based on detecting the command keyword “pause,” the NMD causes playback to be paused. One advantage of a command keyword engine is that the recorded audio does not necessarily need to be sent to a VAS for processing, which may result in a quicker response to the voice input as well as increased user privacy, among other possible benefits. In some implementations described below, a detected command keyword event may cause one or more subsequent actions, such as local natural language processing of a voice input. In some implementations, a command keyword event may be one condition among one or more other conditions that must be detected before causing such actions.
According to example techniques described herein, after detecting a command keyword, example NMDs may generate a command keyword event (and perform a command corresponding to the detected command keyword) only when certain conditions corresponding to the detected command keyword are met. For instance, after detecting the command keyword “skip,” an example NMD generates a command keyword event (and skips to the next track) only when certain playback conditions indicating that a skip should be performed are met. These playback conditions may include, for example, (i) a first condition that a media item is being played back, (ii) a second condition that a queue is active, and (iii) a third condition that the queue includes a media item subsequent to the media item being played back. If any of these conditions are not satisfied, the command keyword event is not generated (and no skip is performed).
By requiring both (a) detection of a command keyword and (b) certain conditions corresponding to the detected command keyword before generating a command keyword event, the prevalence of false positives may be reduced. For instance, when playing TV audio, dialogue or other TV audio would not have the potential to generate false positives for the “skip” command keyword since the TV audio input is active (and not a queue). Moreover, the NMD can continually listen for command keywords (rather than requiring a button press to put the NMD in condition to receive a voice input) as the conditions relating to the state of the controlled device gate wake word event generation.
Aspects of conditioning keyword events may also be applicable to VAS wake-word engines and other traditional nonce wake-word engines. For example, such conditioning can possibly make practicable other wake word engines in addition to command keyword engines that might otherwise be prone to false positives. For instance, an NMD may include a streaming audio service wake word engine that supports certain wake words unique to the streaming audio service. For instance, after detecting a streaming audio service wake word, an example NMD generates a streaming audio service wake word event only when certain streaming audio service are met. These playback conditions may include, for example, (i) an active subscription to the streaming audio service and (ii) audio tracks from the streaming audio service in a queue, among other examples.
Further, a command keyword may be a single word or a phrase. Phrases generally include more syllables, which generally make the command keyword more unique and easier to identify by the command keyword engine. Accordingly, in some cases, command keywords that are phrases may be less prone to false positive detections. Further, using a phrase may allow more intent to be incorporated into the command keyword. For instance, a command keyword of “skip forward” signals that a skip should be forward in a queue to a subsequent track, rather than backward to a previous track.
Yet further, an NMD may include a local natural language unit (NLU). In contrast to a NLU implemented in one or more cloud servers that is capable of recognizing a wide variety of voice inputs, example local NLUs are capable of recognizing a relatively small library of keywords (e.g., 10,000 words and phrases), which facilitates practical implementation on the NMD. When the command keyword engine generates a command keyword event after detecting a command keyword in a voice input, the local NLU may process a voice utterance portion of the voice input to look for keywords from the library and determine an intent from the found keywords.
If the voice utterance portion of the voice input includes at least one keyword from the library, the NMD may perform the command corresponding to the command keyword according to one or more parameters corresponding to the least one keyword. In other words, the keywords may alter or customize the command corresponding to the command keyword. For instance, the command keyword engine may be configured to detect “play” as a command keyword and the local NLU library could include the phrase “low volume.” Then, if the user speaks “Play music at low volume” as a voice input, the command keyword engine generates a command keyword event for “play” and uses the keyword “low volume” as a parameter for the “play” command. Accordingly, the NMD not only causes playback based on this voice input, but also lowers the volume.
Example techniques involve customizing the keywords in the library to users of the media playback system. For instance, the NMD may populate the library using names (e.g., zone names, smart device names, and user names) that have been configured in the media playback system. Yet further, the NMD may populate the local NLU library with names of favorite playlists, Internet radio stations, and the like. Such customization allows the local NLU to more efficiently assist the user with voice commands. Such customization may also be advantageous because the size of the local NLU library can be limited.
One possible advantage of a local NLU is increased privacy. By processing voice utterances locally, a user may avoid transmitting voice recordings to the cloud (e.g., to servers of a voice assistant service). Further, in some implementations, the NMD may use a local area network to discover playback devices and/or smart devices connected to the network, which may avoid providing this data to the cloud. Also, the user's preferences and customizations may remain local to the NMD(s) in the household, perhaps only using the cloud as an optional backup. Other advantages are possible as well.
As noted above, example techniques related to command keywords. A first example implementation involves a device including a network interface, one or more processors, at least one microphone configured to detect sound, at least one speaker, a wake-word engine configured to receive input sound data representing the sound detected by the at least one microphone and generate a voice assistant service (VAS) wake word event when the wake-word engine detects a VAS wake word in the input sound data, wherein the device streams sound data representing the sound detected by the at least one microphone to one or more servers of the voice assistant service when the VAS wake word event is generated, and a command keyword engine configured to receive input sound data representing the sound detected by the at least one microphone and generate a command keyword event when (a) the second wake-word engine detects, in the input sound data, one of a plurality of command keywords supported by the second wake-word engine and (b) one or more playback conditions corresponding to the detected command keyword are satisfied, wherein each command keyword of the plurality of command keywords is a respective playback command. The device detects, via the command keyword engine, a first command keyword, and determines whether one or more playback conditions corresponding to the first command keyword are satisfied. Based on (a) detecting the first command keyword and (b) determining that the one or more playback conditions corresponding to the first command keyword are satisfied, the device generates, via the command keyword engine, a command keyword event corresponding to the first command keyword. In response to the command keyword event and determining that the one or more playback conditions are satisfied, the device performs a first playback command corresponding to the first command keyword.
A second example implementation involves a device including a network interface, one or more processors, at least one microphone configured to detect sound, at least one speaker, a wake-word engine configured to receive input sound data representing the sound detected by the at least one microphone and generate a voice assistant service (VAS) wake word event when the wake-word engine detects a VAS wake word in the input sound data, wherein the device streams sound data representing the sound detected by the at least one microphone to one or more servers of the voice assistant service when the VAS wake word event is generated, a command keyword engine configured to receive input sound data representing the sound detected by the at least one microphone. The device detects, via the command keyword engine, a first command keyword that is one a plurality of command keywords supported by the device, and determines, via a local natural language unit (NLU), an intent based on the least one keyword. After detecting the first command keyword event and determining the intent, the device performs a first playback command corresponding to the first command keyword according to the determined intent.
While some embodiments described herein may refer to functions performed by given actors, such as “users” and/or other entities, it should be understood that this description is for purposes of explanation only. The claims should not be interpreted to require action by any such example actor unless explicitly required by the language of the claims themselves.
Moreover, some functions are described herein as being performed “based on” or “in response to” another element or function. “Based on” should be understood that one element or function is related to another function or element. “In response to” should be understood that one element or function is a necessary result of another function or element. For the sake of brevity, functions are generally described as being based on another function when a functional link exists; however, such disclosure should be understood as disclosing either type of functional relationship.
Within these rooms and spaces, the MPS 100 includes one or more computing devices. Referring to
With reference still to
As further shown in
In some implementations, the various playback devices, NMDs, and/or controller devices 102-104 may be communicatively coupled to at least one remote computing device associated with a VAS and at least one remote computing device associated with a media content service (“MCS”). For instance, in the illustrated example of
As further shown in
In various implementations, one or more of the playback devices 102 may take the form of or include an on-board (e.g., integrated) network microphone device. For example, the playback devices 102a-e include or are otherwise equipped with corresponding NMDs 103a-e, respectively. A playback device that includes or is equipped with an NMD may be referred to herein interchangeably as a playback device or an NMD unless indicated otherwise in the description. In some cases, one or more of the NMDs 103 may be a stand-alone device. For example, the NMDs 103f and 103g may be stand-alone devices. A stand-alone NMD may omit components and/or functionality that is typically included in a playback device, such as a speaker or related electronics. For instance, in such cases, a stand-alone NMD may not produce audio output or may produce limited audio output (e.g., relatively low-quality audio output).
The various playback and network microphone devices 102 and 103 of the MPS 100 may each be associated with a unique name, which may be assigned to the respective devices by a user, such as during setup of one or more of these devices. For instance, as shown in the illustrated example of
As discussed above, an NMD may detect and process sound from its environment, such as sound that includes background noise mixed with speech spoken by a person in the NMD's vicinity. For example, as sounds are detected by the NMD in the environment, the NMD may process the detected sound to determine if the sound includes speech that contains voice input intended for the NMD and ultimately a particular VAS. For example, the NMD may identify whether speech includes a wake word associated with a particular VAS.
In the illustrated example of
Upon receiving the stream of sound data, the VAS 190 determines if there is voice input in the streamed data from the NMD, and if so the VAS 190 will also determine an underlying intent in the voice input. The VAS 190 may next transmit a response back to the MPS 100, which can include transmitting the response directly to the NMD that caused the wake-word event. The response is typically based on the intent that the VAS 190 determined was present in the voice input. As an example, in response to the VAS 190 receiving a voice input with an utterance to “Play Hey Jude by The Beatles,” the VAS 190 may determine that the underlying intent of the voice input is to initiate playback and further determine that intent of the voice input is to play the particular song “Hey Jude.” After these determinations, the VAS 190 may transmit a command to a particular MCS 192 to retrieve content (i.e., the song “Hey Jude”), and that MCS 192, in turn, provides (e.g., streams) this content directly to the MPS 100 or indirectly via the VAS 190. In some implementations, the VAS 190 may transmit to the MPS 100 a command that causes the MPS 100 itself to retrieve the content from the MCS 192.
In certain implementations, NMDs may facilitate arbitration amongst one another when voice input is identified in speech detected by two or more NMDs located within proximity of one another. For example, the NMD-equipped playback device 102d in the environment 101 (
In certain implementations, an NMD may be assigned to, or otherwise associated with, a designated or default playback device that may not include an NMD. For example, the Island NMD 103f in the Kitchen 101h (
Further aspects relating to the different components of the example MPS 100 and how the different components may interact to provide a user with a media experience may be found in the following sections. While discussions herein may generally refer to the example MPS 100, technologies described herein are not limited to applications within, among other things, the home environment described above. For instance, the technologies described herein may be useful in other home environment configurations comprising more or fewer of any of the playback, network microphone, and/or controller devices 102-104. For example, the technologies herein may be utilized within an environment having a single playback device 102 and/or a single NMD 103. In some examples of such cases, the NETWORK 111 (
a. Example Playback & Network Microphone Devices
As shown, the playback device 102 includes at least one processor 212, which may be a clock-driven computing component configured to process input data according to instructions stored in memory 213. The memory 213 may be a tangible, non-transitory, computer-readable medium configured to store instructions that are executable by the processor 212. For example, the memory 213 may be data storage that can be loaded with software code 214 that is executable by the processor 212 to achieve certain functions.
In one example, these functions may involve the playback device 102 retrieving audio data from an audio source, which may be another playback device. In another example, the functions may involve the playback device 102 sending audio data, detected-sound data (e.g., corresponding to a voice input), and/or other information to another device on a network via at least one network interface 224. In yet another example, the functions may involve the playback device 102 causing one or more other playback devices to synchronously playback audio with the playback device 102. In yet a further example, the functions may involve the playback device 102 facilitating being paired or otherwise bonded with one or more other playback devices to create a multi-channel audio environment. Numerous other example functions are possible, some of which are discussed below.
As just mentioned, certain functions may involve the playback device 102 synchronizing playback of audio content with one or more other playback devices. During synchronous playback, a listener may not perceive time-delay differences between playback of the audio content by the synchronized playback devices. U.S. Pat. No. 8,234,395 filed on Apr. 4, 2004, and titled “System and method for synchronizing operations among a plurality of independently clocked digital data processing devices,” which is hereby incorporated by reference in its entirety, provides in more detail some examples for audio playback synchronization among playback devices.
To facilitate audio playback, the playback device 102 includes audio processing components 216 that are generally configured to process audio prior to the playback device 102 rendering the audio. In this respect, the audio processing components 216 may include one or more digital-to-analog converters (“DAC”), one or more audio preprocessing components, one or more audio enhancement components, one or more digital signal processors (“DSPs”), and so on. In some implementations, one or more of the audio processing components 216 may be a subcomponent of the processor 212. In operation, the audio processing components 216 receive analog and/or digital audio and process and/or otherwise intentionally alter the audio to produce audio signals for playback.
The produced audio signals may then be provided to one or more audio amplifiers 217 for amplification and playback through one or more speakers 218 operably coupled to the amplifiers 217. The audio amplifiers 217 may include components configured to amplify audio signals to a level for driving one or more of the speakers 218.
Each of the speakers 218 may include an individual transducer (e.g., a “driver”) or the speakers 218 may include a complete speaker system involving an enclosure with one or more drivers. A particular driver of a speaker 218 may include, for example, a subwoofer (e.g., for low frequencies), a mid-range driver (e.g., for middle frequencies), and/or a tweeter (e.g., for high frequencies). In some cases, a transducer may be driven by an individual corresponding audio amplifier of the audio amplifiers 217. In some implementations, a playback device may not include the speakers 218, but instead may include a speaker interface for connecting the playback device to external speakers. In certain embodiments, a playback device may include neither the speakers 218 nor the audio amplifiers 217, but instead may include an audio interface (not shown) for connecting the playback device to an external audio amplifier or audio-visual receiver.
In addition to producing audio signals for playback by the playback device 102, the audio processing components 216 may be configured to process audio to be sent to one or more other playback devices, via the network interface 224, for playback. In example scenarios, audio content to be processed and/or played back by the playback device 102 may be received from an external source, such as via an audio line-in interface (e.g., an auto-detecting 3.5 mm audio line-in connection) of the playback device 102 (not shown) or via the network interface 224, as described below.
As shown, the at least one network interface 224, may take the form of one or more wireless interfaces 225 and/or one or more wired interfaces 226. A wireless interface may provide network interface functions for the playback device 102 to wirelessly communicate with other devices (e.g., other playback device(s), NMD(s), and/or controller device(s)) in accordance with a communication protocol (e.g., any wireless standard including IEEE 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.15, 4G mobile communication standard, and so on). A wired interface may provide network interface functions for the playback device 102 to communicate over a wired connection with other devices in accordance with a communication protocol (e.g., IEEE 802.3). While the network interface 224 shown in
In general, the network interface 224 facilitates data flow between the playback device 102 and one or more other devices on a data network. For instance, the playback device 102 may be configured to receive audio content over the data network from one or more other playback devices, network devices within a LAN, and/or audio content sources over a WAN, such as the Internet. In one example, the audio content and other signals transmitted and received by the playback device 102 may be transmitted in the form of digital packet data comprising an Internet Protocol (IP)-based source address and IP-based destination addresses. In such a case, the network interface 224 may be configured to parse the digital packet data such that the data destined for the playback device 102 is properly received and processed by the playback device 102.
As shown in
In operation, the voice-processing components 220 are generally configured to detect and process sound received via the microphones 222, identify potential voice input in the detected sound, and extract detected-sound data to enable a VAS, such as the VAS 190 (
As further shown in
In some implementations, the power components 227 of the playback device 102 may additionally include an internal power source 229 (e.g., one or more batteries) configured to power the playback device 102 without a physical connection to an external power source. When equipped with the internal power source 229, the playback device 102 may operate independent of an external power source. In some such implementations, the external power source interface 228 may be configured to facilitate charging the internal power source 229. As discussed before, a playback device comprising an internal power source may be referred to herein as a “portable playback device.” On the other hand, a playback device that operates using an external power source may be referred to herein as a “stationary playback device,” although such a device may in fact be moved around a home or other environment.
The playback device 102 further includes a user interface 240 that may facilitate user interactions independent of or in conjunction with user interactions facilitated by one or more of the controller devices 104. In various embodiments, the user interface 240 includes one or more physical buttons and/or supports graphical interfaces provided on touch sensitive screen(s) and/or surface(s), among other possibilities, for a user to directly provide input. The user interface 240 may further include one or more of lights (e.g., LEDs) and the speakers to provide visual and/or audio feedback to a user.
As an illustrative example,
As further shown in
By way of illustration, SONOS, Inc. presently offers (or has offered) for sale certain playback devices that may implement certain of the embodiments disclosed herein, including a “PLAY:1,” “PLAY:3,” “PLAY:5,” “PLAYBAR,” “CONNECT:AMP,” “PLAYBASE,” “BEAM,” “CONNECT,” and “SUB.” Any other past, present, and/or future playback devices may additionally or alternatively be used to implement the playback devices of example embodiments disclosed herein. Additionally, it should be understood that a playback device is not limited to the examples illustrated in
Based on certain command criteria, the NMD and/or a remote VAS may take actions as a result of identifying one or more commands in the voice input. Command criteria may be based on the inclusion of certain keywords within the voice input, among other possibilities. Additionally, or alternatively, command criteria for commands may involve identification of one or more control-state and/or zone-state variables in conjunction with identification of one or more particular commands. Control-state variables may include, for example, indicators identifying a level of volume, a queue associated with one or more devices, and playback state, such as whether devices are playing a queue, paused, etc. Zone-state variables may include, for example, indicators identifying which, if any, zone players are grouped.
In some implementations, the MPS 100 is configured to temporarily reduce the volume of audio content that it is playing upon detecting a certain keyword, such as a wake word, in the keyword portion 280a. The MPS 100 may restore the volume after processing the voice input 280. Such a process can be referred to as ducking, examples of which are disclosed in U.S. patent application Ser. No. 15/438,749, incorporated by reference herein in its entirety.
ASR for command keyword detection may be tuned to accommodate a wide range of keywords (e.g., 5, 10, 100, 1,000, 10,000 keywords). Command keyword detection, in contrast to wake-word detection, may involve feeding ASR output to an onboard, local NLU which together with the ASR determine when command word events have occurred. In some implementations described below, the local NLU may determine an intent based on one or more other keywords in the ASR output produced by a particular voice input. In these or other implementations, a playback device may act on a detected command keyword event only when the playback devices determines that certain conditions have been met, such as environmental conditions (e.g., low background noise).
b. Example Playback Device Configurations
For purposes of control, each zone in the MPS 100 may be represented as a single user interface (“UI”) entity. For example, as displayed by the controller devices 104, Zone A may be provided as a single entity named “Portable,” Zone B may be provided as a single entity named “Stereo,” and Zone C may be provided as a single entity named “Living Room.”
In various embodiments, a zone may take on the name of one of the playback devices belonging to the zone. For example, Zone C may take on the name of the Living Room device 102m (as shown). In another example, Zone C may instead take on the name of the Bookcase device 102d. In a further example, Zone C may take on a name that is some combination of the Bookcase device 102d and Living Room device 102m. The name that is chosen may be selected by a user via inputs at a controller device 104. In some embodiments, a zone may be given a name that is different than the device(s) belonging to the zone. For example, Zone B in
As noted above, playback devices that are bonded may have different playback responsibilities, such as playback responsibilities for certain audio channels. For example, as shown in
Additionally, playback devices that are configured to be bonded may have additional and/or different respective speaker drivers. As shown in
In some implementations, playback devices may also be “merged.” In contrast to certain bonded playback devices, playback devices that are merged may not have assigned playback responsibilities, but may each render the full range of audio content that each respective playback device is capable of. Nevertheless, merged devices may be represented as a single UI entity (i.e., a zone, as discussed above). For instance,
In some embodiments, a stand-alone NMD may be in a zone by itself. For example, the NMD 103h from
Zones of individual, bonded, and/or merged devices may be arranged to form a set of playback devices that playback audio in synchrony. Such a set of playback devices may be referred to as a “group,” “zone group,” “synchrony group,” or “playback group.” In response to inputs provided via a controller device 104, playback devices may be dynamically grouped and ungrouped to form new or different groups that synchronously play back audio content. For example, referring to
In various implementations, the zones in an environment may be assigned a particular name, which may be the default name of a zone within a zone group or a combination of the names of the zones within a zone group, such as “Dining Room+Kitchen,” as shown in
Referring back to
In some embodiments, the memory 213 of the playback device 102 may store instances of various variable types associated with the states. Variables instances may be stored with identifiers (e.g., tags) corresponding to type. For example, certain identifiers may be a first type “al” to identify playback device(s) of a zone, a second type “b1” to identify playback device(s) that may be bonded in the zone, and a third type “c1” to identify a zone group to which the zone may belong. As a related example, in
In yet another example, the MPS 100 may include variables or identifiers representing other associations of zones and zone groups, such as identifiers associated with Areas, as shown in
The memory 213 may be further configured to store other data. Such data may pertain to audio sources accessible by the playback device 102 or a playback queue that the playback device (or some other playback device(s)) may be associated with. In embodiments described below, the memory 213 is configured to store a set of command data for selecting a particular VAS when processing voice inputs. During operation, one or more playback zones in the environment of
For instance, the user may be in the Office zone where the playback device 102n is playing the same hip-hop music that is being playing by playback device 102c in the Patio zone. In such a case, playback devices 102c and 102n may be playing the hip-hop in synchrony such that the user may seamlessly (or at least substantially seamlessly) enjoy the audio content that is being played out-loud while moving between different playback zones. Synchronization among playback zones may be achieved in a manner similar to that of synchronization among playback devices, as described in previously referenced U.S. Pat. No. 8,234,395.
As suggested above, the zone configurations of the MPS 100 may be dynamically modified. As such, the MPS 100 may support numerous configurations. For example, if a user physically moves one or more playback devices to or from a zone, the MPS 100 may be reconfigured to accommodate the change(s). For instance, if the user physically moves the playback device 102c from the Patio zone to the Office zone, the Office zone may now include both the playback devices 102c and 102n. In some cases, the user may pair or group the moved playback device 102c with the Office zone and/or rename the players in the Office zone using, for example, one of the controller devices 104 and/or voice input. As another example, if one or more playback devices 102 are moved to a particular space in the home environment that is not already a playback zone, the moved playback device(s) may be renamed or associated with a playback zone for the particular space.
Further, different playback zones of the MPS 100 may be dynamically combined into zone groups or split up into individual playback zones. For example, the Dining Room zone and the Kitchen zone may be combined into a zone group for a dinner party such that playback devices 102i and 102l may render audio content in synchrony. As another example, bonded playback devices in the Den zone may be split into (i) a television zone and (ii) a separate listening zone. The television zone may include the Front playback device 102b. The listening zone may include the Right, Left, and SUB playback devices 102a, 102j, and 102k, which may be grouped, paired, or merged, as described above. Splitting the Den zone in such a manner may allow one user to listen to music in the listening zone in one area of the living room space, and another user to watch the television in another area of the living room space. In a related example, a user may utilize either of the NMD 103a or 103b (
c. Example Controller Devices
The memory 413 of the controller device 104 may be configured to store controller application software and other data associated with the MPS 100 and/or a user of the system 100. The memory 413 may be loaded with instructions in software 414 that are executable by the processor 412 to achieve certain functions, such as facilitating user access, control, and/or configuration of the MPS 100. The controller device 104 is configured to communicate with other network devices via the network interface 424, which may take the form of a wireless interface, as described above.
In one example, system information (e.g., such as a state variable) may be communicated between the controller device 104 and other devices via the network interface 424. For instance, the controller device 104 may receive playback zone and zone group configurations in the MPS 100 from a playback device, an NMD, or another network device. Likewise, the controller device 104 may transmit such system information to a playback device or another network device via the network interface 424. In some cases, the other network device may be another controller device.
The controller device 104 may also communicate playback device control commands, such as volume control and audio playback control, to a playback device via the network interface 424. As suggested above, changes to configurations of the MPS 100 may also be performed by a user using the controller device 104. The configuration changes may include adding/removing one or more playback devices to/from a zone, adding/removing one or more zones to/from a zone group, forming a bonded or merged player, separating one or more playback devices from a bonded or merged player, among others.
As shown in
The playback control region 542 (
The playback zone region 543 (
In some embodiments, the graphical representations of playback zones may be selectable to bring up additional selectable icons to manage or configure the playback zones in the MPS 100, such as a creation of bonded zones, creation of zone groups, separation of zone groups, and renaming of zone groups, among other possibilities.
For example, as shown, a “group” icon may be provided within each of the graphical representations of playback zones. The “group” icon provided within a graphical representation of a particular zone may be selectable to bring up options to select one or more other zones in the MPS 100 to be grouped with the particular zone. Once grouped, playback devices in the zones that have been grouped with the particular zone will be configured to play audio content in synchrony with the playback device(s) in the particular zone. Analogously, a “group” icon may be provided within a graphical representation of a zone group. In this case, the “group” icon may be selectable to bring up options to deselect one or more zones in the zone group to be removed from the zone group. Other interactions and implementations for grouping and ungrouping zones via a user interface are also possible. The representations of playback zones in the playback zone region 543 (
The playback status region 544 (
The playback queue region 546 may include graphical representations of audio content in a playback queue associated with the selected playback zone or zone group. In some embodiments, each playback zone or zone group may be associated with a playback queue comprising information corresponding to zero or more audio items for playback by the playback zone or zone group. For instance, each audio item in the playback queue may comprise a uniform resource identifier (URI), a uniform resource locator (URL), or some other identifier that may be used by a playback device in the playback zone or zone group to find and/or retrieve the audio item from a local audio content source or a networked audio content source, which may then be played back by the playback device.
In one example, a playlist may be added to a playback queue, in which case information corresponding to each audio item in the playlist may be added to the playback queue. In another example, audio items in a playback queue may be saved as a playlist. In a further example, a playback queue may be empty, or populated but “not in use” when the playback zone or zone group is playing continuously streamed audio content, such as Internet radio that may continue to play until otherwise stopped, rather than discrete audio items that have playback durations. In an alternative embodiment, a playback queue can include Internet radio and/or other streaming audio content items and be “in use” when the playback zone or zone group is playing those items. Other examples are also possible.
When playback zones or zone groups are “grouped” or “ungrouped,” playback queues associated with the affected playback zones or zone groups may be cleared or re-associated. For example, if a first playback zone including a first playback queue is grouped with a second playback zone including a second playback queue, the established zone group may have an associated playback queue that is initially empty, that contains audio items from the first playback queue (such as if the second playback zone was added to the first playback zone), that contains audio items from the second playback queue (such as if the first playback zone was added to the second playback zone), or a combination of audio items from both the first and second playback queues. Subsequently, if the established zone group is ungrouped, the resulting first playback zone may be re-associated with the previous first playback queue or may be associated with a new playback queue that is empty or contains audio items from the playback queue associated with the established zone group before the established zone group was ungrouped. Similarly, the resulting second playback zone may be re-associated with the previous second playback queue or may be associated with a new playback queue that is empty or contains audio items from the playback queue associated with the established zone group before the established zone group was ungrouped. Other examples are also possible.
With reference still to
The sources region 548 may include graphical representations of selectable audio content sources and/or selectable voice assistants associated with a corresponding VAS. The VASes may be selectively assigned. In some examples, multiple VASes, such as AMAZON's Alexa, MICROSOFT's Cortana, etc., may be invokable by the same NMD. In some embodiments, a user may assign a VAS exclusively to one or more NMDs. For example, a user may assign a first VAS to one or both of the NMDs 102a and 102b in the Living Room shown in
d. Example Audio Content Sources
The audio sources in the sources region 548 may be audio content sources from which audio content may be retrieved and played by the selected playback zone or zone group. One or more playback devices in a zone or zone group may be configured to retrieve for playback audio content (e.g., according to a corresponding URI or URL for the audio content) from a variety of available audio content sources. In one example, audio content may be retrieved by a playback device directly from a corresponding audio content source (e.g., via a line-in connection). In another example, audio content may be provided to a playback device over a network via one or more other playback devices or network devices. As described in greater detail below, in some embodiments audio content may be provided by one or more media content services.
Example audio content sources may include a memory of one or more playback devices in a media playback system such as the MPS 100 of
In some embodiments, audio content sources may be added or removed from a media playback system such as the MPS 100 of
At step 650b, the playback device 102 receives the message 651a and adds the selected media content to the playback queue for play back.
At step 650c, the control device 104 receives input corresponding to a command to play back the selected media content. In response to receiving the input corresponding to the command to play back the selected media content, the control device 104 transmits a message 651b to the playback device 102 causing the playback device 102 to play back the selected media content. In response to receiving the message 651b, the playback device 102 transmits a message 651c to the computing device 106 requesting the selected media content. The computing device 106, in response to receiving the message 651c, transmits a message 651d comprising data (e.g., audio data, video data, a URL, a URI) corresponding to the requested media content.
At step 650d, the playback device 102 receives the message 651d with the data corresponding to the requested media content and plays back the associated media content.
At step 650e, the playback device 102 optionally causes one or more other devices to play back the selected media content. In one example, the playback device 102 is one of a bonded zone of two or more players (
Referring to the
The NMD 703 further includes microphones 720 and the at least one network interface 720 as described above and may also include other components, such as audio amplifiers, a user interface, etc., which are not shown in
Each channel 762 may correspond to a particular microphone 720. For example, an NMD having six microphones may have six corresponding channels. Each channel of the detected sound SD may bear certain similarities to the other channels but may differ in certain regards, which may be due to the position of the given channel's corresponding microphone relative to the microphones of other channels. For example, one or more of the channels of the detected sound SD may have a greater signal to noise ratio (“SNR”) of speech to background noise than other channels.
As further shown in
The spatial processor 764 is typically configured to analyze the detected sound SD and identify certain characteristics, such as a sound's amplitude (e.g., decibel level), frequency spectrum, directionality, etc. In one respect, the spatial processor 764 may help filter or suppress ambient noise in the detected sound SD from potential user speech based on similarities and differences in the constituent channels 762 of the detected sound SD, as discussed above. As one possibility, the spatial processor 764 may monitor metrics that distinguish speech from other sounds. Such metrics can include, for example, energy within the speech band relative to background noise and entropy within the speech band—a measure of spectral structure—which is typically lower in speech than in most common background noise. In some implementations, the spatial processor 764 may be configured to determine a speech presence probability, examples of such functionality are disclosed in U.S. patent application Ser. No. 15/984,073, filed May 18, 2018, titled “Linear Filtering for Noise-Suppressed Speech Detection,” which is incorporated herein by reference in its entirety.
In operation, the one or more buffers 768—one or more of which may be part of or separate from the memory 213 (
The network interface 724 may then provide this information to a remote server that may be associated with the MPS 100. In one aspect, the information stored in the additional buffer 769 does not reveal the content of any speech but instead is indicative of certain unique features of the detected sound itself. In a related aspect, the information may be communicated between computing devices, such as the various computing devices of the MPS 100, without necessarily implicating privacy concerns. In practice, the MPS 100 can use this information to adapt and fine-tune voice processing algorithms, including sensitivity tuning as discussed below. In some implementations the additional buffer may comprise or include functionality similar to lookback buffers disclosed, for example, in U.S. patent application Ser. No. 15/989,715, filed May 25, 2018, titled “Determining and Adapting to Changes in Microphone Performance of Playback Devices”; U.S. patent application Ser. No. 16/141,875, filed Sep. 25, 2018, titled “Voice Detection Optimization Based on Selected Voice Assistant Service”; and U.S. patent application Ser. No. 16/138,111, filed Sep. 21, 2018, titled “Voice Detection Optimization Using Sound Metadata,” which are incorporated herein by reference in their entireties.
In any event, the detected-sound data forms a digital representation (i.e., sound-data stream), SDS, of the sound detected by the microphones 720. In practice, the sound-data stream SDS may take a variety of forms. As one possibility, the sound-data stream SDS may be composed of frames, each of which may include one or more sound samples. The frames may be streamed (i.e., read out) from the one or more buffers 768 for further processing by downstream components, such as the VAS wake-word engines 770 and the voice extractor 773 of the NMD 703.
In some implementations, at least one buffer 768 captures detected-sound data utilizing a sliding window approach in which a given amount (i.e., a given window) of the most recently captured detected-sound data is retained in the at least one buffer 768 while older detected-sound data is overwritten when it falls outside of the window. For example, at least one buffer 768 may temporarily retain 20 frames of a sound specimen at given time, discard the oldest frame after an expiration time, and then capture a new frame, which is added to the 19 prior frames of the sound specimen.
In practice, when the sound-data stream SDS is composed of frames, the frames may take a variety of forms having a variety of characteristics. As one possibility, the frames may take the form of audio frames that have a certain resolution (e.g., 16 bits of resolution), which may be based on a sampling rate (e.g., 44,100 Hz). Additionally, or alternatively, the frames may include information corresponding to a given sound specimen that the frames define, such as metadata that indicates frequency response, power input level, SNR, microphone channel identification, and/or other information of the given sound specimen, among other examples. Thus, in some embodiments, a frame may include a portion of sound (e.g., one or more samples of a given sound specimen) and metadata regarding the portion of sound. In other embodiments, a frame may only include a portion of sound (e.g., one or more samples of a given sound specimen) or metadata regarding a portion of sound.
In any case, downstream components of the NMD 703 may process the sound-data stream SDS For instance, the VAS wake-word engines 770 are configured to apply one or more identification algorithms to the sound-data stream SDS (e.g., streamed sound frames) to spot potential wake words in the detected-sound SD. This process may be referred to as automatic speech recognition. The VAS wake-word engine 770a and command keyword engine 771a apply different identification algorithms corresponding to their respective wake words, and further generate different events based on detecting a wake word in the detected-sound SD.
Example wake word detection algorithms accept audio as input and provide an indication of whether a wake word is present in the audio. Many first- and third-party wake word detection algorithms are known and commercially available. For instance, operators of a voice service may make their algorithm available for use in third-party devices. Alternatively, an algorithm may be trained to detect certain wake-words.
For instance, when the VAS wake-word engine 770a detects a potential VAS wake word, the VAS work-word engine 770a provides an indication of a “VAS wake-word event” (also referred to as a “VAS wake-word trigger”). In the illustrated example of
In multi-VAS implementations, the NMD 703 may include a VAS selector 774 (shown in dashed lines) that is generally configured to direct extraction by the voice extractor 773 and transmission of the sound-data stream SDS to the appropriate VAS when a given wake-word is identified by a particular wake-word engine (and a corresponding wake-word trigger), such as the VAS wake-word engine 770a and at least one additional VAS wake-word engine 770b (shown in dashed lines). In such implementations, the NMD 703 may include multiple, different VAS wake-word engines and/or voice extractors, each supported by a respective VAS.
Similar to the discussion above, each VAS wake-word engine 770 may be configured to receive as input the sound-data stream SDS from the one or more buffers 768 and apply identification algorithms to cause a wake-word trigger for the appropriate VAS. Thus, as one example, the VAS wake-word engine 770a may be configured to identify the wake word “Alexa” and cause the NMD 703a to invoke the AMAZON VAS when “Alexa” is spotted. As another example, the wake-word engine 770b may be configured to identify the wake word “Ok, Google” and cause the NMD 520 to invoke the GOOGLE VAS when “Ok, Google” is spotted. In single-VAS implementations, the VAS selector 774 may be omitted.
In response to the VAS wake-word event (e.g., in response to the signal SVW indicating the wake-word event), the voice extractor 773 is configured to receive and format (e.g., packetize) the sound-data stream SDS. For instance, the voice extractor 773 packetizes the frames of the sound-data stream SDS into messages. The voice extractor 773 transmits or streams these messages, MV, that may contain voice input in real time or near real time to a remote VAS via the network interface 724.
The VAS is configured to process the sound-data stream SDS contained in the messages MV sent from the NMD 703. More specifically, the NMD 703a is configured to identify a voice input 780 based on the sound-data stream SDS. As described in connection with
When a VAS wake-word event occurs, the VAS may first process the keyword portion within the sound-data stream SDS to verify the presence of a VAS wake word. In some instances, the VAS may determine that the keyword portion comprises a false wake word (e.g., the word “Election” when the word “Alexa” is the target VAS wake word). In such an occurrence, the VAS may send a response to the NMD 703a with an instruction for the NMD 703a to cease extraction of sound data, which causes the voice extractor 773 to cease further streaming of the detected-sound data to the VAS. The VAS wake-word engine 770a may resume or continue monitoring sound specimens until it spots another potential VAS wake word, leading to another VAS wake-word event. In some implementations, the VAS does not process or receive the keyword portion but instead processes only the utterance portion.
In any case, the VAS processes the utterance portion to identify the presence of any words in the detected-sound data and to determine an underlying intent from these words. The words may correspond to one or more commands, as well as certain keywords. The keyword may be, for example, a word in the voice input identifying a particular device or group in the MPS 100. For instance, in the illustrated example, the keyword may be one or more words identifying one or more zones in which the music is to be played, such as the Living Room and the Dining Room (
To determine the intent of the words, the VAS is typically in communication with one or more databases associated with the VAS (not shown) and/or one or more databases (not shown) of the MPS 100. Such databases may store various user data, analytics, catalogs, and other information for natural language processing and/or other processing. In some implementations, such databases may be updated for adaptive learning and feedback for a neural network based on voice-input processing. In some cases, the utterance portion may include additional information, such as detected pauses (e.g., periods of non-speech) between words spoken by a user, as shown in
After processing the voice input, the VAS may send a response to the MPS 100 with an instruction to perform one or more actions based on an intent it determined from the voice input. For example, based on the voice input, the VAS may direct the MPS 100 to initiate playback on one or more of the playback devices 102, control one or more of these playback devices 102 (e.g., raise/lower volume, group/ungroup devices, etc.), or turn on/off certain smart devices, among other actions. After receiving the response from the VAS, the wake-word engine 770a of the NMD 703 may resume or continue to monitor the sound-data stream SDS1 until it spots another potential wake-word, as discussed above.
In general, the one or more identification algorithms that a particular VAS wake-word engine, such as the VAS wake-word engine 770a, applies are configured to analyze certain characteristics of the detected sound stream SDS and compare those characteristics to corresponding characteristics of the particular VAS wake-word engine's one or more particular VAS wake words. For example, the wake-word engine 770a may apply one or more identification algorithms to spot spectral characteristics in the detected sound stream SDS that match the spectral characteristics of the engine's one or more wake words, and thereby determine that the detected sound SD comprises a voice input including a particular VAS wake word.
In some implementations, the one or more identification algorithms may be third-party identification algorithms (i.e., developed by a company other than the company that provides the NMD 703a). For instance, operators of a voice service (e.g., AMAZON) may make their respective algorithms (e.g., identification algorithms corresponding to AMAZON's ALEXA) available for use in third-party devices (e.g., the NMDs 103), which are then trained to identify one or more wake words for the particular voice assistant service. Additionally, or alternatively, the one or more identification algorithms may be first-party identification algorithms that are developed and trained to identify certain wake words that are not necessarily particular to a given voice service. Other possibilities also exist.
As noted above, the NMD 703a also includes a command keyword engine 771a in parallel with the VAS wake-word engine 770a. Like the VAS wake-word engine 770a, the command keyword engine 771a may apply one or more identification algorithms corresponding to one or more wake words. A “command keyword event” is generated when a particular command keyword is identified in the detected-sound SD. In contrast to the nonce words typically as utilized as VAS wake words, command keywords function as both the activation word and the command itself. For instance, example command keywords may correspond to playback commands (e.g., “play,” “pause,” “skip,” etc.) as well as control commands (“turn on”), among other examples. Under appropriate conditions, based on detecting one of these command keywords, the NMD 703a performs the corresponding command.
The command keyword engine 771a can employ an automatic speech recognizer 772. The ASR 772 is configured to output phonetic or phenomic representations, such as text corresponding to words, based on sound in the sound-data stream SDS to text. For instance, the ASR 772 may transcribe spoken words represented in the sound-data stream SDS to one or more strings representing the voice input 780 as text. The command keyword engine 771 can feed ASR output (labeled as SASR) to a local natural language unit (NLU) 779 that identifies particular keywords as being command keywords for invoking command-keyword events, as described below.
As noted above, in some example implementations, the NMD 703a is configured to perform natural language processing, which may be carried out using an onboard natural language processor, referred to herein as a natural language unit (NLU) 779. The local NLU 779 is configured to analyze text output of the ASR 772 of the command keyword engine 771a to spot (i.e., detect or identify) keywords in the voice input 780. In
In one aspect, the library of the local NLU 779 includes command keywords. When the local NLU 779 identifies a command keyword in the signal SASR, the command keyword engine 771a generates a command keyword event and performs a command corresponding to the command keyword in the signal SASR, assuming that one or more conditions corresponding to that command keyword are satisfied.
Further, the library of the local NLU 779 may also include keywords corresponding to parameters. The local NLU 779 may then determine an underlying intent from the matched keywords in the voice input 780. For instance, if the local NLU matches the keywords “David Bowie” and “kitchen” in combination with a play command, the local NLU 779 may determine an intent of playing David Bowie in the Kitchen 101h on the playback device 102i. In contrast to a processing of the voice input 780 by a cloud-based VAS, local processing of the voice input 780 by the local NLU 779 may be relatively less sophisticated, as the NLU 779 does not have access to the relatively greater processing capabilities and larger voice databases that a VAS generally has access to.
In some examples, the local NLU 779 may determine an intent with one or more slots, which correspond to respective keywords. For instance, referring back to the play David Bowie in the Kitchen example, when processing the voice input, the local NLU 779 may determine that an intent is to play music (e.g., intent=playMusic), while a first slot includes David Bowie as target content (e.g., slot1=DavidBowie) and a second slot includes the Kitchen 101h as the target playback device (e.g., slot2=kitchen). Here, the intent (to “playMusic”) is based on the command keyword and the slots are parameters modifying the intent to a particular target content and playback device.
Within examples, the command keyword engine 771a outputs a signal, SCW, that indicates the occurrence of a command keyword event to the local NLU 779. In response to the command keyword event (e.g., in response to the signal SCW indicating the command keyword event), the local NLU 779 is configured to receive and process the signal SASR. In particular, the local NLU 779 looks at the words within the signal SASR to find keywords that match keywords in the library of the local NLU 779.
Some error in performing local automatic speech recognition is expected. Within examples, the ASR 772 may generate a confidence score when transcribing spoken words to text, which indicates how closely the spoken words in the voice input 780 matches the sound patterns for that word. In some implementations, generating a command keyword event is based on the confidence score for a given command keyword. For instance, the command keyword engine 771a may generate a command keyword event when the confidence score for a given sound exceeds a given threshold value (e.g., 0.5 on a scale of 0-1, indicating that the given sound is more likely than not the command keyword). Conversely, when the confidence score for a given sound is at or below the given threshold value, the command keyword engine 771a does not generate the command keyword event.
Similarly, some error in performing keyword matching is expected. Within examples, the local NLU may generate a confidence score when determining an intent, which indicates how closely the transcribed words in the signal SASR match the corresponding keywords in the library of the local NLU. In some implementations, performing an operation according to a determined intent is based on the confidence score for keywords matched in the signal SASR. For instance, the NMD 703 may perform an operation according to a determined intent when the confidence score for a given sound exceeds a given threshold value (e.g., 0.5 on a scale of 0-1, indicating that the given sound is more likely than not the command keyword). Conversely, when the confidence score for a given intent is at or below the given threshold value, the NMD 703 does not perform the operation according to the determined intent.
As noted above, in some implementations, a phrase may be used a command keyword, which provides additional syllables to match (or not match). For instance, the phrase “play me some music” has more syllables than “play,” which provides additional sound patterns to match to words. Accordingly, command keywords that are phrases may generally be less prone to false wake words.
As indicated above, the NMD 703a generates a command keyword event (and performs a command corresponding to the detected command keyword) only when certain conditions corresponding to a detected command keyword are met. These conditions are intended to lower the prevalence of false positive command keyword events. For instance, after detecting the command keyword “skip,” the NMD 703a generates a command keyword event (and skips to the next track) only when certain playback conditions indicating that a skip should be performed are met. These playback conditions may include, for example, (i) a first condition that a media item is being played back, (ii) a second condition that a queue is active, and (iii) a third condition that the queue includes a media item subsequent to the media item being played back. If any of these conditions are not satisfied, the command keyword event is not generated (and no skip is performed).
The NMD 703a includes the one or more state machine(s) 775a to facilitate determining whether the appropriate conditions are met. The state machine 775a transitions between a first state and a second state based on whether one or more conditions corresponding to the detected command keyword are met. In particular, for a given command keyword corresponding to a particular command requiring one or more particular conditions, the state machine 775a transitions into a first state when one or more particular conditions are satisfied and transitions into a second state when at least one condition of the one or more particular conditions is not satisfied.
Within example implementations, the command conditions are based on states indicated in state variables. As noted above, the devices of the MPS 100 may store state variables describing the state of the respective device. For instance, the playback devices 102 may store state variables indicating the state of the playback devices 102, such as the audio content currently playing (or paused), the volume levels, network connection status, and the like). These state variables are updated (e.g., periodically, or based on an event (i.e., when a state in a state variable changes)) and the state variables further can be shared among the devices of the MPS 100, including the NMD 703.
Similarly, the NMD 703 may maintain these state variables (either by virtue of being implemented in a playback device or as a stand-alone NMD). The state machine 775a monitors the states indicated in these state variables, and determines whether the states indicated in the appropriate state variables indicate that the command condition(s) are satisfied. Based on these determinations, the state machine 775a transitions between the first state and the second state, as described above.
In some implementations, the command-keyword engine 771 may be disabled unless certain conditions have been met via the state machines. For example, the first state and the second state of the state machine 775a may operate as enable/disable toggles to the command keyword engine 771a. In particular, while a state machine 775a corresponding to a particular command keyword is in the first state, the state machine 775a enables the command keyword engine 771a of the particular command keyword. Conversely, while the state machine 775a corresponding to the particular command keyword is in the second state, the state machine 775a disables the command keyword engine 771a of the particular command keyword. Accordingly, the disabled command keyword engine 771a ceases analyzing the sound-data stream SDS. In such cases when at least one command condition is not satisfied, the NMD 703a may suppress generation of command keyword event when the command keyword engine 771a detects a command keyword. Suppressing generation may involve gating, blocking or otherwise preventing output from the command keyword engine 771a from generating the command keyword event. Alternatively, suppressing generation may involve the NMD 703 ceasing to feed the sound-data stream SDS to the ASR 772. Such suppression prevents a command corresponding to the detected command keyword from being performed when at least one command condition is not satisfied. In such embodiments, the command keyword engine 771a may continue analyzing the sound-data stream SDS while the state machine 775a is in the first state, but command keyword events are disabled.
Other example conditions may be based on the output of a voice activity detector (“VAD”) 765. The VAD 765 is configured to detect the presence (or lack thereof) of voice activity in the sound-data stream SDS. In particular, the VAD 765 may analyze frames corresponding to the pre-roll portion of the voice input 780 (
The VAD 765 may utilize any suitable voice activity detection algorithms. Example voice detection algorithms involve determining whether a given frame includes one or more features or qualities that correspond to voice activity, and further determining whether those features or qualities diverge from noise to a given extent (e.g., if a value exceeds a threshold for a given frame). Some example voice detection algorithms involve filtering or otherwise reducing noise in the frames prior to identifying the features or qualities.
In some examples, the VAD 765 may determine whether voice activity is present in the environment based on one or more metrics. For example, the VAD 765 can be configured distinguish between frames that include voice activity and frames that don't include voice activity. The frames that the VAD determines have voice activity may be caused by speech regardless of whether it near- or far-field. In this example and others, the VAD 765 may determine a count of frames in the pre-roll portion of the voice input 780 that indicate voice activity. If this count exceeds a threshold percentage or number of frames, the VAD 765 may be configured to output a signal or set a state variable indicating that voice activity is present in the environment. Other metrics may be used as well in addition to, or as an alternative to, such a count.
The presence of voice activity in an environment may indicate that a voice input is being directed to the NMD 73. Accordingly, when the VAD 765 indicates that voice activity is not present in the environment (perhaps as indicated by a state variable set by the VAD 765) this may be configured as one of the command conditions for the command keywords. When this condition is met (i.e., the VAD 765 indicates that voice activity is present in the environment), the state machine 775a will transition to the first state to enable performing commands based on command keywords, so long as any other conditions for a particular command keyword are satisfied.
Further, in some implementations, the NMD 703 may include a noise classifier 766. The noise classifier 766 is configured to determine sound metadata (frequency response, signal levels, etc.) and identify signatures in the sound metadata corresponding to various noise sources. The noise classifier 766 may include a neural network or other mathematical model configured to identify different types of noise in detected sound data or metadata. One classification of noise may be speech (e.g., far-field speech). Another classification, may be a specific type of speech, such as background speech, and example of which is described in greater detail with reference to
For example, analyzing the sound metadata can include comparing one or more features of the sound metadata with known noise reference values or a sample population data with known noise. For example, any features of the sound metadata such as signal levels, frequency response spectra, etc. can be compared with noise reference values or values collected and averaged over a sample population. In some examples, analyzing the sound metadata includes projecting the frequency response spectrum onto an eigenspace corresponding to aggregated frequency response spectra from a population of NMDs. Further, projecting the frequency response spectrum onto an eigenspace can be performed as a pre-processing step to facilitate downstream classification.
In various embodiments, any number of different techniques for classification of noise using the sound metadata can be used, for example machine learning using decision trees, or Bayesian classifiers, neural networks, or any other classification techniques. Alternatively or additionally, various clustering techniques may be used, for example K-Means clustering, mean-shift clustering, expectation-maximization clustering, or any other suitable clustering technique. Techniques to classify noise may include one or more techniques disclosed in U.S. application Ser. No. 16/227,308 filed Dec. 20, 2018, and titled “Optimization of Network Microphone Devices Using Noise Classification,” which is herein incorporated by reference in its entirety.
To illustrate,
Referring back to
As noted above, one classification of sound may be background speech, such as speech indicative of far-field speech and/or speech indicative of a conversation not involving the NMD 703. The noise classifier 766 may output a signal and/or set a state variable indicating that background speech is present in the environment. The presence of voice activity (i.e., speech) in the pre-roll portion of the voice input 780 indicates that the voice input 780 might not be directed to the NMD 703, but instead be conversational speech within the environment. For instance, a household member might speak something like “our kids should have a play date soon” without intending to direct the command keyword “play” to the NMD 703.
Further, when the noise classifier indicates that background speech is present is present in the environment, this condition may disable the command keyword engine 771a. In some implementations, the condition of background speech being absent in the environment (perhaps as indicated by a state variable set by the noise classifier 766) is configured as one of the command conditions for the command keywords. Accordingly, the state machine 775a will not transition to the first state when the noise classifier 766 indicates that background speech is present in the environment.
Further, the noise classifier 766 may determine whether background speech is present in the environment based on one or more metrics. For example, the noise classifier 766 may determine a count of frames in the pre-roll portion of the voice input 780 that indicate background speech. If this count exceeds a threshold percentage or number of frames, the noise classifier 766 may be configured to output the signal or set the state variable indicating that background speech is present in the environment. Other metrics may be used as well in addition to, or as an alternative to, such a count.
Within example implementations, the NMD 703a may support a plurality of command keywords. To facilitate such support, the command keyword engine 771a may implement multiple identification algorithms corresponding to respective command keywords. Alternatively, the NMD 703a may implement additional command keyword engines 771b configured to identify respective command keywords. Yet further, the library of the local NLU 779 may include a plurality of command keywords and be configured to search for text patterns corresponding to these command keywords in the signal SASR.
Further, command keywords may require different conditions. For instance, the conditions for “skip” may be different than the conditions for “play” as “skip” may require that the condition that a media item is being played back and play may require the opposite condition that a media item is not being played back. To facilitate these respective conditions, the NMD 703a may implement respective state machines 775a corresponding to each command keyword. Alternatively, the NMD 703a may implement a state machine 775a having respective states for each command keyword. Other examples are possible as well.
In some example implementations, the VAS wake-word engine 770a generates a VAS wake-word event when certain conditions are met. The NMD 703b includes a state machine 775b, which is similar to the state machine 775a. The state machine 775b transitions between a first state and a second state based on whether one or more conditions corresponding to the VAS wake word are met.
For instance, in some examples, the VAS wake-word engine 770a may generate a VAS wake word event only when background speech was not present in the environment before a VAS wake-word event was detected. An indication of whether voice activity is present in the environment may come from the noise classifier 766. As noted above, the noise classifier 766 may be configured to output a signal or set a state variable indicating that far-field speech is present in the environment. Yet further, the VAS wake-word engine 770a may generate a VAS wake word event only when voice activity is present in the environment. As indicated above, the VAD 765 may be configured to output a signal or set a state variable indicating that voice activity is present in the environment.
To illustrate, as shown in
Yet further, the NMD 703 may include one or more sensors that output a signal indicating whether one or more users are in proximity to the NMD 703. Example sensors include a temperature sensor, an infrared sensor, an imaging sensor, and/or a capacitive sensor, among other examples. The NMD 703 may use output from such sensors to set one or more state variables indicating whether one or more users are in proximity to the NMD 703. Then, the state machine 775b may use the presence or lack thereof as a condition for the state machine 775b. For instance, the state machine 775b may enable the VAS wake-word engine and/or the command keyword engine 771a when at least one user is in proximity to the NMD 703.
To illustrate exemplary state machine operation,
At 777b, the state machine 775 transitions into the second state 778b when any command condition is not satisfied. At 777c, the state machine 775 remains in the second state 778b while any command condition is not satisfied. While the state machine 775 remains in the second state 778b, the NMD 703a will not act on the command keyword event when the command keyword is detected by the command keyword engine 771a.
Referring back to
For instance, the NMD 703a may include a particular streaming audio service (e.g., Apple Music) command keyword engine 771b. This particular command keyword engine 771b may be configured to detect command keywords specific to the particular streaming audio service and generate streaming audio service wake word events. For instance, one command keyword may be “Friends Mix,” which corresponds to a command to play back a custom playlists generated from playback histories of one or more “friends” within the particular streaming audio service.
A custom command keyword engine 771b may be relatively more prone to false wake words than the VAS wake-word engine 770a, as generally the VAS wake-word engine 770a is more sophisticated than a custom command keyword engine 771b. To mitigate this, custom command keywords may require one or more conditions to be satisfied before generating a custom command keyword event. Further, in some implementations, in an effort to reduce the prevalence of false positives, multiple conditions may be imposed as a requirement to include a custom command keyword engine 771b in the NMD 703a.
These custom command keyword conditions may include service-specific conditions. For instance, command keywords corresponding to premium features or playlists may require a subscription as a condition. As another example, custom command keywords corresponding to a particular streaming audio service may require media items from that streaming audio service in the playback queue. Other conditions are possible as well.
To gate custom command keyword engines based on the custom command keyword conditions, the NMD 703a may additional state machines 775a corresponding to each custom command keyword. Alternatively, the NMD 703a may implement a state machine 775a having respective states for each custom command keyword. Other examples are possible as well. These custom command conditions may depend on the state variables maintained by the devices within the MPS 100, and may also depend on state variables or other data structures representing a state of a user account of a cloud service, such as a streaming audio service.
Referring back to
To further reduce false positives, the command keyword engine 771a may utilize a relative low sensitivity compared with the VAS wake-word engine 770a. In practice, a wake-word engine may include a sensitivity level setting that is modifiable. The sensitivity level may define a degree of similarity between a word identified in the detected sound stream SDS1 and the wake-word engine's one or more particular wake words that is considered to be a match (i.e., that triggers a VAS wake-word or command keyword event). In other words, the sensitivity level defines how closely, as one example, the spectral characteristics in the detected sound stream SDS2 must match the spectral characteristics of the engine's one or more wake words to be a wake-word trigger.
In this respect, the sensitivity level generally controls how many false positives that the VAS wake-word engine 770a and command keyword engine 771a identifies. For example, if the VAS wake-word engine 770a is configured to identify the wake-word “Alexa” with a relatively high sensitivity, then false wake words of “Election” or “Lexus” may cause the wake-word engine 770a to flag the presence of the wake-word “Alexa.” In contrast, if the command keyword engine 771a is configured with a relatively low sensitivity, then the false wake words of “may” or “day” would not cause the command keyword engine 771a to flag the presence of the command keyword “Play.”
In practice, a sensitivity level may take a variety of forms. In example implementations, a sensitivity level takes the form of a confidence threshold that defines a minimum confidence (i.e., probability) level for a wake-word engine that serves as a dividing line between triggering or not triggering a wake-word event when the wake-word engine is analyzing detected sound for its particular wake word. In this regard, a higher sensitivity level corresponds to a lower confidence threshold (and more false positives), whereas a lower sensitivity level corresponds to a higher confidence threshold (and fewer false positives). For example, lowering a wake-word engine's confidence threshold configures it to trigger a wake-word event when it identifies words that have a lower likelihood that they are the actual particular wake word, whereas raising the confidence threshold configures the engine to trigger a wake-word event when it identifies words that have a higher likelihood that they are the actual particular wake word. Within examples, a sensitivity level of the command keyword engine 771a may be based on more or more confidence scores, such as the confidence score in spotting a command keyword and/or a confidence score in determining an intent. Other examples of sensitivity levels are also possible.
In example implementations, sensitivity level parameters (e.g., the range of sensitivities) for a particular wake-word engine can be updated, which may occur in a variety of manners. As one possibility, a VAS or other third-party provider of a given wake-word engine may provide to the NMD 703 a wake-word engine update that modifies one or more sensitivity level parameters for the given VAS wake-word engine 770a. By contrast, the sensitive level parameters of the command keyword engine 771a may be configured by the manufacturer of the NMD 703a or by another cloud service (e.g., for a custom wake-word engine 771b).
Notably, within certain examples, the NMD 703a foregoes sending any data representing the detected sound SD (e.g., the messages MV) to a VAS when processing a voice input 780 including a command keyword. In implementations including the local NLU 779, the NMD 703a can further process the voice utterance portion of the voice input 780 (in addition to the keyword word portion) without necessarily sending the voice utterance portion of the voice input 780 to the VAS. Accordingly, speaking a voice input 780 (with a command keyword) to the NMD 703 may provide increased privacy relative to other NMDs that process all voice inputs using a VAS.
As indicated above, the keywords in the library of the local NLU 779 correspond to parameters. These parameters may define to perform the command corresponding to the detected command keyword. When keywords are recognized in the voice input 780, the command corresponding to the detected command keyword is performed according to parameters corresponding to the detected keywords.
For instance, an example voice input 780 may be “play music at low volume” with “play” being the command keyword portion (corresponding to a playback command) and “music at low volume” being the voice utterance portion. When analyzing this voice input 780, the NLU 779 may recognize that “low volume” is a keyword in its library corresponding to a parameter representing a certain (low) volume level. Accordingly, the NLU 779 may determine an intent to play at this lower volume level. Then, when performing the playback command corresponding to “play,” this command is performed according to the parameter representing a certain volume level.
In a second example, another example voice input 780 may be “play my favorites in the Kitchen” with “play” again being the command keyword portion (corresponding to a playback command) and “my favorites in the Kitchen” as the voice utterance portion. When analyzing this voice input 780, the NLU 779 may recognize that “favorites” and “Kitchen” match keywords in its library. In particular, “favorites” corresponds to a first parameter representing particular audio content (i.e., a particular playlist that includes a user's favorite audio tracks) while “Kitchen” corresponds to a second parameter representing a target for the playback command (i.e., the kitchen 101h zone. Accordingly, the NLU 779 may determine an intent to play this particular playlist in the kitchen 101h zone.
In a third example, a further example voice input 780 may be “volume up” with “volume” being the command keyword portion (corresponding to a volume adjustment command) and “up” being the voice utterance portion. When analyzing this voice input 780, the NLU 779 may recognize that “up” is a keyword in its library corresponding to a parameter representing a certain volume increase (e.g., a 10 point increase on a 100 point volume scale). Accordingly, the NLU 779 may determine an intent to increase volume. Then, when performing the volume adjustment command corresponding to “volume,” this command is performed according to the parameter representing the certain volume increase.
Within examples, certain command keywords are functionally linked to a subset of the keywords within the library of the local NLU 779, which may hasten analysis. For instance, the command keyword “skip” may be functionality linked to the keywords “forward” and “backward” and their cognates. Accordingly, when the command keyword “skip” is detected in a given voice input 780, analyzing the voice utterance portion of that voice input 780 with the local NLU 779 may involve determining whether the voice input 780 includes any keywords that match these functionally linked keywords (rather than determining whether the voice input 780 includes any keywords that match any keyword in the library of the local NLU 779). Since vastly fewer keywords are checked, this analysis is relatively quicker than a full search of the library. By contrast, a nonce VAS wake word such as “Alexa” provides no indication as to the scope of the accompanying voice input.
Some commands may require one or more parameters, as such the command keyword alone does not provide enough information to perform the corresponding command. For example, the command keyword “volume” might require a parameter to specify a volume increase or decrease, as the intent of “volume” of volume alone is unclear. As another example, the command keyword “group” may require two or more parameters identifying the target devices to group.
Accordingly, in some example implementations, when a given command keyword is detected in the voice input 780 by the command keyword engine 771a, the local NLU 779 may determine whether the voice input 780 includes keywords matching keywords in the library corresponding to the required parameters. If the voice input 780 does include keywords matching the required parameters, the NMD 703a proceeds to perform the command (corresponding to the given command keyword) according to the parameters specified by the keywords.
However, if the voice input 780 does include keywords matching the required parameters for the command, the NMD 703a may prompt the user to provide the parameters. For instance, in a first example, the NMD 703a may play an audible prompt such as “I've heard a command, but I need more information” or “Can I help you with something?” Alternatively, the NMD 703a may send a prompt to a user's personal device via a control application (e.g., the software components 132c of the control device(s) 104).
In further examples, the NMD 703a may play an audible prompt customized to the detected command keyword. For instance, after detect a command keyword corresponding to a volume adjustment command (e.g., “volume”), the audible prompt may include a more specific request such as “Do you want to adjust the volume up or down?” As another example, for a grouping command corresponding to the command keyword “group,” the audible prompt may be “Which devices do you want to group?” Supporting such specific audible prompts may be made practicable by supporting a relatively limited number of command keywords (e.g., less than 100), but other implementations may support more command keywords with the trade-off of requiring additional memory and processing capability.
Within additional examples, when a voice utterance portion does not include keywords corresponding to one or more required parameters, the NMD 703a may perform the corresponding command according to one or more default parameters. For instance, if a playback command does not include keywords indicating target playback devices 102 for playback, the NMD 703a may default to playback on the NMD 703a itself (e.g., if the NMD 703a is implemented within a playback device 102) or to playback on one or more associated playback devices 102 (e.g., playback devices 102 in the same room or zone as the NMD 703a). Further, in some examples, the user may configure default parameters using a graphical user interface (e.g., user interface 430) or voice user interface. For example, if a grouping command does not specify the playback devices 102 to group, the NMD 703a may default to instructing two or more pre-configured default playback devices 102 to form a synchrony group. Default parameters may be stored in data storage (e.g., the memory 112b (
In some cases, the NMD 703a sends the voice input 780 to a VAS when the local NLU 779 is unable to process the voice input 780 (e.g., when the local NLU is unable to find matches to keywords in the library, or when the local NLU 779 has a low confidence score as to intent). In an example, to trigger sending the voice input 780, the NMD 703a may generate a bridging event, which causes the voice extractor 773 to process the sound-data stream SD, as discussed above. That is, the NMD 703a generates a bridging event to trigger the voice extractor 773 without a VAS wake-word being detected by the VAS wake-word engine 770a (instead based on a command keyword in the voice input 780, as well as the NLU 779 being unable to process the voice input 780).
Before sending the voice input 780 to the VAS (e.g., via the messages MV), the NMD 703a may obtain confirmation from the user that the user acquiesces to the voice input 780 being sent to the VAS. For instance, the NMD 703a may play an audible prompt to send the voice input to a default or otherwise configured VAS, such as “I'm sorry, I didn't understand that. May I ask Alexa?” In another example, the NMD 703a may play an audible prompt using a VAS voice (i.e., a voice that is known to most users as being associated with a particular VAS), such as “Can I help you with something?” In such examples, generation of the bridging event (and trigging of the voice extractor 773) is contingent on a second affirmative voice input 780 from the user.
Within certain example implementations, the local NLU 779 may process the signal SASR without necessarily a command keyword event being generated by the command keyword engine 771a (i.e., directly). That is, the automatic speech recognition 772 may be configured to perform automatic speech recognition on the sound-data stream SD, which the local NLU 779 processes for matching keywords without requiring a command keyword event. If keywords in the voice input 780 are found to match keywords corresponding to a command (possibly with one or more keywords corresponding to one or more parameters), the NMD 703a performs the command according to the one or more parameters.
Further, in such examples, the local NLU 779 may process the signal SASR directly only when certain conditions are met. In particular, in some embodiments, the local NLU 779 processes the signal SASR only when the state machine 775a is in the first state. The certain conditions may include a condition corresponding to no background speech in the environment. An indication of whether background speech is present in the environment may come from the noise classifier 766. As noted above, the noise classifier 766 may be configured to output a signal or set a state variable indicating that far-field speech is present in the environment. Further, another condition may corresponding to voice activity in the environment. The VAD 765 may be configured to output a signal or set a state variable indicating that voice activity is present in the environment. Similarly, The prevalence of false positive detection of commands with a direct processing approach may be mitigated using the conditions determined by the state machine 775a.
In some examples, the library of the local NLU 779 is partially customized to the individual user(s). In a first aspect, the library may be customized to the devices that are within the household of the NMD (e.g., the household within the environment 101 (
Within example implementations, the NMD 703a may populate the library of the local NLU 779 locally within the network 111 (
In further examples, the NMD 703a may populate the library by discovering devices connected to the network 111. For instance, the NMD 703a may transmit discovery requests via the network 111 according to a protocol configured for device discovery, such as universal plug-and-play (UPnP) or zero-configuration networking. Devices on the network 111 may then respond to the discovery requests and exchange data representing the device names, identifiers, addresses and the like to facilitate communication and control via the network 111. The NMD 703a may read these names from the exchanged messages and include them in the library of the local NLU 779 by training the local NLU 779 to recognize them as keywords.
In further examples, the NMD 703a may populate the library using the cloud. To illustrate,
One or more communication links 903a, 903b, and 903c (referred to hereinafter as “the links 903”) communicatively couple the MPS 100 and the cloud servers 906. The links 903 can include one or more wired networks and one or more wireless networks (e.g., the Internet). Further, similar to the network 111 (
In some implementations, the media playback system control servers 906a facilitate populating the library of local NLU 779 with the NMD(s) 703a (representing one or more of the NMD 703a (
In some examples, the media playback system control servers 906a may utilize user accounts and/or user profiles in obtaining keywords specific to the user. As noted above, a user of the MPS 100 may set-up a user profile to define settings and other information within the MPS 100. The user profile may then in turn be registered with user accounts of one or more streaming audio services to facilitate streaming audio from such services to the playback devices 102 of the MPS 100.
Through use of these registered streaming audio services, the streaming audio service servers 906b may collect data indicating a user's saved or preferred playlists, artists, albums, tracks, and the like, either via usage history or via user input (e.g., via a user input designating a media item as saved or a favorite). This data may be stored in a database on the streaming audio service servers 906b to facilitate providing certain features of the streaming audio service to the user, such as custom playlists, recommendations, and similar features. Under appropriate conditions (e.g., after receiving user permission), the streaming audio service servers 906b may share this data with the media playback system control servers 906a over the links 903b.
Accordingly, within examples, the media playback system control servers 906a may maintain or have access to data indicating a user's saved or preferred playlists, artists, albums, tracks, genres, and the like. If a user has registered their user profile with multiple streaming audio services, the saved data may include saved playlists, artists, albums, tracks, and the like from two or more streaming audio services. Further, the media playback system control servers 906a may develop a more complete understanding of the user's preferred playlists, artists, albums, tracks, and the like by aggregating data from the two or more streaming audio services, as compared with a streaming audio service that only has access to data generated through use of its own service.
Moreover, in some implementations, in addition to the data shared from the streaming audio service servers 906b, the media playback system control servers 906a may collect usage data from the MPS 100 over the links 903a, after receiving user permission. This may include data indicating a user's saved or preferred media items on a zone basis. Different types of music may be preferred in different rooms. For instance, a user may prefer upbeat music in the Kitchen 101h and more mellow music to assist with focus in the Office 101e.
Using the data indicating a user's saved or preferred playlists, artists, albums, tracks, and the like, the media playback system control servers 906a may identify names of playlists, artists, albums, tracks, and the like that the user is likely to refer to when providing playback commands to the NMDs 703a via voice input. Data representing these names can then be transmitted via the links 903a and the network 904 to the NMDs 703a and then added to the library of the local NLU 779 as keywords. For instance, the media playback system control servers 906a may send instructions to the NMDs 703a to include certain names as keywords in the library of the local NLU 779. Alternatively, the NMDs 703a (or another device of the MPS 100) may identify names of playlists, artists, albums, tracks, and the like that the user is likely to refer to when providing playback commands to the NMDs 703a via voice input and then include these names in the library of the local NLU 779.
Due to such customization, similar voice inputs may result in different operations being performed when the voice input is processed by the local NLU 779 as compared with processing by a VAS. For instance, a first voice input of “Alexa, play me my favorites in the Office” may trigger a VAS wake-word event, as it includes a VAS wake word (“Alexa”). A second voice input of “Play me my favorites in the Office” may trigger a command keyword, as it includes a command keyword (“play”). Accordingly, the first voice input is sent by the NMD 703a to the VAS, while the second voice input is processed by the local NLU 779.
While these voice inputs are nearly identical, they may cause different operations. In particular, the VAS may, to the best of its ability, determine a first playlist of audio tracks to add to a queue of the playback device 102f in the office 101e. Similarly, the local NLU 779 may recognize keywords “favorites” and “kitchen” in the second voice input. Accordingly, the NMD 703a performs the voice command of “play” with parameters of <favorites playlist> and <kitchen 101h zone>, which causes a second playlist of audio tracks to be added to the queue of the playback device 102f in the office 101e. However, the second playlist of audio tracks may include a more complete and/or more accurate collection of the user's favorite audio tracks, as the second playlist of audio tracks may draw on data indicating a user's saved or preferred playlists, artists, albums, and tracks from multiple streaming audio services, and/or the usage data collected by the media playback system control servers 906a. In contrast, the VAS may draw on its relatively limited conception of the user's saved or preferred playlists, artists, albums, and tracks when determining the first playlist.
To illustrate,
A household may include multiple users. Two or more users may configure their own respective user profiles with the MPS 100. Each user profile may have its own user accounts of one or more streaming audio services associated with the respective user profile. Further, the media playback system control servers 906a may maintain or have access to data indicating each user's saved or preferred playlists, artists, albums, tracks, genres, and the like, which may be associated with the user profile of that user.
In various examples, names corresponding to user profiles may be populated in the library of the local NLU 779. This may facilitate referring to a particular user's saved or preferred playlists, artists, albums, tracks, or genres. For instance, when a voice input of “Play Anne's favorites on the patio” is processed by the local NLU 779, the local NLU 779 may determine that “Anne” matches a stored keyword corresponding to a particular user. Then, when performing the playback command corresponding to that voice input, the NMD 703a adds a playlist of that particular user's favorite audio tracks to the queue of the playback device 102c in the patio 101i.
In some cases, a voice input might not include a keyword corresponding to a particular user, but multiple user profiles are configured with the MPS 100. In some cases, the NMD 703a may determine the user profile to use in performing a command using voice recognition. Alternatively, the NMD 703a may default to a certain user profile. Further, the NMD 703a may use preferences from the multiple user profiles when performing a command corresponding to a voice input that did not identify a particular user profile. For instance, the NMD 703a may determine a favorites playlist including preferred or saved audio tracks from each user profile registered with the MPS 100.
The IOT cloud servers 906c may be configured to provide supporting cloud services to the smart devices 990. The smart devices 990 may include various “smart” internet-connected devices, such as lights, thermostats, cameras, security systems, appliances, and the like. For instance, an IOT cloud server 906c may provide a cloud service supporting a smart thermostat, which allows a user to control the smart thermostat over the internet via a smartphone app or website.
Accordingly, within examples, the IOT cloud servers 906c may maintain or have access to data associated with a user's smart devices 990, such as device names, settings, and configuration. Under appropriate conditions (e.g., after receiving user permission), the IOT cloud servers 906c may share this data with the media playback system control servers 906a and/or the NMD 703a via the links 903c. For instance, the IOT cloud servers 906c that provide the smart thermostat cloud service may provide data representing such keywords to the NMD 703a, which facilitates populating the library of the local NLU 779 with keywords corresponding to the temperature.
Yet further, in some cases, the IOT cloud servers 906c may also provide keywords specific to control of their corresponding smart devices 990. For instance, the IOT cloud server 906c that provides the cloud service supporting the smart thermostat may provide a set of keywords corresponding to voice control of a thermostat, such as “temperature,” “warmer,” or “cooler,” among other examples. Data representing such keywords may be sent to the NMDs 703a over the links 903 and the network 904 from the IOT cloud servers 906c.
As noted above, some households may include more than NMD 703a. In example implementations, two or more NMDs 703a may synchronize or otherwise update the libraries of their respective local NLU 779. For instance, a first NMD 703a and a second NMD 703a may share data representing the libraries of their respective local NLU 779, possibly using a network (e.g., the network 904). Such sharing may facilitate the NMDs 703a being able to respond to voice input similarly, among other possible benefits.
In some embodiments, one or more of the components described above can operate in conjunction with the microphones 720 to detect and store a user's voice profile, which may be associated with a user account of the MPS 100. In some embodiments, voice profiles may be stored as and/or compared to variables stored in a set of command information or data table. The voice profile may include aspects of the tone or frequency of a user's voice and/or other unique aspects of the user, such as those described in previously-referenced U.S. patent application Ser. No. 15/438,749.
In some embodiments, one or more of the components described above can operate in conjunction with the microphones 720 to determine the location of a user in the home environment and/or relative to a location of one or more of the NMDs 103. Techniques for determining the location or proximity of a user may include one or more techniques disclosed in previously-referenced U.S. patent application Ser. No. 15/438,749, U.S. Pat. No. 9,084,058 filed Dec. 29, 2011, and titled “Sound Field Calibration Using Listener Localization,” and U.S. Pat. No. 8,965,033 filed Aug. 31, 2012, and titled “Acoustic Optimization.” Each of these applications is herein incorporated by reference in its entirety.
At block 1202, the method 1200 involves monitoring an input sound-data stream for (i) a wake-word event and (ii) a first command keyword event. For instance, the VAS wake-word engine 770a of the NMD 703a may apply one or more wake-word identification algorithms to the sound-data stream SDS (
At block 1204, the method 1200 involves detecting a wake-word event. Detecting the wake word event may involve the VAS wake-word engine 770a of the NMD 703a detecting a first sound detected via the microphone(s) 720 includes a first voice input comprising a wake word. The VAS wake-word engine 770a may detect such a wake word in the first voice input using an identification algorithm.
At block 1206, the method 1200 involves streaming sound data corresponding to the first voice input to one or more remote servers of a voice assistant service. For example, the voice extractor 773 may extract at least a portion (e.g., a wake word portion and/or a voice utterance portion) of the first voice input from the sound-data stream SDS (
At block 1208, the method 1200 involves detecting a first command keyword event. For example, after detecting a second sound, the command keyword engine 771a of the NMD 703a may detect a first command keyword in the sound-data stream SD corresponding to a second voice input in the second sound. Other examples are possible as well.
At block 1210, the method 1200 involves determining whether one or more playback conditions corresponding to the first command keyword are satisfied. Determining whether the one or more playback conditions corresponding to the first command keyword are satisfied may involve determining a state of a state machine. For instance, the state machine(s) 775 of the NMD 703a may transition to a first state when the one or more playback conditions corresponding to the first command keyword are satisfied and transition to a second state when at least one condition of the one or more playback conditions corresponding to the first command keyword are not satisfied (
At block 1212, the method 1200 involves performing a first playback command corresponding to the first command keyword. For instance, the NMD 703a may perform the first playback command based on detecting the first command keyword event and determining that the one or more playback conditions corresponding to the first command keyword are satisfied. Within examples, performing the first playback command may involve generating one or more instructions to perform the command, which cause the target playback device(s) to perform the first playback command.
Within examples, the target playback device(s) 102 to perform the first playback command may be explicitly or implicitly defined. For example, the target playback devices 102 may be explicitly defined by reference in the voice input 780 to the name(s) of one or more playback devices (e.g., by reference to a zone or zone group name). Alternatively, the voice input might not include any reference to the name(s) of one or more playback devices and instead may implicitly refer to playback device(s) 102 associated with the NMD 703a. Playback devices 102 associated with the NMD 703a may include a playback device implementing the NMD 703a, as illustrated by the playback device 102d implementing the NMD 103d (
Within examples, performing the first playback operation may involve transmitting one or more instructions over one or more networks. For instance, the NMD 703a may transmit instructions locally over the network 903 to one or more playback devices 102 to perform instructions such as transport control (
Yet further, transmitting instructions may involve both local and cloud based operations. For instance, the NMD 703a may transmit instructions locally over the network 903 to one or more playback devices 102 to add one or more audio tracks to the playback queue over the network 903. Then, the one or more playback devices 102 may transmit a request to the streaming audio service service(s) 906b to stream one or more audio tracks to the target playback device(s) 102 for playback over the links 903. Other examples are possible as well.
At block 1302, the method 1300 involves monitoring an input sound-data stream for (i) a wake-word event and (ii) a first command keyword event. For instance, the VAS wake-word engine 770a of the NMD 703a may apply one or more wake-word identification algorithms to the sound-data stream SDS (
At block 1304, the method 1300 involves detecting a wake-word event. Detecting the wake word event may involve the VAS wake-word engine 770a of the NMD 703a detecting a first sound detected via the microphone(s) 720 includes a first voice input comprising a wake word. The VAS wake-word engine 770a may detect such a wake word in the first voice input using an identification algorithm.
At block 1306, the method 1300 involves streaming sound data corresponding to the first voice input to one or more remote servers of a voice assistant service. For example, the voice extractor 773 may extract at least a portion (e.g., a wake word portion and/or a voice utterance portion) of the first voice input from the sound-data stream SDS (
At block 1308, the method 1300 involves detecting a first command keyword event. For example, after detecting a second sound, the command keyword engine 771a of the NMD 703a may detect a first command keyword in the sound-data stream SD corresponding to a second voice input in the second sound (
At block 1310, the method 1300 involves determining an intent based on the at least one keyword. For instance, the local NLU 779 may determine an intent from one or more keyword in the second voice input. As indicated above, the keywords in the library of the local NLU 779 correspond to parameters. The keyword(s) in a voice input may indicate an intent, such as to play particular audio content in a particular zone.
At block 1312, the method 1300 involves performing a first playback command according to the determined intent. Within examples, performing the first playback command may involve generating one or more instructions to perform the command according to the determined intent, which cause the target playback device(s) to perform the first playback command customized by the parameter(s) in the voice utterance portion of the voice input. As indicated above in connection with block 1212 (
Similar to the methods 1200 and 1300, the method 1400 may be performed by a networked microphone device, such as the NMD 120 (
At block 1402, the method 1400 involves detecting sound via one or more microphones. For instance, the NMD 703 may detect sound via the microphones 720 (
At block 1404, the method 1400 involves determining that (i) the detected sound includes a voice input, (ii) the detected sound excludes background speech, and (iii) the voice input includes a command keyword.
For instance, to determine whether the detected sound includes a voice input, the voice activity detector 765 may analyze the detected sound to determine a presence (or lack thereof) of voice activity in the sound-data stream SDS (
Yet further, to determine whether the voice input includes a command keyword, the command keyword engine 771a may analyze the sound-data stream SDS (
At block 1406, the method 1400 involves performing a playback function corresponding to the command keyword. For instance, the NMD may perform the playback function based on determining (i) the detected sound includes the voice input, (ii) the detected sound excludes background speech, and (iii) the voice input includes the command keyword. Blocks 1212 and 1312 of
Yet further, a voice activity detector (VAD) and a noise classifier have analyzed 150 frames of a pre-roll portion of the voice input. As shown, the VAD has detected voice in 140 frames of the 150 pre-roll frames, which indicates that a voice input may be present in the detected sound. Further, the noise classifier has detected ambient noise in 11 frames, background speech in 127 frames, and fan noise in 12 frames. In this NMD, the noise classifier is classifying the predominant noise source in each frame. This indicates the presence of background speech. As a result, the NMD has determined not to trigger on the detected command keyword “play.”
Yet further, a voice activity detector (VAD) and a noise classifier have analyzed 150 frames of a pre-roll portion of the voice input. As shown, the VAD has detected voice in 87 frames of the 150 pre-roll frames, which indicates that a voice input may be present in the detected sound. Further, the noise classifier has detected ambient noise in 18 frames, background speech in 8 frames, and fan noise in 124 frames. This indicates that background speech is not present. Given the foregoing, the NMD has determined to trigger on the detected command keyword “play.”
As shown, the ASR has transcribed the voice input as “play beet les in the kitchen.” Some error in performing ASR is expected (e.g., “beet les”). Here, the local NLU has matched the keyword “beet les” to “The Beatles” in the local NLU library, which sets up this artist as a content parameter to the play command. Further, the local NLU has also matched the keyword “kitchen” to “kitchen” in the local NLU library, which sets up the kitchen zone as a target parameter to the play command. The local NLU produced a confidence score of 0.63428231948273443 associated with the intent determination.
Here as well, a voice activity detector (VAD) and a noise classifier have analyzed 150 frames of a pre-roll portion of the voice input. As shown, the noise classifier has detected ambient noise in 142 frames, background speech in 8 frames, and fan noise in 0 frames. This indicates that background speech is not present. The VAD has detected voice in 112 frames of the 150 pre-roll frames, which indicates that a voice input may be present in the detected sound. Here, the NMD has determined to trigger on the detected command keyword “play.”
Yet further, a voice activity detector (VAD) and a noise classifier have analyzed 150 frames of a pre-roll portion of the voice input. As shown, the VAD has detected voice in 140 frames of the 150 pre-roll frames, which indicates that a voice input may be present in the detected sound. Further, the noise classifier has detected ambient noise in 11 frames, background speech in 127 frames, and fan noise in 12 frames This indicates the presence of background speech. As a result, the NMD has determined not to trigger on the detected command keyword “play.”
As shown, the ASR has transcribed the voice input as “lay some music in the office.” Here, the local NLU has matched the keyword “lay” to “play” in the local NLU library, which corresponds to a playback command. Further, the local NLU has also matched the keyword “office” to “office” in the local NLU library, which sets up the office zone as a target parameter to the play command. The local NLU produced a confidence score of 0.14620494842529297 associated with the keyword matching. In some examples, this low confidence score may cause the NMD to not accept the voice input (e.g., if this confidence score is below a threshold, such as 0.5).
The description above discloses, among other things, various example systems, methods, apparatus, and articles of manufacture including, among other components, firmware and/or software executed on hardware. It is understood that such examples are merely illustrative and should not be considered as limiting. For example, it is contemplated that any or all of the firmware, hardware, and/or software aspects or components can be embodied exclusively in hardware, exclusively in software, exclusively in firmware, or in any combination of hardware, software, and/or firmware. Accordingly, the examples provided are not the only way(s) to implement such systems, methods, apparatus, and/or articles of manufacture.
The specification is presented largely in terms of illustrative environments, systems, procedures, steps, logic blocks, processing, and other symbolic representations that directly or indirectly resemble the operations of data processing devices coupled to networks. These process descriptions and representations are typically used by those skilled in the art to most effectively convey the substance of their work to others skilled in the art. Numerous specific details are set forth to provide a thorough understanding of the present disclosure. However, it is understood to those skilled in the art that certain embodiments of the present disclosure can be practiced without certain, specific details. In other instances, well known methods, procedures, components, and circuitry have not been described in detail to avoid unnecessarily obscuring aspects of the embodiments. Accordingly, the scope of the present disclosure is defined by the appended claims rather than the forgoing description of embodiments.
When any of the appended claims are read to cover a purely software and/or firmware implementation, at least one of the elements in at least one example is hereby expressly defined to include a tangible, non-transitory medium such as a memory, DVD, CD, Blu-ray, and so on, storing the software and/or firmware.
The present technology is illustrated, for example, according to various aspects described below. Various examples of aspects of the present technology are described as numbered examples (1, 2, 3, etc.) for convenience. These are provided as examples and do not limit the present technology. It is noted that any of the dependent examples may be combined in any combination, and placed into a respective independent example. The other examples can be presented in a similar manner.
Example 1: A method to be performed by a playback device comprising a network interface and at least one microphone configured to detect sound, the method comprising monitoring an input sound-data stream representing the sound detected by the at least one microphone for (i) a wake-word event and (ii) a first command keyword event; detecting the wake-word event, wherein detecting the wake-word event comprises after detecting a first sound via the one or more microphones, determining that the detected first sound includes a first voice input comprising a wake word; streaming, via the network interface, sound data corresponding to at least a portion of the first voice input to one or more remote servers of a voice assistant service; detecting the first command keyword event, wherein detecting the first command keyword event comprises after detecting a second sound via the one or more microphones, determining that the detected second sound includes a second voice input comprising a first command keyword, wherein the first command keyword is one of a plurality of command keywords supported by the playback device; determining that one or more playback conditions corresponding to the first command keyword are satisfied; and in response to detecting the first command keyword event and determining that the one or more playback conditions corresponding to the first command keyword are satisfied, performing a first playback command corresponding to the first command keyword.
Example 2: The method of Example 1, further comprising: after detecting the first command keyword event, detecting a subsequent first command keyword event, wherein detecting the subsequent first command keyword event comprises after detecting a third sound via the at least one microphone determining that the third sound includes a third voice input comprising the first command keyword; determining that at least one playback condition of the one or more playback conditions corresponding to the first command keyword is not satisfied; and in response to determining that the at least one playback condition is not satisfied, forgoing the performing of the first playback command corresponding to the first command keyword.
Example 3: The method of any of Examples 1 and 2, further comprising: detecting a second command keyword event, wherein detecting the second command keyword event comprises after detecting a third sound via the at least one microphone, determining that the third sound includes a third voice input comprising a second command keyword in the detected third sound; determining that one or more playback conditions corresponding to the second command keyword are satisfied; and in response to detecting the second command keyword event and determining that the one or more playback conditions corresponding to the second command keyword are satisfied, performing a second playback command corresponding to the second command keyword.
Example 4: The method of Example 3, wherein at least one playback condition of the one or more playback conditions corresponding to the second command keyword is not a playback condition of the one or more playback conditions corresponding to the first command keyword.
Example 5: The method of any of Examples 1-4, wherein the first command keyword is a skip command, wherein the one or more playback conditions corresponding to the first command keyword comprise (i) a first condition that a media item is being played back on the playback device, (ii) a second condition that a queue is active on the playback device, and (iii) a third condition that the queue includes a media item subsequent to the media item being played back on the playback device, and wherein performing the first playback command corresponding to the first command keyword comprises skipping forward in the queue to play back the media item subsequent to the media item being played back on the playback device.
Example 6: The method of any of Examples 1-4, wherein the first command keyword is a pause command, wherein the one or more playback conditions corresponding to the first command keyword comprise a condition that audio content is being played back on the playback device, and wherein performing the first playback command corresponding to the first command keyword comprises pausing playback of audio content on the playback device.
Example 7: The method of any of Examples 1-4, wherein the first command keyword is a volume increase command, wherein the one or more playback conditions corresponding to the first command keyword comprise a first condition that audio content is being played back on the playback device and a second condition that a volume level on the playback device is not at maximum volume level, wherein performing the first playback command corresponding to the first command keyword comprises increasing the volume level on the playback device.
Example 8: The method of any of Examples 1-8, wherein the one or more playback conditions corresponding to the first command keyword comprises a first condition of an absence of background speech in the detected first sound.
Example 9: A tangible, non-transitory, computer-readable medium having instructions stored thereon that are executable by one or more processors to cause a playback device to perform the method of any one of Examples 1-8.
Example 10: A playback device comprising a speaker, a network interface, one or more microphones configured to detect sound, one or more processors, and a tangible, non-tangible computer-readable medium having instructions stored thereon that are executable by the one or more processors to cause the playback device to perform the method of any of Examples 1-8.
Example 11: A method to be performed by a playback device comprising a network interface and at least one microphone configured to detect sound, the method comprising monitoring an input sound-data stream representing the sound detected by the at least one microphone for (i) a wake-word event and (ii) a first command keyword event; detecting the wake-word event, wherein detecting the wake-word event comprises after detecting a first sound via the one or more microphones, determining that that the detected first sound includes a first voice input comprising a wake word; streaming, via the network interface, sound data corresponding to at least a portion of the first voice input to one or more remote servers of a voice assistant service; detecting the first command keyword event, wherein detecting the first command keyword event comprises after detecting a second sound via the one or more microphones, determining that the detected second sound includes a second voice input comprising a first command keyword and at least one keyword, wherein the first command keyword is one of a plurality of command keywords supported by the playback device, and wherein the first command keyword corresponds to a first playback command; determining, via a local natural language unit (NLU), an intent based on the at least one keyword, wherein the NLU includes a pre-determined library of keywords comprising the at least one keyword; and after (a) detecting the first command keyword event and (b) determining the intent, performing the first playback command according to the determined intent.
Example 12: The method of Example 11, wherein the method further comprises detecting a second command keyword event, wherein detecting the second command keyword event comprises after detecting a third sound via the at least one microphone, determining that the third sound includes a third voice input comprising the second command keyword; determining that the third voice input comprising the second command keyword does not include at least one other keyword from the pre-determined library of keywords; and after determining that the third voice input comprising the second command keyword does not include the at one least keyword from the pre-determined library of keywords, streaming sound data representing at least a portion of the voice input comprising the second command keyword to one or more servers of the voice assistant service for processing by the one or more remote servers of the voice assistance service.
Example 13: The method of Example 12, wherein the method further comprises playing back an audio prompt to request confirmation for invoking the voice assistant service to process the second command keyword; and after playing back the audio prompt, receiving data representing confirmation to invoke the voice assistant service for processing the second command keyword, wherein streaming the sound data representing at least a portion of the voice input comprising the second command keyword to one or more servers of the voice assistant service occurs only after receiving the data representing confirmation to invoke the voice assistant service.
Example 14: The method of any of Examples 10-13, further comprising detecting a second command keyword event, wherein detecting the second command keyword event comprises after detecting a third sound via the at least one microphone, determining that the third sound includes a third voice input comprising the second command keyword; determining that the third voice input comprising the second command keyword does not include at least one other keyword from the pre-determined library of keywords; and after determining that the third voice input comprising the second command keyword does not include the at one least keyword from the pre-determined library of keywords, performing the first playback command according to one or more default parameters.
Example 15: The method of any of Examples 10-14, wherein a first keyword of the at least one keyword in the detected second sound represents a zone name corresponding to a first zone of a media playback system, wherein performing the first playback command according to the determined intent comprises sending one or more instructions to perform the first playback command in the first zone, and wherein the media playback system comprises the playback device.
Example 16: The method of any of examples 10-15, further comprising populating the pre-determined library of keywords with zones names corresponding to respective zones within the media playback system, wherein each zone comprises one or more respective playback devices, and wherein the pre-determined library of keywords is populated with the zone name corresponding to the first zone of the media playback system.
Example 17: The method of any of Examples 10-16, further comprising discovering, via the network interface, smart home devices connected to a local area network; and populating the pre-determined library of keywords with names corresponding to respective smart home devices discovered on the local area network.
Example 18: The method of any of Examples 10-17, wherein a media playback system comprises the playback device, wherein the media playback system is registered to one or more user profiles, and wherein the functions further comprise: populating the pre-determined library of keywords with names corresponding to playlists that are designated as favorites by the one or more user profiles.
Example 19: The method of Example 18, wherein a first user profile of the one or more user profiles is associated with a user account of a first streaming audio service and a user account of a second streaming audio service, and wherein the playlists comprise a first playlist of a first streaming audio service designated as a favorite by the user account of the first streaming audio service and a second playlist comprising audio tracks from the first streaming audio service and the second streaming audio service.
Example 20: The method of any of Examples 10-16, wherein detecting the first command keyword event further comprises determining that one or more playback conditions corresponding to the first command keyword are satisfied.
Example 21: The method of Example 20, wherein the one or more playback conditions corresponding to the first command keyword comprises a first condition of an absence of background speech in the detected first sound.
Example 22: A tangible, non-transitory, computer-readable medium having instructions stored thereon that are executable by one or more processors to cause a playback device to perform the method of any one of Examples 10-21.
Example 23: A playback device comprising a speaker, a network interface, one or more microphones configured to detect sound, one or more processors, and a tangible, non-tangible computer-readable medium having instructions stored thereon that are executable by the one or more processors to cause the playback device to perform the method of any of Examples 10-21.
Example 24: A method to be performed by a playback device comprising a network interface and at least one microphone configured to detect sound, the method comprising detecting a sound via the one or more microphones; determining that (i) the detected sound includes a voice input, (ii) the detected sound excludes background speech, and (iii) the voice input includes a command keyword; and in response to determining (i) the detected sound includes a voice input, (ii) the detected sound excludes background speech, and (iii) the voice input includes a command keyword, performing a playback function corresponding to the command keyword.
Example 25: The method of Example 24, wherein the detected sound is a first detected sound, and wherein the method further comprises detecting a second sound via the at least one microphone; determining that the detected second sound includes a wake word; and after determining that the detected second sound includes the wake word, streaming, via a network interface of the playback device, voice input in the detected second sound to one or more remote servers of a voice assistant service.
Example 26: The method of any of Example 24 and Example 25, wherein determining that there is an absence of background speech in the detected second sound comprises: determining sound metadata corresponding to the detected sound; and analyzing the sound metadata to classify the detected sound according to one or more particular signatures selected from a plurality of signatures, wherein each signature of the plurality of signatures is associated with a noise source, and wherein at least one of the signatures of the plurality of signatures is a background speech signature indicative of background speech.
Example 27: The method of Example 26, wherein analyzing the sound metadata comprises: classifying frames associated with the detected sound as having a particular speech signature other than the background speech signature; and comparing a number of frames, if any, classified with the background speech signature to a number of frames classified with a signature other than the background speech signature.
Example 28: The method of any one of Example 24 and Example 25, wherein determining that there is a voice input in the detected sound comprises detecting voice activity in the detected sound.
Example 29: The method of Example 28, wherein detecting voice activity in the detected sound comprises: determining a number of first frames associated with the detected sound as containing speech; and comparing the number of first frames to a number of second frames that are (a) associated with the detected sound and (b) not indicative of speech.
Example 30: The method of Example 29, wherein the first frames comprise one or more frames generated in response to near-field voice activity and one or more frames generated in response to far-field voice activity.
Example 31: A tangible, non-transitory, computer-readable medium having instructions stored thereon that are executable by one or more processors to cause a playback device to perform the method of any one of Examples 24-30.
Example 32: A playback device comprising one or more microphones configured to detect sound, one or more processors, and a tangible, non-tangible computer-readable medium having instructions stored thereon that are executable by the one or more processors to cause the playback device to perform the method of any of Examples 24-30.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4741038 | Elko et al. | Apr 1988 | A |
| 4941187 | Slater | Jul 1990 | A |
| 4974213 | Siwecki | Nov 1990 | A |
| 5036538 | Oken et al. | Jul 1991 | A |
| 5440644 | Farinelli et al. | Aug 1995 | A |
| 5588065 | Tanaka et al. | Dec 1996 | A |
| 5740260 | Odom | Apr 1998 | A |
| 5761320 | Farinelli et al. | Jun 1998 | A |
| 5923902 | Inagaki | Jul 1999 | A |
| 5949414 | Namikata et al. | Sep 1999 | A |
| 6032202 | Lea et al. | Feb 2000 | A |
| 6088459 | Hobelsberger | Jul 2000 | A |
| 6256554 | DiLorenzo | Jul 2001 | B1 |
| 6301603 | Maher et al. | Oct 2001 | B1 |
| 6311157 | Strong | Oct 2001 | B1 |
| 6366886 | Dragosh et al. | Apr 2002 | B1 |
| 6404811 | Cvetko et al. | Jun 2002 | B1 |
| 6408078 | Hobelsberger | Jun 2002 | B1 |
| 6469633 | Wachter et al. | Oct 2002 | B1 |
| 6522886 | Youngs et al. | Feb 2003 | B1 |
| 6594347 | Calder et al. | Jul 2003 | B1 |
| 6594630 | Zlokarnik et al. | Jul 2003 | B1 |
| 6611537 | Edens et al. | Aug 2003 | B1 |
| 6611604 | Irby et al. | Aug 2003 | B1 |
| 6631410 | Kowalski et al. | Oct 2003 | B1 |
| 6757517 | Chang et al. | Jun 2004 | B2 |
| 6778869 | Champion | Aug 2004 | B2 |
| 6937977 | Gerson | Aug 2005 | B2 |
| 7099821 | Visser et al. | Aug 2006 | B2 |
| 7103542 | Doyle | Sep 2006 | B2 |
| 7130608 | Hollstrom et al. | Oct 2006 | B2 |
| 7130616 | Janik | Oct 2006 | B2 |
| 7143939 | Henzerling | Dec 2006 | B2 |
| 7174299 | Fujii et al. | Feb 2007 | B2 |
| 7228275 | Endo et al. | Jun 2007 | B1 |
| 7236773 | Thomas | Jun 2007 | B2 |
| 7295548 | Blank et al. | Nov 2007 | B2 |
| 7356471 | Ito et al. | Apr 2008 | B2 |
| 7383297 | Atsmon et al. | Jun 2008 | B1 |
| 7391791 | Balassanian et al. | Jun 2008 | B2 |
| 7483538 | McCarty et al. | Jan 2009 | B2 |
| 7571014 | Lambourne et al. | Aug 2009 | B1 |
| 7577757 | Carter et al. | Aug 2009 | B2 |
| 7630501 | Blank et al. | Dec 2009 | B2 |
| 7643894 | Braithwaite et al. | Jan 2010 | B2 |
| 7657910 | McAulay et al. | Feb 2010 | B1 |
| 7661107 | Van et al. | Feb 2010 | B1 |
| 7702508 | Bennett | Apr 2010 | B2 |
| 7792311 | Holmgren et al. | Sep 2010 | B1 |
| 7853341 | McCarty et al. | Dec 2010 | B2 |
| 7961892 | Fedigan | Jun 2011 | B2 |
| 7987294 | Bryce et al. | Jul 2011 | B2 |
| 8014423 | Thaler et al. | Sep 2011 | B2 |
| 8019076 | Lambert | Sep 2011 | B1 |
| 8032383 | Bhardwaj et al. | Oct 2011 | B1 |
| 8041565 | Bhardwaj et al. | Oct 2011 | B1 |
| 8045952 | Qureshey et al. | Oct 2011 | B2 |
| 8073125 | Zhang et al. | Dec 2011 | B2 |
| 8073681 | Baldwin et al. | Dec 2011 | B2 |
| 8103009 | Mccarty et al. | Jan 2012 | B2 |
| 8136040 | Fleming | Mar 2012 | B2 |
| 8165867 | Fish | Apr 2012 | B1 |
| 8234395 | Millington et al. | Jul 2012 | B2 |
| 8239206 | Lebeau et al. | Aug 2012 | B1 |
| 8255224 | Singleton et al. | Aug 2012 | B2 |
| 8284982 | Bailey | Oct 2012 | B2 |
| 8290603 | Lambourne | Oct 2012 | B1 |
| 8340975 | Rosenberger et al. | Dec 2012 | B1 |
| 8364481 | Strope et al. | Jan 2013 | B2 |
| 8385557 | Tashev et al. | Feb 2013 | B2 |
| 8386261 | Mellott et al. | Feb 2013 | B2 |
| 8386523 | Mody et al. | Feb 2013 | B2 |
| 8423893 | Ramsay et al. | Apr 2013 | B2 |
| 8428758 | Naik et al. | Apr 2013 | B2 |
| 8453058 | Coccaro et al. | May 2013 | B1 |
| 8473618 | Spear et al. | Jun 2013 | B2 |
| 8483853 | Lambourne | Jul 2013 | B1 |
| 8484025 | Moreno et al. | Jul 2013 | B1 |
| 8588849 | Patterson et al. | Nov 2013 | B2 |
| 8600443 | Kawaguchi et al. | Dec 2013 | B2 |
| 8710970 | Oelrich et al. | Apr 2014 | B2 |
| 8738925 | Park et al. | May 2014 | B1 |
| 8775191 | Sharifi et al. | Jul 2014 | B1 |
| 8831761 | Kemp et al. | Sep 2014 | B2 |
| 8831957 | Taubman et al. | Sep 2014 | B2 |
| 8848879 | Coughlan et al. | Sep 2014 | B1 |
| 8861756 | Zhu et al. | Oct 2014 | B2 |
| 8874448 | Kauffmann et al. | Oct 2014 | B1 |
| 8938394 | Faaborg et al. | Jan 2015 | B1 |
| 8942252 | Balassanian et al. | Jan 2015 | B2 |
| 8983383 | Haskin | Mar 2015 | B1 |
| 8983844 | Thomas et al. | Mar 2015 | B1 |
| 9015049 | Baldwin et al. | Apr 2015 | B2 |
| 9042556 | Kallai et al. | May 2015 | B2 |
| 9060224 | List | Jun 2015 | B1 |
| 9094539 | Noble | Jul 2015 | B1 |
| 9098467 | Blanksteen et al. | Aug 2015 | B1 |
| 9124650 | Maharajh et al. | Sep 2015 | B2 |
| 9124711 | Park et al. | Sep 2015 | B2 |
| 9148742 | Koulomzin et al. | Sep 2015 | B1 |
| 9190043 | Krisch et al. | Nov 2015 | B2 |
| 9208785 | Ben-David et al. | Dec 2015 | B2 |
| 9215545 | Dublin et al. | Dec 2015 | B2 |
| 9245527 | Lindahl | Jan 2016 | B2 |
| 9251793 | Lebeau et al. | Feb 2016 | B2 |
| 9253572 | Beddingfield, Sr. et al. | Feb 2016 | B2 |
| 9262612 | Cheyer | Feb 2016 | B2 |
| 9263042 | Sharifi | Feb 2016 | B1 |
| 9275637 | Salvador et al. | Mar 2016 | B1 |
| 9288597 | Carlsson et al. | Mar 2016 | B2 |
| 9300266 | Grokop | Mar 2016 | B2 |
| 9304736 | Whiteley et al. | Apr 2016 | B1 |
| 9307321 | Unruh | Apr 2016 | B1 |
| 9318107 | Sharifi | Apr 2016 | B1 |
| 9319816 | Narayanan | Apr 2016 | B1 |
| 9324322 | Torok et al. | Apr 2016 | B1 |
| 9335819 | Jaeger et al. | May 2016 | B1 |
| 9361878 | Boukadakis | Jun 2016 | B2 |
| 9361885 | Ganong, III et al. | Jun 2016 | B2 |
| 9368105 | Freed et al. | Jun 2016 | B1 |
| 9373329 | Strope et al. | Jun 2016 | B2 |
| 9374634 | Macours | Jun 2016 | B2 |
| 9386154 | Baciu et al. | Jul 2016 | B2 |
| 9390708 | Hoffmeister | Jul 2016 | B1 |
| 9401058 | De La Fuente et al. | Jul 2016 | B2 |
| 9412392 | Lindahl et al. | Aug 2016 | B2 |
| 9426567 | Lee et al. | Aug 2016 | B2 |
| 9431021 | Scalise et al. | Aug 2016 | B1 |
| 9443527 | Watanabe et al. | Sep 2016 | B1 |
| 9472201 | Sleator | Oct 2016 | B1 |
| 9472203 | Ayrapetian et al. | Oct 2016 | B1 |
| 9484030 | Meaney et al. | Nov 2016 | B1 |
| 9489948 | Chu et al. | Nov 2016 | B1 |
| 9494683 | Sadek | Nov 2016 | B1 |
| 9509269 | Rosenberg | Nov 2016 | B1 |
| 9510101 | Polleros | Nov 2016 | B1 |
| 9514476 | Kay et al. | Dec 2016 | B2 |
| 9514752 | Sharifi | Dec 2016 | B2 |
| 9516081 | Tebbs et al. | Dec 2016 | B2 |
| 9536541 | Chen et al. | Jan 2017 | B2 |
| 9548053 | Basye et al. | Jan 2017 | B1 |
| 9548066 | Jain et al. | Jan 2017 | B2 |
| 9552816 | Vanlund et al. | Jan 2017 | B2 |
| 9554210 | Ayrapetian et al. | Jan 2017 | B1 |
| 9560441 | McDonough, Jr. et al. | Jan 2017 | B1 |
| 9576591 | Kim et al. | Feb 2017 | B2 |
| 9601116 | Casado et al. | Mar 2017 | B2 |
| 9615170 | Kirsch et al. | Apr 2017 | B2 |
| 9615171 | O'Neill et al. | Apr 2017 | B1 |
| 9626695 | Balasubramanian et al. | Apr 2017 | B2 |
| 9632748 | Faaborg et al. | Apr 2017 | B2 |
| 9633186 | Ingrassia, Jr. et al. | Apr 2017 | B2 |
| 9633368 | Greenzeiger et al. | Apr 2017 | B2 |
| 9633660 | Haughay et al. | Apr 2017 | B2 |
| 9633661 | Typrin et al. | Apr 2017 | B1 |
| 9633671 | Giacobello et al. | Apr 2017 | B2 |
| 9633674 | Sinha et al. | Apr 2017 | B2 |
| 9640179 | Hart et al. | May 2017 | B1 |
| 9640183 | Jung et al. | May 2017 | B2 |
| 9641919 | Poole et al. | May 2017 | B1 |
| 9646614 | Bellegarda et al. | May 2017 | B2 |
| 9648564 | Cui et al. | May 2017 | B1 |
| 9653060 | Hilmes et al. | May 2017 | B1 |
| 9653075 | Chen et al. | May 2017 | B1 |
| 9659555 | Hilmes et al. | May 2017 | B1 |
| 9672821 | Krishnaswamy et al. | Jun 2017 | B2 |
| 9674587 | Triplett et al. | Jun 2017 | B2 |
| 9685171 | Yang | Jun 2017 | B1 |
| 9691378 | Meyers et al. | Jun 2017 | B1 |
| 9691379 | Mathias et al. | Jun 2017 | B1 |
| 9697826 | Sainath et al. | Jul 2017 | B2 |
| 9697828 | Prasad et al. | Jul 2017 | B1 |
| 9698999 | Mutagi et al. | Jul 2017 | B2 |
| 9704478 | Vitaladevuni et al. | Jul 2017 | B1 |
| 9721566 | Newendorp et al. | Aug 2017 | B2 |
| 9721568 | Polansky et al. | Aug 2017 | B1 |
| 9721570 | Beal et al. | Aug 2017 | B1 |
| 9728188 | Rosen et al. | Aug 2017 | B1 |
| 9734822 | Sundaram et al. | Aug 2017 | B1 |
| 9736578 | Iyengar et al. | Aug 2017 | B2 |
| 9743204 | Welch et al. | Aug 2017 | B1 |
| 9743207 | Hartung | Aug 2017 | B1 |
| 9747011 | Lewis et al. | Aug 2017 | B2 |
| 9747899 | Pogue et al. | Aug 2017 | B2 |
| 9747920 | Ayrapetian et al. | Aug 2017 | B2 |
| 9747926 | Sharifi et al. | Aug 2017 | B2 |
| 9749760 | Lambourne | Aug 2017 | B2 |
| 9754605 | Chhetri | Sep 2017 | B1 |
| 9762967 | Clarke et al. | Sep 2017 | B2 |
| 9769420 | Moses | Sep 2017 | B1 |
| 9779725 | Sun et al. | Oct 2017 | B2 |
| 9779732 | Lee et al. | Oct 2017 | B2 |
| 9779734 | Lee | Oct 2017 | B2 |
| 9779735 | Civelli et al. | Oct 2017 | B2 |
| 9805733 | Park | Oct 2017 | B2 |
| 9811314 | Plagge et al. | Nov 2017 | B2 |
| 9813810 | Nongpiur | Nov 2017 | B1 |
| 9813812 | Berthelsen et al. | Nov 2017 | B2 |
| 9818407 | Secker-Walker et al. | Nov 2017 | B1 |
| 9820036 | Tritschler et al. | Nov 2017 | B1 |
| 9820039 | Lang | Nov 2017 | B2 |
| 9826306 | Lang | Nov 2017 | B2 |
| 9865259 | Typrin et al. | Jan 2018 | B1 |
| 9865264 | Gelfenbeyn et al. | Jan 2018 | B2 |
| 9881616 | Beckley et al. | Jan 2018 | B2 |
| 9900723 | Choisel et al. | Feb 2018 | B1 |
| 9916839 | Scalise et al. | Mar 2018 | B1 |
| 9947316 | Millington et al. | Apr 2018 | B2 |
| 9947333 | David | Apr 2018 | B1 |
| 9972318 | Kelly et al. | May 2018 | B1 |
| 9972343 | Thorson et al. | May 2018 | B1 |
| 9973849 | Zhang et al. | May 2018 | B1 |
| 9979560 | Kim et al. | May 2018 | B2 |
| 10013381 | Mayman et al. | Jul 2018 | B2 |
| 10013995 | Lashkari et al. | Jul 2018 | B1 |
| 10025447 | Dixit et al. | Jul 2018 | B1 |
| 10026401 | Mutagi et al. | Jul 2018 | B1 |
| 10048930 | Vega et al. | Aug 2018 | B1 |
| 10049675 | Haughay | Aug 2018 | B2 |
| 10051366 | Buoni et al. | Aug 2018 | B1 |
| 10051600 | Zhong et al. | Aug 2018 | B1 |
| 10057698 | Drinkwater et al. | Aug 2018 | B2 |
| RE47049 | Zhu et al. | Sep 2018 | E |
| 10068573 | Aykac et al. | Sep 2018 | B1 |
| 10074369 | Devaraj et al. | Sep 2018 | B2 |
| 10074371 | Wang et al. | Sep 2018 | B1 |
| 10079015 | Lockhart et al. | Sep 2018 | B1 |
| 10089981 | Elangovan et al. | Oct 2018 | B1 |
| 10108393 | Millington et al. | Oct 2018 | B2 |
| 10115400 | Wilberding | Oct 2018 | B2 |
| 10116748 | Farmer et al. | Oct 2018 | B2 |
| 10127911 | Kim et al. | Nov 2018 | B2 |
| 10134388 | Lilly | Nov 2018 | B1 |
| 10134399 | Lang et al. | Nov 2018 | B2 |
| 10136204 | Poole et al. | Nov 2018 | B1 |
| 10152969 | Reilly et al. | Dec 2018 | B2 |
| 10181323 | Beckhardt et al. | Jan 2019 | B2 |
| 10186265 | Lockhart et al. | Jan 2019 | B1 |
| 10186266 | Devaraj et al. | Jan 2019 | B1 |
| 10192546 | Piersol | Jan 2019 | B1 |
| 10224056 | Torok et al. | Mar 2019 | B1 |
| 10225651 | Lang | Mar 2019 | B2 |
| 10229680 | Gillespie et al. | Mar 2019 | B1 |
| 10241754 | Kadarundalagi Raghuram Doss et al. | Mar 2019 | B1 |
| 10248376 | Keyser-Allen et al. | Apr 2019 | B2 |
| 10276161 | Hughes et al. | Apr 2019 | B2 |
| 10297256 | Reilly et al. | May 2019 | B2 |
| 10339917 | Aleksic et al. | Jul 2019 | B2 |
| 10339957 | Chenier et al. | Jul 2019 | B1 |
| 10346122 | Morgan | Jul 2019 | B1 |
| 10354650 | Gruenstein et al. | Jul 2019 | B2 |
| 10365887 | Mulherkar | Jul 2019 | B1 |
| 10365889 | Plagge et al. | Jul 2019 | B2 |
| 10366688 | Gunn et al. | Jul 2019 | B2 |
| 10366699 | Dharia et al. | Jul 2019 | B1 |
| 10374816 | Leblang et al. | Aug 2019 | B1 |
| 10381001 | Gunn et al. | Aug 2019 | B2 |
| 10381002 | Gunn et al. | Aug 2019 | B2 |
| 10381003 | Wakisaka et al. | Aug 2019 | B2 |
| 10388272 | Thomson et al. | Aug 2019 | B1 |
| 10433058 | Torgerson et al. | Oct 2019 | B1 |
| 10445365 | Luke et al. | Oct 2019 | B2 |
| 10469966 | Lambourne | Nov 2019 | B2 |
| 10499146 | Lang et al. | Dec 2019 | B2 |
| 10510340 | Fu et al. | Dec 2019 | B1 |
| 10511904 | Buoni et al. | Dec 2019 | B2 |
| 10515625 | Metallinou et al. | Dec 2019 | B1 |
| 10522146 | Tushinskiy | Dec 2019 | B1 |
| 10546583 | White et al. | Jan 2020 | B2 |
| 10573312 | Thomson et al. | Feb 2020 | B1 |
| 10573321 | Smith et al. | Feb 2020 | B1 |
| 10580405 | Wang et al. | Mar 2020 | B1 |
| 10586540 | Smith et al. | Mar 2020 | B1 |
| 10599287 | Kumar et al. | Mar 2020 | B2 |
| 10600406 | Shapiro et al. | Mar 2020 | B1 |
| 10602268 | Soto | Mar 2020 | B1 |
| 10614807 | Beckhardt et al. | Apr 2020 | B2 |
| 10621981 | Sereshki | Apr 2020 | B2 |
| 10622009 | Zhang | Apr 2020 | B1 |
| 10623811 | Cwik | Apr 2020 | B1 |
| 10624612 | Sumi et al. | Apr 2020 | B2 |
| 10643609 | Pogue et al. | May 2020 | B1 |
| 10672383 | Thomson et al. | Jun 2020 | B1 |
| 10679625 | Lockhart et al. | Jun 2020 | B1 |
| 10681460 | Woo et al. | Jun 2020 | B2 |
| 10685669 | Lan et al. | Jun 2020 | B1 |
| 10694608 | Baker et al. | Jun 2020 | B2 |
| 10706843 | Elangovan et al. | Jul 2020 | B1 |
| 10712997 | Wilberding et al. | Jul 2020 | B2 |
| 10728196 | Wang | Jul 2020 | B2 |
| 10740065 | Jarvis et al. | Aug 2020 | B2 |
| 10748531 | Kim | Aug 2020 | B2 |
| 10762896 | Yavagal et al. | Sep 2020 | B1 |
| 10777189 | Fu et al. | Sep 2020 | B1 |
| 10797667 | Fish et al. | Oct 2020 | B2 |
| 10847143 | Millington et al. | Nov 2020 | B2 |
| 10847149 | Mok et al. | Nov 2020 | B1 |
| 10848885 | Lambourne | Nov 2020 | B2 |
| RE48371 | Zhu et al. | Dec 2020 | E |
| 10867596 | Yoneda et al. | Dec 2020 | B2 |
| 10878811 | Smith et al. | Dec 2020 | B2 |
| 10878826 | Li et al. | Dec 2020 | B2 |
| 10897679 | Lambourne | Jan 2021 | B2 |
| 10911596 | Do et al. | Feb 2021 | B1 |
| 10943598 | Singh et al. | Mar 2021 | B2 |
| 10971158 | Patangay et al. | Apr 2021 | B1 |
| 11127405 | Antos et al. | Sep 2021 | B1 |
| 20010003173 | Lim | Jun 2001 | A1 |
| 20010042107 | Palm | Nov 2001 | A1 |
| 20020022453 | Balog et al. | Feb 2002 | A1 |
| 20020026442 | Lipscomb et al. | Feb 2002 | A1 |
| 20020034280 | Infosino | Mar 2002 | A1 |
| 20020046023 | Fujii et al. | Apr 2002 | A1 |
| 20020072816 | Shdema et al. | Jun 2002 | A1 |
| 20020116196 | Tran | Aug 2002 | A1 |
| 20020124097 | Isely et al. | Sep 2002 | A1 |
| 20030015354 | Edwards et al. | Jan 2003 | A1 |
| 20030038848 | Lee et al. | Feb 2003 | A1 |
| 20030040908 | Yang et al. | Feb 2003 | A1 |
| 20030070182 | Pierre et al. | Apr 2003 | A1 |
| 20030070869 | Hlibowicki | Apr 2003 | A1 |
| 20030072462 | Hlibowicki | Apr 2003 | A1 |
| 20030095672 | Hobelsberger | May 2003 | A1 |
| 20030130850 | Badt et al. | Jul 2003 | A1 |
| 20030157951 | Hasty | Aug 2003 | A1 |
| 20030235244 | Pessoa et al. | Dec 2003 | A1 |
| 20040024478 | Hans et al. | Feb 2004 | A1 |
| 20040093219 | Shin et al. | May 2004 | A1 |
| 20040105566 | Matsunaga et al. | Jun 2004 | A1 |
| 20040127241 | Shostak | Jul 2004 | A1 |
| 20040128135 | Anastasakos et al. | Jul 2004 | A1 |
| 20040234088 | McCarty et al. | Nov 2004 | A1 |
| 20050031131 | Browning et al. | Feb 2005 | A1 |
| 20050031132 | Browning et al. | Feb 2005 | A1 |
| 20050031133 | Browning et al. | Feb 2005 | A1 |
| 20050031134 | Leske | Feb 2005 | A1 |
| 20050031137 | Browning et al. | Feb 2005 | A1 |
| 20050031138 | Browning et al. | Feb 2005 | A1 |
| 20050031139 | Browning et al. | Feb 2005 | A1 |
| 20050031140 | Browning | Feb 2005 | A1 |
| 20050033582 | Gadd et al. | Feb 2005 | A1 |
| 20050047606 | Lee et al. | Mar 2005 | A1 |
| 20050077843 | Benditt | Apr 2005 | A1 |
| 20050164664 | Difonzo et al. | Jul 2005 | A1 |
| 20050195988 | Tashev et al. | Sep 2005 | A1 |
| 20050201254 | Looney et al. | Sep 2005 | A1 |
| 20050207584 | Bright | Sep 2005 | A1 |
| 20050235334 | Togashi et al. | Oct 2005 | A1 |
| 20050254662 | Blank et al. | Nov 2005 | A1 |
| 20050268234 | Rossi et al. | Dec 2005 | A1 |
| 20050283330 | Laraia et al. | Dec 2005 | A1 |
| 20050283475 | Beranek et al. | Dec 2005 | A1 |
| 20060004834 | Pyhalammi et al. | Jan 2006 | A1 |
| 20060023945 | King et al. | Feb 2006 | A1 |
| 20060041431 | Maes | Feb 2006 | A1 |
| 20060093128 | Oxford | May 2006 | A1 |
| 20060104451 | Browning et al. | May 2006 | A1 |
| 20060147058 | Wang | Jul 2006 | A1 |
| 20060190269 | Tessel et al. | Aug 2006 | A1 |
| 20060190968 | Jung et al. | Aug 2006 | A1 |
| 20060247913 | Huerta et al. | Nov 2006 | A1 |
| 20060262943 | Oxford | Nov 2006 | A1 |
| 20070018844 | Sutardja | Jan 2007 | A1 |
| 20070019815 | Asada et al. | Jan 2007 | A1 |
| 20070033043 | Hyakumoto | Feb 2007 | A1 |
| 20070038999 | Millington | Feb 2007 | A1 |
| 20070060054 | Romesburg | Mar 2007 | A1 |
| 20070071206 | Gainsboro et al. | Mar 2007 | A1 |
| 20070071255 | Schobben | Mar 2007 | A1 |
| 20070076131 | Li et al. | Apr 2007 | A1 |
| 20070076906 | Takagi et al. | Apr 2007 | A1 |
| 20070140058 | McIntosh et al. | Jun 2007 | A1 |
| 20070140521 | Mitobe et al. | Jun 2007 | A1 |
| 20070142944 | Goldberg et al. | Jun 2007 | A1 |
| 20070147651 | Mitobe et al. | Jun 2007 | A1 |
| 20070201639 | Park et al. | Aug 2007 | A1 |
| 20070254604 | Kim | Nov 2007 | A1 |
| 20070286426 | Xiang et al. | Dec 2007 | A1 |
| 20080037814 | Shau | Feb 2008 | A1 |
| 20080090537 | Sutardja | Apr 2008 | A1 |
| 20080090617 | Sutardja | Apr 2008 | A1 |
| 20080144858 | Khawand et al. | Jun 2008 | A1 |
| 20080146289 | Korneluk et al. | Jun 2008 | A1 |
| 20080182518 | Lo | Jul 2008 | A1 |
| 20080207115 | Lee et al. | Aug 2008 | A1 |
| 20080208594 | Cross et al. | Aug 2008 | A1 |
| 20080221897 | Cerra et al. | Sep 2008 | A1 |
| 20080247530 | Barton et al. | Oct 2008 | A1 |
| 20080248797 | Freeman et al. | Oct 2008 | A1 |
| 20080291896 | Tuubel et al. | Nov 2008 | A1 |
| 20080301729 | Broos et al. | Dec 2008 | A1 |
| 20090003620 | McKillop et al. | Jan 2009 | A1 |
| 20090005893 | Sugii et al. | Jan 2009 | A1 |
| 20090010445 | Matsuo et al. | Jan 2009 | A1 |
| 20090018828 | Nakadai et al. | Jan 2009 | A1 |
| 20090043206 | Towfiq et al. | Feb 2009 | A1 |
| 20090052688 | Ishibashi et al. | Feb 2009 | A1 |
| 20090076821 | Brenner et al. | Mar 2009 | A1 |
| 20090153289 | Hope et al. | Jun 2009 | A1 |
| 20090191854 | Beason | Jul 2009 | A1 |
| 20090197524 | Haff et al. | Aug 2009 | A1 |
| 20090220107 | Every et al. | Sep 2009 | A1 |
| 20090228919 | Zott et al. | Sep 2009 | A1 |
| 20090238377 | Ramakrishnan et al. | Sep 2009 | A1 |
| 20090238386 | Usher et al. | Sep 2009 | A1 |
| 20090248397 | Garcia et al. | Oct 2009 | A1 |
| 20090249222 | Schmidt et al. | Oct 2009 | A1 |
| 20090264072 | Dai | Oct 2009 | A1 |
| 20090323907 | Gupta et al. | Dec 2009 | A1 |
| 20090326949 | Douthitt et al. | Dec 2009 | A1 |
| 20100014690 | Wolff et al. | Jan 2010 | A1 |
| 20100023638 | Bowman | Jan 2010 | A1 |
| 20100035593 | Franco et al. | Feb 2010 | A1 |
| 20100041443 | Yokota | Feb 2010 | A1 |
| 20100070922 | Demaio et al. | Mar 2010 | A1 |
| 20100075723 | Min et al. | Mar 2010 | A1 |
| 20100088100 | Lindahl | Apr 2010 | A1 |
| 20100092004 | Kuze | Apr 2010 | A1 |
| 20100161335 | Whynot | Jun 2010 | A1 |
| 20100172516 | Lastrucci | Jul 2010 | A1 |
| 20100178873 | Lee et al. | Jul 2010 | A1 |
| 20100179874 | Higgins et al. | Jul 2010 | A1 |
| 20100185448 | Meisel | Jul 2010 | A1 |
| 20100211199 | Naik et al. | Aug 2010 | A1 |
| 20110033059 | Bhaskar et al. | Feb 2011 | A1 |
| 20110035580 | Wang et al. | Feb 2011 | A1 |
| 20110044461 | Kuech et al. | Feb 2011 | A1 |
| 20110044489 | Saiki et al. | Feb 2011 | A1 |
| 20110046952 | Koshinaka | Feb 2011 | A1 |
| 20110066634 | Phillips et al. | Mar 2011 | A1 |
| 20110091055 | Leblanc | Apr 2011 | A1 |
| 20110103615 | Sun | May 2011 | A1 |
| 20110131032 | Yang, II et al. | Jun 2011 | A1 |
| 20110145581 | Malhotra et al. | Jun 2011 | A1 |
| 20110170707 | Yamada et al. | Jul 2011 | A1 |
| 20110182436 | Murgia et al. | Jul 2011 | A1 |
| 20110202924 | Banguero et al. | Aug 2011 | A1 |
| 20110218656 | Bishop et al. | Sep 2011 | A1 |
| 20110267985 | Wilkinson et al. | Nov 2011 | A1 |
| 20110276333 | Wang et al. | Nov 2011 | A1 |
| 20110280422 | Neumeyer et al. | Nov 2011 | A1 |
| 20110285808 | Feng et al. | Nov 2011 | A1 |
| 20110289506 | Trivi et al. | Nov 2011 | A1 |
| 20110299706 | Sakai | Dec 2011 | A1 |
| 20120020486 | Fried et al. | Jan 2012 | A1 |
| 20120022863 | Cho et al. | Jan 2012 | A1 |
| 20120022864 | Leman et al. | Jan 2012 | A1 |
| 20120078635 | Rothkopf et al. | Mar 2012 | A1 |
| 20120086568 | Scott et al. | Apr 2012 | A1 |
| 20120123268 | Tanaka et al. | May 2012 | A1 |
| 20120128160 | Kim et al. | May 2012 | A1 |
| 20120131125 | Seidel et al. | May 2012 | A1 |
| 20120148075 | Goh et al. | Jun 2012 | A1 |
| 20120162540 | Ouchi et al. | Jun 2012 | A1 |
| 20120163603 | Abe et al. | Jun 2012 | A1 |
| 20120177215 | Bose et al. | Jul 2012 | A1 |
| 20120183149 | Hiroe | Jul 2012 | A1 |
| 20120224715 | Kikkeri | Sep 2012 | A1 |
| 20120297284 | Matthews, III et al. | Nov 2012 | A1 |
| 20120308044 | Vander et al. | Dec 2012 | A1 |
| 20120308046 | Muza | Dec 2012 | A1 |
| 20130006453 | Wang et al. | Jan 2013 | A1 |
| 20130024018 | Chang et al. | Jan 2013 | A1 |
| 20130034241 | Pandey et al. | Feb 2013 | A1 |
| 20130039527 | Jensen et al. | Feb 2013 | A1 |
| 20130051755 | Brown et al. | Feb 2013 | A1 |
| 20130058492 | Silzle et al. | Mar 2013 | A1 |
| 20130066453 | Seefeldt | Mar 2013 | A1 |
| 20130080146 | Kato et al. | Mar 2013 | A1 |
| 20130124211 | Mcdonough | May 2013 | A1 |
| 20130148821 | Sorensen | Jun 2013 | A1 |
| 20130170647 | Reilly et al. | Jul 2013 | A1 |
| 20130179173 | Lee et al. | Jul 2013 | A1 |
| 20130183944 | Mozer et al. | Jul 2013 | A1 |
| 20130191119 | Sugiyama | Jul 2013 | A1 |
| 20130191122 | Mason | Jul 2013 | A1 |
| 20130198298 | Li et al. | Aug 2013 | A1 |
| 20130211826 | Mannby | Aug 2013 | A1 |
| 20130216056 | Thyssen | Aug 2013 | A1 |
| 20130262101 | Srinivasan | Oct 2013 | A1 |
| 20130315420 | You | Nov 2013 | A1 |
| 20130317635 | Bates et al. | Nov 2013 | A1 |
| 20130322462 | Poulsen | Dec 2013 | A1 |
| 20130322665 | Bennett et al. | Dec 2013 | A1 |
| 20130324031 | Loureiro | Dec 2013 | A1 |
| 20130329896 | Krishnaswamy et al. | Dec 2013 | A1 |
| 20130331970 | Beckhardt et al. | Dec 2013 | A1 |
| 20130332165 | Beckley et al. | Dec 2013 | A1 |
| 20130339028 | Rosner et al. | Dec 2013 | A1 |
| 20130343567 | Triplett et al. | Dec 2013 | A1 |
| 20140003611 | Mohammad et al. | Jan 2014 | A1 |
| 20140003625 | Sheen et al. | Jan 2014 | A1 |
| 20140003635 | Mohammad et al. | Jan 2014 | A1 |
| 20140005813 | Reimann | Jan 2014 | A1 |
| 20140006026 | Lamb et al. | Jan 2014 | A1 |
| 20140006825 | Shenhav | Jan 2014 | A1 |
| 20140019743 | Deluca | Jan 2014 | A1 |
| 20140034929 | Hamada et al. | Feb 2014 | A1 |
| 20140046464 | Reimann | Feb 2014 | A1 |
| 20140064501 | Olsen et al. | Mar 2014 | A1 |
| 20140073298 | Rossmann | Mar 2014 | A1 |
| 20140075306 | Rega | Mar 2014 | A1 |
| 20140075311 | Boettcher et al. | Mar 2014 | A1 |
| 20140094151 | Klappert et al. | Apr 2014 | A1 |
| 20140100854 | Chen et al. | Apr 2014 | A1 |
| 20140109138 | Cannistraro et al. | Apr 2014 | A1 |
| 20140122075 | Bak et al. | May 2014 | A1 |
| 20140136195 | Abdossalami et al. | May 2014 | A1 |
| 20140145168 | Ohsawa et al. | May 2014 | A1 |
| 20140146983 | Kim et al. | May 2014 | A1 |
| 20140149118 | Lee et al. | May 2014 | A1 |
| 20140163978 | Basye et al. | Jun 2014 | A1 |
| 20140164400 | Kruglick | Jun 2014 | A1 |
| 20140167931 | Lee et al. | Jun 2014 | A1 |
| 20140168344 | Shoemake et al. | Jun 2014 | A1 |
| 20140172953 | Blanksteen | Jun 2014 | A1 |
| 20140181271 | Millington | Jun 2014 | A1 |
| 20140192986 | Lee et al. | Jul 2014 | A1 |
| 20140195252 | Gruber et al. | Jul 2014 | A1 |
| 20140207457 | Biatov et al. | Jul 2014 | A1 |
| 20140214429 | Pantel | Jul 2014 | A1 |
| 20140215332 | Lee et al. | Jul 2014 | A1 |
| 20140219472 | Huang et al. | Aug 2014 | A1 |
| 20140222436 | Binder et al. | Aug 2014 | A1 |
| 20140229184 | Shires | Aug 2014 | A1 |
| 20140244013 | Reilly | Aug 2014 | A1 |
| 20140244712 | Walters et al. | Aug 2014 | A1 |
| 20140249817 | Hart et al. | Sep 2014 | A1 |
| 20140252386 | Ito et al. | Sep 2014 | A1 |
| 20140254805 | Su et al. | Sep 2014 | A1 |
| 20140258292 | Thramann et al. | Sep 2014 | A1 |
| 20140259075 | Chang et al. | Sep 2014 | A1 |
| 20140269757 | Park et al. | Sep 2014 | A1 |
| 20140270282 | Tammi et al. | Sep 2014 | A1 |
| 20140274185 | Luna et al. | Sep 2014 | A1 |
| 20140274203 | Ganong, III et al. | Sep 2014 | A1 |
| 20140274218 | Kadiwala et al. | Sep 2014 | A1 |
| 20140277650 | Zurek et al. | Sep 2014 | A1 |
| 20140278372 | Nakadai et al. | Sep 2014 | A1 |
| 20140278933 | McMillan | Sep 2014 | A1 |
| 20140291642 | Watabe et al. | Oct 2014 | A1 |
| 20140303969 | Inose et al. | Oct 2014 | A1 |
| 20140310002 | Nitz et al. | Oct 2014 | A1 |
| 20140310614 | Jones | Oct 2014 | A1 |
| 20140330896 | Addala et al. | Nov 2014 | A1 |
| 20140334645 | Yun et al. | Nov 2014 | A1 |
| 20140340888 | Ishisone et al. | Nov 2014 | A1 |
| 20140357248 | Tonshal et al. | Dec 2014 | A1 |
| 20140358535 | Lee et al. | Dec 2014 | A1 |
| 20140363022 | Dizon et al. | Dec 2014 | A1 |
| 20140363024 | Apodaca | Dec 2014 | A1 |
| 20140365227 | Cash et al. | Dec 2014 | A1 |
| 20140369491 | Kloberdans et al. | Dec 2014 | A1 |
| 20140372109 | Iyer et al. | Dec 2014 | A1 |
| 20150006176 | Pogue et al. | Jan 2015 | A1 |
| 20150006184 | Marti et al. | Jan 2015 | A1 |
| 20150010169 | Popova et al. | Jan 2015 | A1 |
| 20150014680 | Yamazaki et al. | Jan 2015 | A1 |
| 20150016642 | Walsh et al. | Jan 2015 | A1 |
| 20150018992 | Griffiths et al. | Jan 2015 | A1 |
| 20150019201 | Schoenbach | Jan 2015 | A1 |
| 20150019219 | Tzirkel-Hancock et al. | Jan 2015 | A1 |
| 20150036831 | Klippel | Feb 2015 | A1 |
| 20150039303 | Lesso et al. | Feb 2015 | A1 |
| 20150039317 | Klein et al. | Feb 2015 | A1 |
| 20150058018 | Georges et al. | Feb 2015 | A1 |
| 20150063580 | Huang et al. | Mar 2015 | A1 |
| 20150066479 | Pasupalak et al. | Mar 2015 | A1 |
| 20150086034 | Lombardi et al. | Mar 2015 | A1 |
| 20150091709 | Reichert et al. | Apr 2015 | A1 |
| 20150092947 | Gossain et al. | Apr 2015 | A1 |
| 20150104037 | Lee et al. | Apr 2015 | A1 |
| 20150106085 | Lindahl | Apr 2015 | A1 |
| 20150110294 | Chen et al. | Apr 2015 | A1 |
| 20150112672 | Giacobello et al. | Apr 2015 | A1 |
| 20150124975 | Pontoppidan | May 2015 | A1 |
| 20150128065 | Torii et al. | May 2015 | A1 |
| 20150134456 | Baldwin | May 2015 | A1 |
| 20150154976 | Mutagi | Jun 2015 | A1 |
| 20150161990 | Sharifi | Jun 2015 | A1 |
| 20150169279 | Duga | Jun 2015 | A1 |
| 20150170645 | Di et al. | Jun 2015 | A1 |
| 20150172843 | Quan | Jun 2015 | A1 |
| 20150179181 | Morris et al. | Jun 2015 | A1 |
| 20150180432 | Gao et al. | Jun 2015 | A1 |
| 20150181318 | Gautama et al. | Jun 2015 | A1 |
| 20150189438 | Hampiholi et al. | Jul 2015 | A1 |
| 20150200454 | Heusdens et al. | Jul 2015 | A1 |
| 20150201271 | Diethorn et al. | Jul 2015 | A1 |
| 20150221678 | Yamazaki et al. | Aug 2015 | A1 |
| 20150222563 | Burns et al. | Aug 2015 | A1 |
| 20150222987 | Angel, Jr. et al. | Aug 2015 | A1 |
| 20150228274 | Leppanen et al. | Aug 2015 | A1 |
| 20150228803 | Koezuka et al. | Aug 2015 | A1 |
| 20150237406 | Ochoa et al. | Aug 2015 | A1 |
| 20150243287 | Nakano et al. | Aug 2015 | A1 |
| 20150245152 | Ding et al. | Aug 2015 | A1 |
| 20150245154 | Dadu et al. | Aug 2015 | A1 |
| 20150249889 | Iyer et al. | Sep 2015 | A1 |
| 20150253292 | Larkin et al. | Sep 2015 | A1 |
| 20150253960 | Lin et al. | Sep 2015 | A1 |
| 20150254057 | Klein et al. | Sep 2015 | A1 |
| 20150263174 | Yamazaki et al. | Sep 2015 | A1 |
| 20150271593 | Sun et al. | Sep 2015 | A1 |
| 20150277846 | Yen et al. | Oct 2015 | A1 |
| 20150280676 | Holman et al. | Oct 2015 | A1 |
| 20150296299 | Klippel et al. | Oct 2015 | A1 |
| 20150302856 | Kim et al. | Oct 2015 | A1 |
| 20150319529 | Klippel | Nov 2015 | A1 |
| 20150325267 | Lee et al. | Nov 2015 | A1 |
| 20150331663 | Beckhardt et al. | Nov 2015 | A1 |
| 20150334471 | Innes et al. | Nov 2015 | A1 |
| 20150338917 | Steiner et al. | Nov 2015 | A1 |
| 20150341406 | Rockefeller et al. | Nov 2015 | A1 |
| 20150346845 | Di et al. | Dec 2015 | A1 |
| 20150348548 | Piernot et al. | Dec 2015 | A1 |
| 20150348551 | Gruber et al. | Dec 2015 | A1 |
| 20150355878 | Corbin | Dec 2015 | A1 |
| 20150363061 | De, III et al. | Dec 2015 | A1 |
| 20150363401 | Chen et al. | Dec 2015 | A1 |
| 20150370531 | Faaborg | Dec 2015 | A1 |
| 20150371657 | Gao et al. | Dec 2015 | A1 |
| 20150371659 | Gao | Dec 2015 | A1 |
| 20150371664 | Bar-Or et al. | Dec 2015 | A1 |
| 20150380010 | Srinivasan et al. | Dec 2015 | A1 |
| 20150382047 | Van Os et al. | Dec 2015 | A1 |
| 20160007116 | Holman | Jan 2016 | A1 |
| 20160021458 | Johnson et al. | Jan 2016 | A1 |
| 20160026428 | Morganstern et al. | Jan 2016 | A1 |
| 20160029142 | Isaac et al. | Jan 2016 | A1 |
| 20160035321 | Cho et al. | Feb 2016 | A1 |
| 20160035337 | Aggarwal et al. | Feb 2016 | A1 |
| 20160036962 | Rand et al. | Feb 2016 | A1 |
| 20160042748 | Jain et al. | Feb 2016 | A1 |
| 20160044151 | Shoemaker et al. | Feb 2016 | A1 |
| 20160050488 | Matheja et al. | Feb 2016 | A1 |
| 20160055850 | Nakadai et al. | Feb 2016 | A1 |
| 20160057522 | Choisel et al. | Feb 2016 | A1 |
| 20160070526 | Sheen | Mar 2016 | A1 |
| 20160072804 | Chien et al. | Mar 2016 | A1 |
| 20160077710 | Lewis et al. | Mar 2016 | A1 |
| 20160086609 | Yue et al. | Mar 2016 | A1 |
| 20160088036 | Corbin et al. | Mar 2016 | A1 |
| 20160088392 | Huttunen et al. | Mar 2016 | A1 |
| 20160093304 | Kim et al. | Mar 2016 | A1 |
| 20160094718 | Mani et al. | Mar 2016 | A1 |
| 20160094917 | Wilk et al. | Mar 2016 | A1 |
| 20160098393 | Hebert | Apr 2016 | A1 |
| 20160098992 | Renard et al. | Apr 2016 | A1 |
| 20160103653 | Jang | Apr 2016 | A1 |
| 20160104480 | Sharifi | Apr 2016 | A1 |
| 20160111110 | Gautama et al. | Apr 2016 | A1 |
| 20160125876 | Schroeter et al. | May 2016 | A1 |
| 20160127780 | Roberts et al. | May 2016 | A1 |
| 20160133259 | Rubin et al. | May 2016 | A1 |
| 20160134966 | Fitzgerald et al. | May 2016 | A1 |
| 20160134982 | Iyer | May 2016 | A1 |
| 20160140957 | Duta et al. | May 2016 | A1 |
| 20160148615 | Lee et al. | May 2016 | A1 |
| 20160154089 | Altman | Jun 2016 | A1 |
| 20160155442 | Kannan et al. | Jun 2016 | A1 |
| 20160155443 | Khan et al. | Jun 2016 | A1 |
| 20160157035 | Russell et al. | Jun 2016 | A1 |
| 20160162469 | Santos | Jun 2016 | A1 |
| 20160171976 | Sun et al. | Jun 2016 | A1 |
| 20160173578 | Sharma et al. | Jun 2016 | A1 |
| 20160173983 | Berthelsen et al. | Jun 2016 | A1 |
| 20160180853 | Vanlund et al. | Jun 2016 | A1 |
| 20160189716 | Lindahl et al. | Jun 2016 | A1 |
| 20160196499 | Khan et al. | Jul 2016 | A1 |
| 20160203331 | Khan et al. | Jul 2016 | A1 |
| 20160210110 | Feldman | Jul 2016 | A1 |
| 20160212538 | Fullam et al. | Jul 2016 | A1 |
| 20160216938 | Millington | Jul 2016 | A1 |
| 20160217789 | Lee et al. | Jul 2016 | A1 |
| 20160225385 | Hammarqvist | Aug 2016 | A1 |
| 20160232451 | Scherzer | Aug 2016 | A1 |
| 20160234204 | Rishi et al. | Aug 2016 | A1 |
| 20160234615 | Lambourne | Aug 2016 | A1 |
| 20160239255 | Chavez et al. | Aug 2016 | A1 |
| 20160240192 | Raghuvir | Aug 2016 | A1 |
| 20160241976 | Pearson | Aug 2016 | A1 |
| 20160253050 | Mishra et al. | Sep 2016 | A1 |
| 20160260431 | Newendorp et al. | Sep 2016 | A1 |
| 20160283841 | Sainath et al. | Sep 2016 | A1 |
| 20160302018 | Russell et al. | Oct 2016 | A1 |
| 20160314782 | Klimanis | Oct 2016 | A1 |
| 20160316293 | Klimanis | Oct 2016 | A1 |
| 20160322045 | Hatfield et al. | Nov 2016 | A1 |
| 20160336519 | Seo et al. | Nov 2016 | A1 |
| 20160343866 | Koezuka et al. | Nov 2016 | A1 |
| 20160343949 | Seo et al. | Nov 2016 | A1 |
| 20160343954 | Seo et al. | Nov 2016 | A1 |
| 20160345114 | Hanna et al. | Nov 2016 | A1 |
| 20160352915 | Gautama | Dec 2016 | A1 |
| 20160353217 | Starobin et al. | Dec 2016 | A1 |
| 20160353218 | Starobin et al. | Dec 2016 | A1 |
| 20160357503 | Triplett et al. | Dec 2016 | A1 |
| 20160364206 | Keyser-Allen et al. | Dec 2016 | A1 |
| 20160366515 | Mendes et al. | Dec 2016 | A1 |
| 20160372113 | David et al. | Dec 2016 | A1 |
| 20160372688 | Seo et al. | Dec 2016 | A1 |
| 20160373269 | Okubo et al. | Dec 2016 | A1 |
| 20160373909 | Rasmussen et al. | Dec 2016 | A1 |
| 20160379634 | Yamamoto et al. | Dec 2016 | A1 |
| 20170003931 | Dvortsov et al. | Jan 2017 | A1 |
| 20170012207 | Seo et al. | Jan 2017 | A1 |
| 20170012232 | Kataishi et al. | Jan 2017 | A1 |
| 20170019732 | Mendes et al. | Jan 2017 | A1 |
| 20170025124 | Mixter et al. | Jan 2017 | A1 |
| 20170025615 | Seo et al. | Jan 2017 | A1 |
| 20170025630 | Seo et al. | Jan 2017 | A1 |
| 20170026769 | Patel | Jan 2017 | A1 |
| 20170032244 | Kurata | Feb 2017 | A1 |
| 20170034263 | Archambault et al. | Feb 2017 | A1 |
| 20170039025 | Kielak | Feb 2017 | A1 |
| 20170040002 | Basson et al. | Feb 2017 | A1 |
| 20170040018 | Tormey | Feb 2017 | A1 |
| 20170041724 | Master et al. | Feb 2017 | A1 |
| 20170053648 | Chi | Feb 2017 | A1 |
| 20170053650 | Ogawa | Feb 2017 | A1 |
| 20170060526 | Barton et al. | Mar 2017 | A1 |
| 20170062734 | Suzuki et al. | Mar 2017 | A1 |
| 20170070478 | Park et al. | Mar 2017 | A1 |
| 20170076212 | Shams et al. | Mar 2017 | A1 |
| 20170076720 | Gopalan et al. | Mar 2017 | A1 |
| 20170076726 | Bae | Mar 2017 | A1 |
| 20170078824 | Heo | Mar 2017 | A1 |
| 20170083285 | Meyers et al. | Mar 2017 | A1 |
| 20170084277 | Sharifi | Mar 2017 | A1 |
| 20170084292 | Yoo | Mar 2017 | A1 |
| 20170084295 | Tsiartas | Mar 2017 | A1 |
| 20170090864 | Jorgovanovic | Mar 2017 | A1 |
| 20170092278 | Evermann et al. | Mar 2017 | A1 |
| 20170092297 | Sainath et al. | Mar 2017 | A1 |
| 20170092299 | Matsuo | Mar 2017 | A1 |
| 20170092889 | Seo et al. | Mar 2017 | A1 |
| 20170092890 | Seo et al. | Mar 2017 | A1 |
| 20170094215 | Western | Mar 2017 | A1 |
| 20170103748 | Weissberg et al. | Apr 2017 | A1 |
| 20170103754 | Higbie et al. | Apr 2017 | A1 |
| 20170103755 | Jeon et al. | Apr 2017 | A1 |
| 20170110124 | Boesen et al. | Apr 2017 | A1 |
| 20170110144 | Sharifi et al. | Apr 2017 | A1 |
| 20170117497 | Seo et al. | Apr 2017 | A1 |
| 20170123251 | Nakada et al. | May 2017 | A1 |
| 20170125037 | Shin | May 2017 | A1 |
| 20170125456 | Kasahara | May 2017 | A1 |
| 20170133007 | Drewes | May 2017 | A1 |
| 20170133011 | Chen et al. | May 2017 | A1 |
| 20170134872 | Silva et al. | May 2017 | A1 |
| 20170139720 | Stein | May 2017 | A1 |
| 20170140449 | Kannan | May 2017 | A1 |
| 20170140748 | Roberts et al. | May 2017 | A1 |
| 20170140759 | Kumar et al. | May 2017 | A1 |
| 20170151930 | Boesen | Jun 2017 | A1 |
| 20170177585 | Rodger et al. | Jun 2017 | A1 |
| 20170178662 | Ayrapetian et al. | Jun 2017 | A1 |
| 20170180561 | Kadiwala et al. | Jun 2017 | A1 |
| 20170188150 | Brunet et al. | Jun 2017 | A1 |
| 20170188437 | Banta | Jun 2017 | A1 |
| 20170193999 | Aleksic et al. | Jul 2017 | A1 |
| 20170206896 | Ko et al. | Jul 2017 | A1 |
| 20170206900 | Lee et al. | Jul 2017 | A1 |
| 20170214996 | Yeo | Jul 2017 | A1 |
| 20170236512 | Williams et al. | Aug 2017 | A1 |
| 20170236515 | Pinsky et al. | Aug 2017 | A1 |
| 20170242649 | Jarvis et al. | Aug 2017 | A1 |
| 20170242651 | Lang et al. | Aug 2017 | A1 |
| 20170242653 | Lang et al. | Aug 2017 | A1 |
| 20170242657 | Jarvis et al. | Aug 2017 | A1 |
| 20170243576 | Millington et al. | Aug 2017 | A1 |
| 20170243587 | Plagge et al. | Aug 2017 | A1 |
| 20170245076 | Kusano et al. | Aug 2017 | A1 |
| 20170255612 | Sarikaya et al. | Sep 2017 | A1 |
| 20170257686 | Gautama et al. | Sep 2017 | A1 |
| 20170269975 | Wood et al. | Sep 2017 | A1 |
| 20170270919 | Parthasarathi et al. | Sep 2017 | A1 |
| 20170278512 | Pandya et al. | Sep 2017 | A1 |
| 20170287485 | Civelli et al. | Oct 2017 | A1 |
| 20170300990 | Tanaka et al. | Oct 2017 | A1 |
| 20170330565 | Daley et al. | Nov 2017 | A1 |
| 20170332168 | Moghimi et al. | Nov 2017 | A1 |
| 20170346872 | Naik et al. | Nov 2017 | A1 |
| 20170352357 | Fink | Dec 2017 | A1 |
| 20170353789 | Kim et al. | Dec 2017 | A1 |
| 20170357475 | Lee et al. | Dec 2017 | A1 |
| 20170357478 | Piersol et al. | Dec 2017 | A1 |
| 20170366393 | Shaker et al. | Dec 2017 | A1 |
| 20170374454 | Bernardini et al. | Dec 2017 | A1 |
| 20170374552 | Xia et al. | Dec 2017 | A1 |
| 20180018964 | Reilly et al. | Jan 2018 | A1 |
| 20180018967 | Lang et al. | Jan 2018 | A1 |
| 20180020306 | Sheen | Jan 2018 | A1 |
| 20180025733 | Qian et al. | Jan 2018 | A1 |
| 20180033428 | Kim et al. | Feb 2018 | A1 |
| 20180033438 | Toma et al. | Feb 2018 | A1 |
| 20180040324 | Wilberding | Feb 2018 | A1 |
| 20180047394 | Tian et al. | Feb 2018 | A1 |
| 20180053504 | Wang et al. | Feb 2018 | A1 |
| 20180054506 | Hart et al. | Feb 2018 | A1 |
| 20180061396 | Srinivasan et al. | Mar 2018 | A1 |
| 20180061402 | Devaraj et al. | Mar 2018 | A1 |
| 20180061404 | Devaraj et al. | Mar 2018 | A1 |
| 20180061419 | Melendo Casado et al. | Mar 2018 | A1 |
| 20180061420 | Patil et al. | Mar 2018 | A1 |
| 20180062871 | Jones et al. | Mar 2018 | A1 |
| 20180084367 | Greff et al. | Mar 2018 | A1 |
| 20180088900 | Glaser et al. | Mar 2018 | A1 |
| 20180091898 | Yoon et al. | Mar 2018 | A1 |
| 20180091913 | Hartung et al. | Mar 2018 | A1 |
| 20180096683 | James et al. | Apr 2018 | A1 |
| 20180096696 | Mixter | Apr 2018 | A1 |
| 20180107446 | Wilberding et al. | Apr 2018 | A1 |
| 20180108351 | Beckhardt et al. | Apr 2018 | A1 |
| 20180122372 | Wanderlust | May 2018 | A1 |
| 20180122378 | Mixter et al. | May 2018 | A1 |
| 20180130469 | Gruenstein et al. | May 2018 | A1 |
| 20180132217 | Stirling-Gallacher | May 2018 | A1 |
| 20180132298 | Birnam et al. | May 2018 | A1 |
| 20180137861 | Ogawa et al. | May 2018 | A1 |
| 20180152557 | White et al. | May 2018 | A1 |
| 20180165055 | Yu et al. | Jun 2018 | A1 |
| 20180167981 | Jon et al. | Jun 2018 | A1 |
| 20180174597 | Lee et al. | Jun 2018 | A1 |
| 20180182383 | Kim et al. | Jun 2018 | A1 |
| 20180182390 | Hughes et al. | Jun 2018 | A1 |
| 20180182397 | Carbune et al. | Jun 2018 | A1 |
| 20180188948 | Ouyang et al. | Jul 2018 | A1 |
| 20180190274 | Kirazci et al. | Jul 2018 | A1 |
| 20180190285 | Heckman et al. | Jul 2018 | A1 |
| 20180197533 | Lyon et al. | Jul 2018 | A1 |
| 20180199146 | Sheen | Jul 2018 | A1 |
| 20180204569 | Nadkar et al. | Jul 2018 | A1 |
| 20180205963 | Matei et al. | Jul 2018 | A1 |
| 20180210698 | Park et al. | Jul 2018 | A1 |
| 20180211665 | Park et al. | Jul 2018 | A1 |
| 20180218747 | Moghimi et al. | Aug 2018 | A1 |
| 20180219976 | Decenzo et al. | Aug 2018 | A1 |
| 20180225933 | Park et al. | Aug 2018 | A1 |
| 20180228006 | Baker et al. | Aug 2018 | A1 |
| 20180233130 | Kaskari et al. | Aug 2018 | A1 |
| 20180233136 | Torok et al. | Aug 2018 | A1 |
| 20180233137 | Torok et al. | Aug 2018 | A1 |
| 20180233139 | Finkelstein et al. | Aug 2018 | A1 |
| 20180233142 | Koishida et al. | Aug 2018 | A1 |
| 20180233150 | Gruenstein et al. | Aug 2018 | A1 |
| 20180234765 | Torok et al. | Aug 2018 | A1 |
| 20180261213 | Arik et al. | Sep 2018 | A1 |
| 20180262793 | Lau et al. | Sep 2018 | A1 |
| 20180262831 | Matheja et al. | Sep 2018 | A1 |
| 20180270565 | Ganesh | Sep 2018 | A1 |
| 20180277107 | Kim | Sep 2018 | A1 |
| 20180277113 | Hartung et al. | Sep 2018 | A1 |
| 20180277119 | Baba et al. | Sep 2018 | A1 |
| 20180277133 | Deetz et al. | Sep 2018 | A1 |
| 20180286394 | Li et al. | Oct 2018 | A1 |
| 20180286414 | Ravindran et al. | Oct 2018 | A1 |
| 20180293484 | Wang et al. | Oct 2018 | A1 |
| 20180308470 | Park et al. | Oct 2018 | A1 |
| 20180314552 | Kim et al. | Nov 2018 | A1 |
| 20180322891 | Van Den Oord et al. | Nov 2018 | A1 |
| 20180324756 | Ryu et al. | Nov 2018 | A1 |
| 20180335903 | Coffman et al. | Nov 2018 | A1 |
| 20180336274 | Choudhury et al. | Nov 2018 | A1 |
| 20180349093 | McCarty et al. | Dec 2018 | A1 |
| 20180358009 | Daley et al. | Dec 2018 | A1 |
| 20180365567 | Kolavennu et al. | Dec 2018 | A1 |
| 20180367944 | Heo et al. | Dec 2018 | A1 |
| 20190012141 | Piersol et al. | Jan 2019 | A1 |
| 20190013019 | Lawrence | Jan 2019 | A1 |
| 20190014592 | Hampel et al. | Jan 2019 | A1 |
| 20190033446 | Bultan et al. | Jan 2019 | A1 |
| 20190042187 | Truong et al. | Feb 2019 | A1 |
| 20190043488 | Booklet et al. | Feb 2019 | A1 |
| 20190043492 | Lang | Feb 2019 | A1 |
| 20190051298 | Lee et al. | Feb 2019 | A1 |
| 20190066672 | Wood et al. | Feb 2019 | A1 |
| 20190074025 | Lashkari et al. | Mar 2019 | A1 |
| 20190079721 | Vega et al. | Mar 2019 | A1 |
| 20190079724 | Feuz et al. | Mar 2019 | A1 |
| 20190081507 | Ide | Mar 2019 | A1 |
| 20190081810 | Jung | Mar 2019 | A1 |
| 20190082255 | Tajiri et al. | Mar 2019 | A1 |
| 20190087455 | He et al. | Mar 2019 | A1 |
| 20190088261 | Lang et al. | Mar 2019 | A1 |
| 20190090056 | Rexach et al. | Mar 2019 | A1 |
| 20190096408 | Li et al. | Mar 2019 | A1 |
| 20190098400 | Buoni et al. | Mar 2019 | A1 |
| 20190104119 | Giorgi et al. | Apr 2019 | A1 |
| 20190104373 | Wodrich et al. | Apr 2019 | A1 |
| 20190108839 | Reilly et al. | Apr 2019 | A1 |
| 20190115011 | Khellah et al. | Apr 2019 | A1 |
| 20190130906 | Kobayashi et al. | May 2019 | A1 |
| 20190163153 | Price et al. | May 2019 | A1 |
| 20190172452 | Smith et al. | Jun 2019 | A1 |
| 20190173687 | Mackay et al. | Jun 2019 | A1 |
| 20190179607 | Thangarathnam et al. | Jun 2019 | A1 |
| 20190179611 | Wojogbe et al. | Jun 2019 | A1 |
| 20190182072 | Roe et al. | Jun 2019 | A1 |
| 20190206391 | Busch et al. | Jul 2019 | A1 |
| 20190206412 | Li et al. | Jul 2019 | A1 |
| 20190219976 | Giorgi et al. | Jul 2019 | A1 |
| 20190220246 | Orr et al. | Jul 2019 | A1 |
| 20190221206 | Chen et al. | Jul 2019 | A1 |
| 20190237067 | Friedman et al. | Aug 2019 | A1 |
| 20190239008 | Lambourne | Aug 2019 | A1 |
| 20190239009 | Lambourne | Aug 2019 | A1 |
| 20190243603 | Keyser-Allen et al. | Aug 2019 | A1 |
| 20190243606 | Jayakumar et al. | Aug 2019 | A1 |
| 20190244608 | Choi et al. | Aug 2019 | A1 |
| 20190281397 | Lambourne | Sep 2019 | A1 |
| 20190287546 | Ganeshkumar | Sep 2019 | A1 |
| 20190288970 | Siddiq | Sep 2019 | A1 |
| 20190289367 | Siddiq | Sep 2019 | A1 |
| 20190295542 | Huang et al. | Sep 2019 | A1 |
| 20190295563 | Kamdar et al. | Sep 2019 | A1 |
| 20190297388 | Panchaksharaiah et al. | Sep 2019 | A1 |
| 20190304443 | Bhagwan | Oct 2019 | A1 |
| 20190311710 | Eraslan et al. | Oct 2019 | A1 |
| 20190311712 | Firik et al. | Oct 2019 | A1 |
| 20190311720 | Pasko | Oct 2019 | A1 |
| 20190317606 | Jain et al. | Oct 2019 | A1 |
| 20190342962 | Chang et al. | Nov 2019 | A1 |
| 20190347063 | Liu et al. | Nov 2019 | A1 |
| 20190348044 | Chun et al. | Nov 2019 | A1 |
| 20190362714 | Mori et al. | Nov 2019 | A1 |
| 20190364375 | Soto et al. | Nov 2019 | A1 |
| 20190364422 | Zhuo | Nov 2019 | A1 |
| 20200007987 | Woo et al. | Jan 2020 | A1 |
| 20200034492 | Verbeke et al. | Jan 2020 | A1 |
| 20200051554 | Kim et al. | Feb 2020 | A1 |
| 20200074990 | Kim et al. | Mar 2020 | A1 |
| 20200090647 | Kurtz | Mar 2020 | A1 |
| 20200092687 | Devaraj et al. | Mar 2020 | A1 |
| 20200098354 | Lin et al. | Mar 2020 | A1 |
| 20200098379 | Tai et al. | Mar 2020 | A1 |
| 20200105245 | Gupta et al. | Apr 2020 | A1 |
| 20200105256 | Fainberg et al. | Apr 2020 | A1 |
| 20200105264 | Jang et al. | Apr 2020 | A1 |
| 20200152206 | Shen et al. | May 2020 | A1 |
| 20200175989 | Lockhart et al. | Jun 2020 | A1 |
| 20200184964 | Myers et al. | Jun 2020 | A1 |
| 20200184980 | Wilberding | Jun 2020 | A1 |
| 20200193973 | Tolomei et al. | Jun 2020 | A1 |
| 20200211539 | Lee | Jul 2020 | A1 |
| 20200211550 | Pan et al. | Jul 2020 | A1 |
| 20200211556 | Mixter et al. | Jul 2020 | A1 |
| 20200213729 | Soto | Jul 2020 | A1 |
| 20200216089 | Garcia et al. | Jul 2020 | A1 |
| 20200234709 | Kunitake | Jul 2020 | A1 |
| 20200251107 | Wang et al. | Aug 2020 | A1 |
| 20200265838 | Lee et al. | Aug 2020 | A1 |
| 20200310751 | Anand et al. | Oct 2020 | A1 |
| 20200336846 | Rohde et al. | Oct 2020 | A1 |
| 20200395006 | Smith et al. | Dec 2020 | A1 |
| 20200395010 | Smith et al. | Dec 2020 | A1 |
| 20200395013 | Smith et al. | Dec 2020 | A1 |
| 20200409652 | Wilberding et al. | Dec 2020 | A1 |
| 20210035561 | D'Amato et al. | Feb 2021 | A1 |
| 20210035572 | D'Amato et al. | Feb 2021 | A1 |
| 20210067867 | Kagoshima | Mar 2021 | A1 |
| 20210118429 | Shan | Apr 2021 | A1 |
| 20210166680 | Jung et al. | Jun 2021 | A1 |
| 20210183366 | Reinspach et al. | Jun 2021 | A1 |
| 20210280185 | Tan et al. | Sep 2021 | A1 |
| Number | Date | Country |
|---|---|---|
| 2017100486 | Jun 2017 | AU |
| 2017100581 | Jun 2017 | AU |
| 101310558 | Nov 2008 | CN |
| 101480039 | Jul 2009 | CN |
| 101661753 | Mar 2010 | CN |
| 101686282 | Mar 2010 | CN |
| 101907983 | Dec 2010 | CN |
| 102123188 | Jul 2011 | CN |
| 102256098 | Nov 2011 | CN |
| 102567468 | Jul 2012 | CN |
| 103052001 | Apr 2013 | CN |
| 103181192 | Jun 2013 | CN |
| 103210663 | Jul 2013 | CN |
| 103546616 | Jan 2014 | CN |
| 103811007 | May 2014 | CN |
| 104010251 | Aug 2014 | CN |
| 104035743 | Sep 2014 | CN |
| 104053088 | Sep 2014 | CN |
| 104092936 | Oct 2014 | CN |
| 104104769 | Oct 2014 | CN |
| 104538030 | Apr 2015 | CN |
| 104575504 | Apr 2015 | CN |
| 104635539 | May 2015 | CN |
| 104865550 | Aug 2015 | CN |
| 105187907 | Dec 2015 | CN |
| 105204357 | Dec 2015 | CN |
| 105206281 | Dec 2015 | CN |
| 105284076 | Jan 2016 | CN |
| 105493442 | Apr 2016 | CN |
| 106028223 | Oct 2016 | CN |
| 106375902 | Feb 2017 | CN |
| 106531165 | Mar 2017 | CN |
| 106708403 | May 2017 | CN |
| 107004410 | Aug 2017 | CN |
| 107919123 | Apr 2018 | CN |
| 109712626 | May 2019 | CN |
| 1349146 | Oct 2003 | EP |
| 1389853 | Feb 2004 | EP |
| 2051542 | Apr 2009 | EP |
| 2166737 | Mar 2010 | EP |
| 2683147 | Jan 2014 | EP |
| 2986034 | Feb 2016 | EP |
| 3128767 | Feb 2017 | EP |
| 3133595 | Feb 2017 | EP |
| 2351021 | Sep 2017 | EP |
| 3270377 | Jan 2018 | EP |
| 3285502 | Feb 2018 | EP |
| 2001236093 | Aug 2001 | JP |
| 2003223188 | Aug 2003 | JP |
| 2004347943 | Dec 2004 | JP |
| 2004354721 | Dec 2004 | JP |
| 2005242134 | Sep 2005 | JP |
| 2005250867 | Sep 2005 | JP |
| 2005284492 | Oct 2005 | JP |
| 2006092482 | Apr 2006 | JP |
| 2007013400 | Jan 2007 | JP |
| 2007142595 | Jun 2007 | JP |
| 2008079256 | Apr 2008 | JP |
| 2008158868 | Jul 2008 | JP |
| 2010141748 | Jun 2010 | JP |
| 2013037148 | Feb 2013 | JP |
| 2014071138 | Apr 2014 | JP |
| 2014137590 | Jul 2014 | JP |
| 2015161551 | Sep 2015 | JP |
| 2015527768 | Sep 2015 | JP |
| 2016095383 | May 2016 | JP |
| 2017072857 | Apr 2017 | JP |
| 2017129860 | Jul 2017 | JP |
| 2017227912 | Dec 2017 | JP |
| 2018055259 | Apr 2018 | JP |
| 20100036351 | Apr 2010 | KR |
| 100966415 | Jun 2010 | KR |
| 20100111071 | Oct 2010 | KR |
| 20130050987 | May 2013 | KR |
| 20140005410 | Jan 2014 | KR |
| 20140035310 | Mar 2014 | KR |
| 20140054643 | May 2014 | KR |
| 20140112900 | Sep 2014 | KR |
| 200153994 | Jul 2001 | WO |
| 2003093950 | Nov 2003 | WO |
| 2008048599 | Apr 2008 | WO |
| 2012166386 | Dec 2012 | WO |
| 2013184792 | Dec 2013 | WO |
| 2014064531 | May 2014 | WO |
| 2014159581 | Oct 2014 | WO |
| 2015017303 | Feb 2015 | WO |
| 2015037396 | Mar 2015 | WO |
| 2015105788 | Jul 2015 | WO |
| 2015131024 | Sep 2015 | WO |
| 2015178950 | Nov 2015 | WO |
| 2016014142 | Jan 2016 | WO |
| 2016022926 | Feb 2016 | WO |
| 2016033364 | Mar 2016 | WO |
| 2016057268 | Apr 2016 | WO |
| 2016085775 | Jun 2016 | WO |
| 2016165067 | Oct 2016 | WO |
| 2016171956 | Oct 2016 | WO |
| 2016200593 | Dec 2016 | WO |
| 2017039632 | Mar 2017 | WO |
| 2017058654 | Apr 2017 | WO |
| 2017138934 | Aug 2017 | WO |
| 2017147075 | Aug 2017 | WO |
| 2017147936 | Sep 2017 | WO |
| 2018027142 | Feb 2018 | WO |
| 2018067404 | Apr 2018 | WO |
| Entry |
|---|
| US 9,299,346 B1, 03/2016, Hart et al. (withdrawn) |
| Notice of Allowance dated Mar. 27, 2019, issued in connection with U.S. Appl. No. 16/214,666, filed Dec. 10, 2018, 6 pages. |
| Notice of Allowance dated Mar. 28, 2018, issued in connection with U.S. Appl. No. 15/699,982, filed Sep. 8, 2017, 17 pages. |
| Notice of Allowance dated Dec. 29, 2017, issued in connection with U.S. Appl. No. 15/131,776, filed Apr. 18, 2016, 13 pages. |
| Notice of Allowance dated Apr. 3, 2019, issued in connection with U.S. Appl. No. 16/160,107, filed Oct. 15, 2018, 7 pages. |
| Notice of Allowance dated Jul. 30, 2018, issued in connection with U.S. Appl. No. 15/098,718, filed Apr. 14, 2016, 5 pages. |
| Notice of Allowance dated Jul. 30, 2019, issued in connection with U.S. Appl. No. 15/131,254, filed Apr. 18, 2016, 9 pages. |
| Notice of Allowance dated Nov. 30, 2018, issued in connection with U.S. Appl. No. 15/438,725, filed Feb. 21, 2017, 5 pages. |
| Notice of Allowance dated May 31, 2019, issued in connection with U.S. Appl. No. 15/717,621, filed Sep. 27, 2017, 9 pages. |
| Notice of Allowance dated Oct. 5, 2018, issued in connection with U.S. Appl. No. 15/211,748, filed Jul. 15, 2018,10 pages. |
| Notice of Allowance dated Feb. 6, 2019, issued in connection with U.S. Appl. No. 16/102,153, filed Aug. 13, 2018, 9 pages. |
| Notice of Allowance dated Jun. 7, 2019, issued in connection with U.S. Appl. No. 16/102,153, filed Aug. 13, 2018, 9 pages. |
| Notice of Allowance dated Aug. 9, 2018, issued in connection with U.S. Appl. No. 15/229,868, filed Aug. 5, 2016, 11 pages. |
| Notice of Allowance dated Mar. 9, 2018, issued in connection with U.S. Appl. No. 15/584,782, filed May 2, 2017, 8 pages. |
| Palm, Inc., “Handbook for the Palm VII Handheld,” May 2000, 311 pages. |
| Preinterview First Office Action dated Aug. 5, 2019, issued in connection with U.S. Appl. No. 16/434,426, filed Jun. 7, 2019, 4 pages. |
| Presentations at WinHEC 2000, May 2000, 138 pages. |
| Restriction Requirement dated Aug. 14, 2019, issued in connection with U.S. Appl. No. 16/214,711, filed Dec. 10, 2018, 5 pages. |
| Restriction Requirement dated Aug. 9, 2018, issued in connection with U.S. Appl. No. 15/717,621, filed Sep. 27, 2017, 8 pages. |
| Souden et al. “An Integrated Solution for Online Multichannel Noise Tracking and Reduction.” IEEE Transactions on Audio, Speech, and Language Processing, vol. 19. No. 7, Sep. 7, 2011, 11 pages. |
| Souden et al. “Gaussian Model-Based Multichannel Speech Presence Probability” IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, No. 5, Jul. 5, 2010, 6pages. |
| Souden et al. “On Optimal Frequency-Domain Multichannel Linear Filtering for Noise Reduction.” IEEE Transactions an Audio, Speech, and Language Processing, vol. 18, No. 2, Feb. 2010, 17pages. |
| Steven J. Nowlan and Geoffrey E. Hinton “Simplifying Neural Networks by Soft Weight-Sharing” Neural Computation 4, 1992, 21 pages. |
| Tsiami et al. “Experiments in acoustic source localization using sparse arrays in adverse indoors environments”, 2014 22nd European Signal Processing Conference, Sep. 1, 2014, 5 pages. |
| Tweet: “How to start using Google app voice commands to make your life easier Share This Story shop ©Bullet”, Jan. 21, 2016, https://bgr.com/2016/01/21/best-ok-google-voice-commands/, 3 page. |
| Ullrich et al. “Soft Weight-Sharing for Neural Network Compression.” ICLR 2017, 16 pages. |
| U.S. Appl. No. 60/490,768, filed Jul. 28, 2003, entitled “Method for synchronizing audio playback between multiple networked devices,” 13 pages. |
| U.S. Appl. No. 60/825,407, filed Sep. 12, 2006, entitled “Controlling and manipulating groupings in a multi-zone music or media system,” 82 pages. |
| UPnP; “Universal Plug and Play Device Architecture,” Jun. 8, 2000; version 1.0; Microsoft Corporation; pp. 1-54. |
| Vacher at al. “Recognition of voice commands by multisource ASR and noise cancellation in a smart home environment” Signal Processing Conference 2012 Proceedings of the 20th European, IEEE, Aug. 27, 2012, 5 pages. |
| Vacher et al. “Speech Recognition in a Smart Home: Some Experiments for Telemonitoring,” 2009 Proceedings of the 5th Conference on Speech Technology and Human-Computer Dialogoue, Constant, 2009, 10 pages. |
| “S Voice or Google Now?”; https://web.archive.org/web/20160807040123/lowdown.carphonewarehouse.com/news/s-voice-or-google-now/ . . . , Apr. 28, 2015; 4 pages. |
| Wung et al. “Robust Acoustic Echo Cancellation in the Short-Time Fourier Transform Domain Using Adaptive Crossband Filters” IEEE International Conference on Acoustic, Speech and Signal Processing ICASSP, 2014, p. 1300-1304. |
| Xiao et al. “A Learning-Based Approach to Direction of Arrival Estimation in Noisy and Reverberant Environments,” 2015 IEEE International Conference on Acoustics, Speech and Signal Processing, Apr. 19, 2015, 5 pages. |
| Yamaha DME 64 Owner's Manual; copyright 2004, 80 pages. |
| Yamaha DME Designer 3.5 setup manual guide; copyright 2004, 16 pages. |
| Yamaha DME Designer 3.5 User Manual; Copyright 2004, 507 pages. |
| Non-Final Office Action dated Jun. 27, 2018, issued in connection with U.S. Appl. No. 15/438,749, filed Feb. 21, 2017, 16 pages. |
| Non-Final Office Action dated Jun. 27, 2019, issued in connection with U.S. Appl. No. 16/437,437, filed Jun. 11, 2019, 8 pages. |
| Non-Final Office Action dated Jun. 27, 2019, issued in connection with U.S. Appl. No. 16/437,476, filed Jun. 11, 2019, 8 pages. |
| Non-Final Office Action dated Mar. 29, 2019, issued in connection with U.S. Appl. No. 16/102,650, filed Aug. 13, 2018, 11 pages. |
| Non-Final Office Action dated Jul. 3, 2019, issued in connection with U.S. Appl. No. 15/948,541, filed Apr. 9, 2018, 7 pages. |
| Non-Final Office Action dated May 3, 2019, issued in connection with U.S. Appl. No. 16/178,122, filed Nov. 1, 2018, 14 pages. |
| Non-Final Office Action dated Oct. 3, 2018, issued in connection with U.S. Appl. No. 16/102,153, filed Aug. 13, 2018, 20 pages. |
| Non-Final Office Action dated Apr. 30, 2019, issued in connection with U.S. Appl. No. 15/718,521, filed Sep. 28, 2017, 39 pages. |
| Non-Final Office Action dated Jun. 30, 2017, issued in connection with U.S. Appl. No. 15/277,810, filed Sep. 27, 2016, 13 pages. |
| Non-Final Office Action dated Apr. 4, 2019, issued in connection with U.S. Appl. No. 15/718,911, filed Sep. 28, 2017, 21 pages. |
| Non-Final Office Action dated Jan. 4, 2019, issued in connection with U.S. Appl. No. 15/948,541, filed Apr. 9, 2018, 6 pages. |
| Non-Final Office Action dated Feb. 6, 2018, issued in connection with U.S. Appl. No. 15/211,689, filed Jul. 15, 2016, 32 pages. |
| Non-Final Office Action dated Feb. 6, 2018, issued in connection with U.S. Appl. No. 15/237,133, filed Aug. 15, 2016, 6 pages. |
| Non-Final Office Action dated Sep. 6, 2017, issued in connection with U.S. Appl. No. 15/131,254, filed Apr. 18, 2016, 13 pages. |
| Non-Final Office Action dated Sep. 6, 2018, issued in connection with U.S. Appl. No. 15/098,760, filed Apr. 14, 2016, 29 pages. |
| Non-Final Office Action dated Apr. 9, 2018, issued in connection with U.S. Appl. No. 15/804,776, filed Nov. 6, 2017, 18 pages. |
| Non-Final Office Action dated May 9, 2018, issued in connection with U.S. Appl. No. 15/818,051, filed Nov. 20, 2017, 22 pages. |
| Notice of Allowance dated Dec. 4, 2017, issued in connection with U.S. Appl. No. 15/277,810, filed Sep. 27, 2016, 5 pages. |
| Notice of Allowance dated Jul. 5, 2018, issued in connection with U.S. Appl. No. 15/237,133, filed Aug. 15, 2016, 5 pages. |
| Notice of Allowance dated Jul. 9, 2018, issued in connection with U.S. Appl. No. 15/438,741, filed Feb. 21, 2017, 5 pages. |
| Notice of Allowance dated Apr. 1, 2019, issued in connection with U.S. Appl. No. 15/935,966, filed Mar. 26, 2018, 5 pages. |
| Notice of Allowance dated Aug. 1, 2018, issued in connection with U.S. Appl. No. 15/297,627, filed Oct. 19, 2016, 9 pages. |
| Notice of Allowance dated Apr. 11, 2018, issued in connection with U.S. Appl. No. 15/719,454, filed Sep. 28, 2017, 15 pages. |
| Notice of Allowance dated Dec. 12, 2018, issued in connection with U.S. Appl. No. 15/811,468, filed Nov. 13, 2017, 9 pages. |
| Notice of Allowance dated Jul. 12, 2017, issued in connection with U.S. Appl. No. 15/098,805, filed Apr. 14, 2016, 8 pages. |
| Notice of Allowance dated Jun. 12, 2019, issued in connection with U.S. Appl. No. 15/670,361, filed Aug. 7, 2017, 7 pages. |
| Notice of Allowance dated Sep. 12, 2018, issued in connection with U.S. Appl. No. 15/438,744, filed Feb. 21, 2017, 15 pages. |
| Notice of Allowance dated Dec. 13, 2017, issued in connection with U.S. Appl. No. 15/784,952, filed Oct. 16, 2017, 9 pages. |
| Notice of Allowance dated Feb. 13, 2019, issued in connection with U.S. Appl. No. 15/959,907, filed Apr. 23, 2018, 10 pages. |
| Notice of Allowance dated Aug. 14, 2017, issued in connection with U.S. Appl. No. 15/098,867, filed Apr. 14, 2016, 10 pages. |
| Notice of Allowance dated Feb. 14, 2017, issued in connection with U.S. Appl. No. 15/229,855, filed Aug. 5, 2016, 11 pages. |
| Notice of Allowance dated Jun. 14, 2017, issued in connection with U.S. Appl. No. 15/282,554, filed Sep. 30, 2016, 11 pages. |
| Notice of Allowance dated Nov. 14, 2018, issued in connection with U.S. Appl. No. 15/297,627, filed Oct. 19, 2016, 5 pages. |
| Notice of Allowance dated Dec. 15, 2017, issued in connection with U.S. Appl. No. 15/223,218, filed Jul. 29, 2016, 7 pages. |
| Notice of Allowance dated Mar. 15, 2019, issued in connection with U.S. Appl. No. 15/804,776, filed Nov. 6, 2017, 9 pages. |
| Notice of Allowance dated Aug. 16, 2017, issued in connection with U.S. Appl. No. 15/098,892, filed an Apr. 14, 2016, 9 pages. |
| Notice of Allowance dated Aug. 17, 2017, issued in connection with U.S. Appl. No. 15/131,244, filed Apr. 18, 2016, 9 pages. |
| Notice of Allowance dated Jul. 17, 2019, issued in connection with U.S. Appl. No. 15/718,911, filed Sep. 28, 2017, 5 pages. |
| Notice of Allowance dated Sep. 17, 2018, issued in connection with U.S. Appl. No. 15/211,689, filed Jul. 15, 2016, 6 pages. |
| Notice of Allowance dated Apr. 18, 2019, issued in connection with U.S. Appl. No. 16/173,797, filed Oct. 29, 2018, 9 pages. |
| Notice of Allowance dated Jul. 18, 2019, issued in connection with U.S. Appl. No. 15/438,749, filed Feb. 21, 2017, 9 pages. |
| Notice of Allowance dated Jul. 18, 2019, issued in connection with U.S. Appl. No. 15/721,141, filed Sep. 29, 2017, 8 pages. |
| Notice of Allowance dated Dec. 19, 2018, issued in connection with U.S. Appl. No. 15/818,051, filed Nov. 20, 2017, 9 pages. |
| Notice of Allowance dated Jul. 19, 2018, issued in connection with U.S. Appl. No. 15/681,937, filed Aug. 21, 2017, 7 pages. |
| Notice of Allowance dated Aug. 2, 2019, issued in connection with U.S. Appl. No. 16/102,650, filed Aug. 13, 2018, 5 pages. |
| Notice of Allowance dated Mar. 20, 2018, issued in connection with U.S. Appl. No. 15/784,952, filed Oct. 16, 2017, 7 pages. |
| Notice of Allowance dated Sep. 20, 2018, issued in connection with U.S. Appl. No. 15/946,599, filed Apr. 5, 2018, 7 pages. |
| Notice of Allowance dated Aug. 22, 2017, issued in connection with U.S. Appl. No. 15/273,679, filed Sep. 22, 2016, 5 pages. |
| Notice of Allowance dated Jan. 22, 2018, issued in connection with U.S. Appl. No. 15/178,180, filed Jun. 9, 2016, 9 pages. |
| Notice of Allowance dated Apr. 24, 2019, issued in connection with U.S. Appl. No. 16/154,469, filed Oct. 3, 2018, 5 pages. |
| Advisory Action dated Jun. 28, 2018, issued in connection with U.S. Appl. No. 15/438,744, filed Feb. 21, 2017, 3 pages. |
| Advisory Action dated Dec. 31, 2018, issued in connection with U.S. Appl. No. 15/804,776, filed Nov. 6, 2017, 4 pages. |
| AudioTron Quick Start Guide, Version 1.0, Mar. 2001, 24 pages. |
| AudioTron Reference Manual, Version 3.0, May 2002, 70 pages. |
| AudioTron Setup Guide, Version 3.0, May 2002, 38 pages. |
| Australian Patent Office, Examination Report dated Oct. 30, 2018, issued in connection with Australian Application No. 2017222436, 3 pages. |
| “Automatic Parameter Tying in Neural Networks” ICLR 2018, 14 pages. |
| Bluetooth. “Specification of the Bluetooth System: The ad hoc SCATTERNET for affordable and highly functional wireless connectivity,” Core, Version 1.0 A, Jul. 26, 1999, 1068 pages. |
| Bluetooth. “Specification of the Bluetooth System: Wireless connections made easy,” Core, Version 1.0 B, Dec. 1, 1999, 1076 pages. |
| Canadian Patent Office, Canadian Office Action dated Nov. 14, 2018, issued in connection with Canadian Application No. 3015491, 3 pages. |
| Chinese Patent Office, First Office Action and Translation dated Mar. 20, 2019, issued in connection with Chinese Application No. 201780025028.2, 18 pages. |
| Chinese Patent Office, First Office Action and Translation dated Mar. 27, 2019, issued in connection with Chinese Application No. 201780025029.7, 9 pages. |
| Chinese Patent Office, Second Office Action and Translation dated Jul. 18, 2019, issued in connection with Chinese Application No. 201780025029.7, 14 pages. |
| Chinese Patent Office, Translation of Office Action dated Jul. 18, 2019, issued in connection with Chinese Application No. 201780025029.7, 8 pages. |
| Corrected Notice of Allowability dated Mar. 8, 2017, issued in connection with U.S. Appl. No. 15/229,855, filed Aug. 5, 2016, 6 pages. |
| Dell, Inc. “Dell Digital Audio Receiver: Reference Guide,” Jun. 2000, 70 pages. |
| Dell, Inc. “Start Here,” Jun. 2000, 2 pages. |
| “Denon 2003-2004 Product Catalog,” Denon, 2003-2004, 44 pages. |
| European Patent Office, European Extended Search Report dated Jan. 3, 2019, issued in connection with European Application No. 177570702, 8 pages. |
| European Patent Office, European Extended Search Report dated Jan. 3, 2019, issued in connection with European Application No. 17757075.1, 9 pages. |
| European Patent Office, European Extended Search Report dated Oct. 30, 2017, issued in connection with EP Application No. 17174435.2, 11 pages. |
| European Patent Office, European Office Action dated Jan. 22, 2019, issued in connection with European Application No. 17174435.2, 9 pages. |
| Fadilpasic,“Cortana can now be the default PDA on your Android”, IT Pro Portal: Accessed via WayBack Machine http://web.archive.org/web/20171129124915/https://www.itproportal.com/2015/08/11/cortana-can-now-be- . . . , Aug. 11, 2015, 6 pages. |
| Final Office Action dated Oct. 6, 2017, issued in connection with U.S. Appl. No. 15/098,760, filed Apr. 14, 2016, 25 pages. |
| Final Office Action dated Apr. 11, 2019, issued in connection with U.S. Appl. No. 15/131,254, filed Apr. 18, 2016, 17 pages. |
| Final Office Action dated Aug. 11, 2017, issued in connection with U.S. Appl. No. 15/131,776, filed Apr. 18, 2016, 7 pages. |
| Final Office Action dated Apr. 13, 2018, issued in connection with U.S. Appl. No. 15/131,254, filed Apr. 18, 2016, 18 pages. |
| Final Office Action dated Apr. 13, 2018, issued in connection with U.S. Appl. No. 15/438,744, filed Feb. 21, 2017, 20 pages. |
| Final Office Action dated Jun. 15, 2017, issued in connection with U.S. Appl. No. 15/098,718, filed Apr. 14, 2016, 15 pages. |
| Final Office Action dated Oct. 15, 2018, issued in connection with U.S. Appl. No. 15/804,776, filed Nov. 6, 2017, 18 pages. |
| Final Office Action dated Oct. 16, 2018, issued in connection with U.S. Appl. No. 15/438,725, filed Feb. 21, 2017, 10 pages. |
| Final Office Action dated Feb. 21, 2018, issued in connection with U.S. Appl. No. 15/297,627, filed Oct. 19, 2016, 12 pages. |
| Final Office Action dated Apr. 26, 2019, issued in connection with U.S. Appl. No. 15/721,141, filed Sep. 29, 2017, 20 pages. |
| Final Office Action dated Apr. 30, 2019, issued in connection with U.S. Appl. No. 15/098,760, filed Apr. 14, 2016, 6 pages. |
| Final Office Action dated Feb. 5, 2019, issued in connection with U.S. Appl. No. 15/438,749, filed Feb. 21, 2017, 17 pages. |
| Fiorenza Arisio et al. “Deliverable 1.1 User Study, analysis of requirements and definition of the application task,” May 31, 2012, http://dirha.fbk.eu/sites/dirha.fbk.eu/files/docs/DIRHA_D1.1., 31 pages. |
| First Action Interview Office Action dated Aug. 14, 2019, issued in connection with U.S. Appl. No. 16/227,308, filed Dec. 20, 2018, 4 pages. |
| First Action Interview Office Action dated Jul. 5, 2019, issued in connection with U.S. Appl. No. 16/227,308, filed Dec. 20, 2018, 4 pages. |
| Freiberger, Karl, “Development and Evaluation of Source Localization Algorithms for Coincident Microphone Arrays,” Diploma Thesis, Apr. 1, 2010, 106 pages. |
| Giacobello et al. “A Sparse Nonuniformly Partitioned Multidelay Filter for Acoustic Echo Cancellation,” 2013, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, Oct. 2013, New Paltz, NY, 4 pages. |
| Giacobello et al. “Tuning Methodology for Speech Enhancement Algorithms using a Simulated Conversational Database and Perceptual Objective Measures,” 2014, 4th Joint Workshop on Hands-free Speech Communication and Microphone Arrays HSCMA, 2014, 5 pages. |
| Han et al. “Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.” ICLR 2016, Feb. 15, 2016, 14 pages. |
| Helwani et al. “Source-domain adaptive filtering for MIMO systems with application to acoustic echo cancellation”, Acoustics Speech and Signal Processing, 2010 IEEE International Conference, Mar. 14, 2010, 4 pages. |
| Hirano et al. “A Noise-Robust Stochastic Gradient Algorithm with an Adaptive Step-Size Suitable for Mobile Hands-Free Telephones,” 1995, International Conference on Acoustics, Speech, and Signal Processing, vol. 2, 4 pages. |
| International Bureau, International Preliminary Report on Patentability, dated Apr. 11, 2019, issued in connection with International Application No. PCT/US2017/0054063, filed on Sep. 28, 2017, 9 pages. |
| International Bureau, International Preliminary Report on Patentability, dated Apr. 23, 2019, issued in connection with International Application No. PCT/US2017/057220, filed on Oct. 18, 2017, 7 pages. |
| International Bureau, International Preliminary Report on Patentability, dated Sep. 7, 2018, issued in connection with International Application No. PCT/US2017/018728, filed on Feb. 21, 2017, 8 pages. |
| International Bureau, International Preliminary Report on Patentability, dated Sep. 7, 2018, issued in connection with International Application No. PCT/US2017/018739, filed on Feb. 21, 2017, 7 pages. |
| International Searching Authority, International Search Report and Written Opinion dated Dec. 19, 2018, in connection with International Application No. PCT/US2018/053517, 13 pages. |
| International Searching Authority, International Search Report and Written Opinion dated Nov. 22, 2017, issued in connection with International Application No. PCT/US2017/054063, filed on Sep. 28, 2017, 11 pages. |
| International Searching Authority, International Search Report and Written Opinion dated Jan. 23, 2018, issued in connection with International Application No. PCT/US2017/57220, filed on Oct. 18, 2017, 8 pages. |
| International Searching Authority, International Search Report and Written Opinion dated May 23, 2017, issued in connection with International Application No. PCT/US2017/018739, Filed on Feb. 21, 2017, 10 pages. |
| International Searching Authority, International Search Report and Written Opinion dated Oct. 23, 2017, issued in connection with International Application No. PCT/US2017/042170, filed on Jul. 14, 2017, 15 pages. |
| International Searching Authority, International Search Report and Written Opinion dated Oct. 24, 2017, issued in connection with International Application No. PCT/US2017/042227, filed on Jul. 14, 2017, 16 pages. |
| International Searching Authority, International Search Report and Written Opinion dated May 30, 2017, issued in connection with International Application No. PCT/US2017/018728, Filed on Feb. 21, 2017, 11 pages. |
| Jo et al., “Synchronized One-to-many Media Streaming with Adaptive Playout Control,” Proceedings of SPIE, 2002, pp. 71-82, vol. 4861. |
| Jones, Stephen, “Dell Digital Audio Receiver: Digital upgrade for your analog stereo,” Analog Stereo, Jun. 24, 2000 retrieved Jun. 18, 2014, 2 pages. |
| Jose Alvarez and Mathieu Salzmann “Compression-aware Training of Deep Networks” 31st Conference on Neural Information Processing Systems, Nov. 13, 2017, 12pages. |
| Korean Patent Office, Korean Office Action and Translation dated Aug. 16, 2019, issued in connection with Korean Application No. 10-2018-7027452, 14 pages. |
| Korean Patent Office, Korean Office Action dated May 8, 2019, issued in connection with Korean Application No. 10-2018-7027451, 7 pages. |
| Korean Patent Office, Korean Office Action dated May 8, 2019, issued in connection with Korean Application No. 10-2018-7027452, 5 pages. |
| Louderback, Jim, “Affordable Audio Receiver Furnishes Homes With MP3,” TechTV Vault. Jun. 28, 2000 retrieved Jul. 10, 2014, 2 pages. |
| Maja Taseska and Emanual A.P. Habets, “MMSE-Based Blind Source Extraction in Diffuse Noise Fields Using a Complex Coherence-Based a Priori Sap Estimator.” International Workshop on Acoustic Signal Enhancement 2012, Sep. 1-6, 2012, 4pages. |
| Morales-Cordovilla et al. “Room Localization for Distant Speech Recognition,” Proceedings of Interspeech 2014, Sep. 14, 2014, 4 pages. |
| Newman, Jared. “Chromecast Audio's multi-room support has arrived,” Dec. 11, 2015, https://www.pcworld.com/article/3014204/customer-electronic/chromcase-audio-s-multi-room-support-has . . . , 1 page. |
| Ngo et al. “Incorporating the Conditional Speech Presence Probability in Multi-Channel Wiener Filter Based Noise Reduction in Hearing Aids.” EURASIP Journal on Advances in Signal Processing vol. 2009, Jun. 2, 2009, 11 pages. |
| Non-Final Office Action dated Jun. 1, 2017, issued in connection with U.S. Appl. No. 15/223,218, filed Jul. 29, 2016, 7 pages. |
| Non-Final Office Action dated Nov. 2, 2017, issued in connection with U.S. Appl. No. 15/584,782, filed May 2, 2017, 11 pages. |
| Non-Final Office Action dated Nov. 3, 2017, issued in connection with U.S. Appl. No. 15/438,741, filed Feb. 21, 2017, 11 pages. |
| Non-Final Office Action dated Feb. 7, 2017, issued in connection with U.S. Appl. No. 15/131,244, filed Apr. 18, 2016, 12 pages. |
| Non-Final Office Action dated Feb. 8, 2017, issued in connection with U.S. Appl. No. 15/098,892, filed Apr. 14, 2016, 17 pages. |
| Non-Final Office Action dated Mar. 9, 2017, issued in connection with U.S. Appl. No. 15/098,760, filed Apr. 14, 2016, 13 pages. |
| Non-Final Office Action dated Jan. 10, 2018, issued in connection with U.S. Appl. No. 15/098,718, filed Apr. 14, 2016, 15 pages. |
| Non-Final Office Action dated Jan. 10, 2018, issued in connection with U.S. Appl. No. 15/229,868, filed Aug. 5, 2016, 13 pages. |
| Non-Final Office Action dated Jan. 10, 2018, issued in connection with U.S. Appl. No. 15/438,725, filed Feb. 21, 2017, 15 pages. |
| Non-Final Office Action dated Sep. 10, 2018, issued in connection with U.S. Appl. No. 15/670,361, filed Aug. 7, 2017, 17 pages. |
| Non-Final Office Action dated Dec. 12, 2016, issued in connection with U.S. Appl. No. 15/098,718, filed Apr. 14, 2016, 11 pages. |
| Non-Final Office Action dated Feb. 12, 2019, issued in connection with U.S. Appl. No. 15/670,361, filed Aug. 7, 2017, 13 pages. |
| Non-Final Office Action dated Jan. 13, 2017, issued in connection with U.S. Appl. No. 15/098,805, filed Apr. 14, 2016, 11 pages. |
| Non-Final Office Action dated Nov. 13, 2018, issued in connection with U.S. Appl. No. 15/717,621, filed Sep. 27, 2017, 23 pages. |
| Non-Final Office Action dated Nov. 13, 2018, issued in connection with U.S. Appl. No. 16/160,107, filed Oct. 15, 2018, 8 pages. |
| Non-Final Office Action dated Sep. 14, 2017, issued in connection with U.S. Appl. No. 15/178,180, filed Jun. 9, 2016, 16 pages. |
| Non-Final Office Action dated Sep. 14, 2018, issued in connection with U.S. Appl. No. 15/959,907, filed Apr. 23, 2018, 15 pages. |
| Non-Final Office Action dated Jan. 15, 2019, issued in connection with U.S. Appl. No. 16/173,797, filed Oct. 29, 2018, 6 pages. |
| Non-Final Office Action dated Mar. 16, 2018, issued in connection with U.S. Appl. No. 15/681,937, filed Aug. 21, 2017, 5 pages. |
| Non-Final Office Action dated Oct. 16, 2018, issued in connection with U.S. Appl. No. 15/131,254, filed Apr. 18, 2016, 16 pages. |
| Non-Final Office Action dated Apr. 18, 2018, issued in connection with U.S. Appl. No. 15/811,468, filed Nov. 13, 2017, 14 pages. |
| Non-Final Office Action dated Jan. 18, 2019, issued in connection with U.S. Appl. No. 15/721,141, filed Sep. 29, 2017, 18 pages. |
| Non-Final Office Action dated Apr. 19, 2017, issued in connection with U.S. Appl. No. 15/131,776, filed Apr. 18, 2016, 12 pages. |
| Non-Final Office Action dated Feb. 20, 2018, issued in connection with U.S. Appl. No. 15/211,748, filed Jul. 15, 2016, 31 pages. |
| Non-Final Office Action dated Aug. 21, 2019, issued in connection with U.S. Appl. No. 16/192,126, filed Nov. 15, 2018, 8 pages. |
| Non-Final Office Action dated Feb. 21, 2019, issued in connection with U.S. Appl. No. 16/214,666, filed Dec. 10, 2018, 12 pages. |
| Non-Final Office Action dated May 22, 2018, issued in connection with U.S. Appl. No. 15/946,599, filed Apr. 5, 2018, 19 pages. |
| Non-Final Office Action dated May 23, 2019, issued in connection with U.S. Appl. No. 16/154,071, filed Oct. 8, 2018, 36 pages. |
| Non-Final Office Action dated Aug. 24, 2017, issued in connection with U.S. Appl. No. 15/297,627, filed Oct. 19, 2016, 13 pages. |
| Non-Final Office Action dated Jul. 24, 2019, issued in connection with U.S. Appl. No. 16/439,009, filed Jun. 12, 2019, 26 pages. |
| Non-Final Office Action dated Jul. 25, 2017, issued in connection with U.S. Appl. No. 15/273,679, filed Jul. 22, 2016, 11 pages. |
| Non-Final Office Action dated Dec. 26, 2018, issued in connection with U.S. Appl. No. 16/154,469, filed Oct. 8, 2018, 7 pages. |
| Non-Final Office Action dated Jan. 26, 2017, issued in connection with U.S. Appl. No. 15/098,867, filed Apr. 14, 2016, 16 pages. |
| Non-Final Office Action dated Oct. 26, 2017, issued in connection with U.S. Appl. No. 15/438,744, filed Feb. 21, 2017, 12 pages. |
| International Bureau, International Preliminary Report on Patentability, dated Jun. 17, 2021, issued in connection with International Application No. PCT/US2019/064907, filed on Dec. 6, 2019, 8 pages. |
| International Bureau, International Preliminary Report on Patentability, dated Mar. 2, 2021, issued in connection with International Application No. PCT/US2019/048558, filed on Aug. 28, 2019, 8 pages. |
| International Bureau, International Preliminary Report on Patentability, dated Feb. 20, 2020, issued in connection with International Application No. PCT/US2018/045397, filed on Aug. 6, 2018, 8 pages. |
| International Bureau, International Preliminary Report on Patentability, dated Mar. 31, 2020, issued in connection with International Application No. PCT/US2018053123, filed on Sep. 27, 2018, 12 pages. |
| International Bureau, International Preliminary Report on Patentability, dated Mar. 31, 2020, issued in connection with International Application No. PCT/US2018053472, filed on Sep. 28, 2018, 8 pages. |
| International Bureau, International Preliminary Report on Patentability, dated Mar. 31, 2020, issued in connection with International Application No. PCT/US2018053517, filed on Sep. 28, 2018, 10 pages. |
| International Bureau, International Search Report and Written Opinion dated Nov. 10, 2020, issued in connection with International Application No. PCT/US2020/044250, filed on Jul. 30, 2020, 15 pages. |
| International Bureau, International Search Report and Written Opinion dated Dec. 11, 2019, issued in connection with International Application No. PCT/US2019/052129, filed on Sep. 20, 2019, 18 pages. |
| International Bureau, International Search Report and Written Opinion dated Nov. 13, 2018, issued in connection with International Application No. PCT/US2018/045397, filed on Aug. 6, 2018, 11 pages. |
| International Bureau, International Search Report and Written Opinion dated Jan. 14, 2019, issued in connection with International Application No. PCT/US2018053472, filed on Sep. 28, 2018, 10 pages. |
| International Bureau, International Search Report and Written Opinion dated Jul. 14, 2020, issued in connection with International Application No. PCT/US2020/017150, filed on Feb. 7, 2020, 27 pages. |
| International Bureau, International Search Report and Written Opinion dated Nov. 14, 2017, issued in connection with International Application No. PCT/US2017/045521, filed on Aug. 4, 2017, 10 pages. |
| International Bureau, International Search Report and Written Opinion dated Jul. 17, 2019, issued in connection with International Application No. PCT/US2019/032934, filed on May 17, 2019, 17 pages. |
| International Bureau, International Search Report and Written Opinion dated Nov. 18, 2019, issued in connection with International Application No. PCT/US2019/048558, filed on Aug. 28, 2019, 11 pages. |
| International Bureau, International Search Report and Written Opinion dated Nov. 18, 2019, issued in connection with International Application No. PCT/US2019052841, filed on Sep. 25, 2019, 12 pages. |
| International Bureau, International Search Report and Written Opinion dated Mar. 2, 2020, issued in connection with International Application No. PCT/US2019064907, filed on Dec. 6, 2019, 11 pages. |
| International Bureau, International Search Report and Written Opinion dated Mar. 2, 2020, issued in connection with International Application No. PCT/US2019/064907, filed on Dec. 6, 2019, 9 pages. |
| International Bureau, International Search Report and Written Opinion dated Dec. 20, 2019, issued in connection with International Application No. PCT/US2019052654, filed on Sep. 24, 2019, 11 pages. |
| International Bureau, International Search Report and Written Opinion dated Sep. 21, 2020, issued in connection with International Application No. PCT/US2020/037229, filed on Jun. 11, 2020, 17 pages. |
| International Bureau, International Search Report and Written Opinion dated Oct. 22, 2020, issued in connection with International Application No. PCT/US2020/044282, filed on Jul. 30, 2020, 15 pages. |
| International Bureau, International Search Report and Written Opinion dated Apr. 23, 2021, issued in connection with International Application No. PCT/US2021/070007, filed on Jan. 6, 2021, 11 pages. |
| International Bureau, International Search Report and Written Opinion dated Jul. 24, 2018, issued in connection with International Application No. PCT/US2018/019010, filed on Feb. 21, 2018, 12 pages. |
| International Bureau, International Search Report and Written Opinion, dated Feb. 27, 2019, issued in connection with International Application No. PCT/US2018/053123, filed on Sep. 27, 2018, 16 pages. |
| International Bureau, International Search Report and Written Opinion dated Sep. 27, 2019, issued in connection with International Application No. PCT/US2019/039828, filed on Jun. 28, 2019, 13 pages. |
| International Bureau, International Search Report and Written Opinion dated Nov. 29, 2019, issued in connection with International Application No. PCT/US2019/053253, filed on Sep. 29, 2019, 14 pages. |
| International Bureau, International Search Report and Written Opinion dated Sep. 4, 2019, issued in connection with International Application No. PCT/US2019/033945, filed on May 24, 2019, 8 pages. |
| International Bureau, International Search Report and Written Opinion dated Aug. 6, 2020, issued in connection with International Application No. PCT/FR2019/000081, filed on May 24, 2019, 12 pages. |
| International Bureau, International Search Report and Written Opinion dated Dec. 6, 2018, issued in connection with International Application No. PCT/US2018/050050, filed on Sep. 7, 2018, 9 pages. |
| International Bureau, International Search Report and Written Opinion dated Dec. 6, 2019, issued in connection with International Application No. PCT/US2019050852, filed on Sep. 12, 2019, 10 pages. |
| International Bureau, International Search Report and Written Opinion dated Oct. 6, 2017, issued in connection with International Application No. PCT/US2017/045551, filed on Aug. 4, 2017, 12 pages. |
| International Bureau, International Search Report and Written Opinion dated Apr. 8, 2020, issued in connection with International Application No. PCT/US2019/067576, filed on Dec. 19, 2019, 12 pages. |
| International Searching Authority, International Search Report and Written Opinion dated Feb. 8, 2021, issued in connection with International Application No. PCT/EP2020/082243, filed on Nov. 16, 2020, 10 pages. |
| International Searching Authority, International Search Report and Written Opinion dated Feb. 12, 2021, issued in connection with International Application No. PCT/US2020/056632, filed on Oct. 21, 2020, 10 pages. |
| International Searching Authority, International Search Report and Written Opinion dated Apr. 23, 2021, issued in connection with International Application No. PCT/US2020/066231, filed on Dec. 18, 2020, 9 pages. |
| Japanese Patent Office, Decision of Refusal and Translation dated Jun. 8, 2021, issued in connection with Japanese Patent Application No. 2019-073348, 5 pages. |
| Japanese Patent Office, English Translation of Office Action dated Nov. 17, 2020, issued in connection with Japanese Application No. 2019-145039, 5 pages. |
| Japanese Patent Office, English Translation of Office Action dated Aug. 27, 2020, issued in connection with Japanese Application No. 2019-073349, 6 pages. |
| Japanese Patent Office, English Translation of Office Action dated Jul. 30, 2020, issued in connection with Japanese Application No. 2019-517281, 26 pages. |
| Japanese Patent Office, Non-Final Office Action and Translation dated Nov. 5, 2019, issued in connection with Japanese Patent Application No. 2019-517281, 6 pages. |
| Japanese Patent Office, Notice of Reasons for Refusal and Translation dated Jun. 22, 2021, issued in connection with Japanese Patent Application No. 2020-517935, 4 pages. |
| Japanese Patent Office, Notice of Reasons for Refusal and Translation dated Nov. 28, 2021, issued in connection with Japanese Patent Application No. 2020-550102, 9 pages. |
| Japanese Patent Office, Office Action and Translation dated Mar. 16, 2021, issued in connection with Japanese Patent Application No. 2020-506725, 7 pages. |
| Japanese Patent Office, Office Action and Translation dated Nov. 17, 2020, issued in connection with Japanese Patent Application No. 2019-145039, 7 pages. |
| Japanese Patent Office, Office Action and Translation dated Apr. 20, 2021, issued in connection with Japanese Patent Application No. 2020-513852, 9 pages. |
| Japanese Patent Office, Office Action and Translation dated Feb. 24, 2021, issued in connection with Japanese Patent Application No. 2019-517281, 4 pages. |
| Japanese Patent Office, Office Action and Translation dated Apr. 27, 2021, issued in connection with Japanese Patent Application No. 2020-518400, 10 pages. |
| Japanese Patent Office, Office Action and Translation dated Aug. 27, 2020, issued in connection with Japanese Patent Application No. 2019-073349, 6 pages. |
| Japanese Patent Office, Office Action and Translation dated Jul. 30, 2020, issued in connection with Japanese Patent Application No. 2019-517281, 6 pages. |
| Japanese Patent Office, Office Action and Translation dated Jul. 6, 2020, issued in connection with Japanese Patent Application No. 2019-073348, 10 pages. |
| Japanese Patent Office, Office Action and Translation dated Jul. 6, 2021, issued in connection with Japanese Patent Application No. 2019-073349, 6 pages. |
| Japanese Patent Office, Office Action and Translation dated Oct. 8, 2019, issued in connection with Japanese Patent Application No. 2019-521032, 5 pages. |
| Japanese Patent Office, Office Action Translation dated Nov. 5, 2019, issued in connection with Japanese Patent Application No. 2019-517281, 2 pages. |
| Japanese Patent Office, Office Action Translation dated Oct. 8, 2019, issued in connection with Japanese Patent Application No. 2019-521032, 8 pages. |
| Johnson, “Implementing Neural Networks into Modern Technology,” IJCNN'99. International Joint Conference on Neural Networks . Proceedings [Cat. No. 99CH36339], Washington, DC, USA, 1999, pp. 1028-1032, vol. 2, doi: 10.1109/IJCNN.1999.831096. [retrieved on Jun. 22, 2020]. |
| Joseph Szurley et al, “Efficient computation of microphone utility in a wireless acoustic sensor network with multi-channel Wiener filter based noise reduction”, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto, Japan, Mar. 25-30, 2012, pp. 2657-2660, XP032227701, DOI: 10.1109/ICASSP .2012.6288463 ISBN: 978-1-4673-0045-2. |
| Ketabdar et al. Detection of Out-of-Vocabulary Words in Posterior Based ASR. Proceedings of Interspeech 2007, Aug. 27, 2007, 4 pages. |
| Kim et al. Character-Aware Neural Language Models. Retrieved from the Internet: URL: https://arxiv.org/pdf/1508.06615v3.pdf, Oct. 16, 2015, 9 pages. |
| Korean Patent Office, Korean Examination Report and Translation dated Nov. 25, 2021, issued in connection with Korean Application No. 10-2021-7008937, 14 pages. |
| Korean Patent Office, Korean Examination Report and Translation dated Apr. 26, 2021, issued in connection with Korean Application No. 10-2021-7008937, 15 pages. |
| Korean Patent Office, Korean Office Action and Translation dated Oct. 14, 2021, issued in connection with Korean Application No. 10-2020-7011843, 29 pages. |
| Korean Patent Office, Korean Office Action and Translation dated Apr. 2, 2020, issued in connection with Korean Application No. 10-2020-7008486, 12 pages. |
| Korean Patent Office, Korean Office Action and Translation dated Mar. 25, 2020, issued in connection with Korean Application No. 10-2019-7012192, 14 pages. |
| Korean Patent Office, Korean Office Action and Translation dated Aug. 26, 2020, issued in connection with Korean Application No. 10-2019-7027640, 16 pages. |
| Korean Patent Office, Korean Office Action and Translation dated Mar. 30, 2020, issued in connection with Korean Application No. 10-2020-7004425, 5 pages. |
| Korean Patent Office, Korean Office Action and Translation dated Jan. 4, 2021, issued in connection with Korean Application No. 10-2020-7034425, 14 pages. |
| Korean Patent Office, Korean Office Action and Translation dated Sep. 9, 2019, issued in connection with Korean Application No. 10-2018-7027451, 21 pages. |
| Lei et al. Accurate and Compact Large Vocabulary Speech Recognition on Mobile Devices. Interspeech 2013, Aug. 25, 2013, 4 pages. |
| Lengerich et al. An End-to-End Architecture for Keyword Spotting and Voice Activity Detection, arXiv:1611.09405v1, Nov. 28, 2016, 5 pages. |
| Matrix—The Ultimate Development Board Sep. 14, 2019 Matrix—The Ultimate Development Board Sep. 14, 2019 https-//web.archive.org/web/20190914035838/https-//www.matrix.one/, 1 page. |
| Mesaros et al. Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge. IEEE/ACM Transactions on Audio, Speech, and Language Processing. Feb. 2018, 16 pages. |
| Non-Final Office Action dated Jul. 12, 2021, issued in connection with U.S. Appl. No. 17/008,104, filed Aug. 31, 2020, 6 pages. |
| Non-Final Office Action dated Jun. 18, 2021, issued in connection with U.S. Appl. No. 17/236,559, filed Apr. 21, 2021, 9 pages. |
| Non-Final Office Action dated Apr. 21, 2021, issued in connection with U.S. Appl. No. 16/109,375, filed Aug. 22, 2018, 9 pages. |
| Non-Final Office Action dated Dec. 21, 2020, issued in connection with U.S. Appl. No. 16/153,530, filed Oct. 5, 2018, 22 pages. |
| Non-Final Office Action dated Jul. 22, 2021, issued in connection with U.S. Appl. No. 16/179,779, filed Nov. 2, 2018, 19 pages. |
| Non-Final Office Action dated Apr. 23, 2021, issued in connection with U.S. Appl. No. 16/660,197, filed Oct. 22, 2019, 9 pages. |
| Non-Final Office Action dated Jun. 25, 2021, issued in connection with U.S. Appl. No. 16/213,570, filed Dec. 7, 2018, 11 pages. |
| Non-Final Office Action dated Jul. 8, 2021, issued in connection with U.S. Appl. No. 16/813,643, filed Mar. 9, 2020, 12 pages. |
| Non-Final Office Action dated Dec. 9, 2020, issued in connection with U.S. Appl. No. 16/271,550, filed Feb. 8, 2019, 35 pages. |
| Non-Final Office Action dated Jul. 9, 2021, issued in connection with U.S. Appl. No. 16/806,747, filed Mar. 2, 2020, 18 pages. |
| Non-Final Office Action dated Nov. 4, 2019, issued in connection with U.S. Appl. No. 16/022,662, filed Jun. 28, 2018, 16 pages. |
| Non-Final Office Action dated Sep. 5, 2019, issued in connection with U.S. Appl. No. 16/416,752, filed May 20, 2019, 14 pages. |
| Non-Final Office Action dated Oct. 9, 2019, issued in connection with U.S. Appl. No. 15/936,177, filed Mar. 26, 2018, 16 pages. |
| Non-Final Office Action dated Jul. 1, 2020, issued in connection with U.S. Appl. No. 16/138,111, filed Sep. 21, 2018, 14 pages. |
| Non-Final Office Action dated Aug. 11, 2021, issued in connection with U.S. Appl. No. 16/841,116, filed Apr. 6, 2020, 9 pages. |
| Non-Final Office Action dated Feb. 11, 2021, issued in connection with U.S. Appl. No. 16/876,493, filed May 18, 2020, 16 pages. |
| Non-Final Office Action dated Mar. 11, 2021, issued in connection with U.S. Appl. No. 16/834,483, filed Mar. 30, 2020, 11 pages. |
| Non-Final Office Action dated Oct. 11, 2019, issued in connection with U.S. Appl. No. 16/177,185, filed Oct. 31, 2018, 14 pages. |
| Non-Final Office Action dated Sep. 11, 2020, issued in connection with U.S. Appl. No. 15/989,715, filed May 25, 2018, 8 pages. |
| Non-Final Office Action dated Sep. 11, 2020, issued in connection with U.S. Appl. No. 16/219,702, filed Dec. 13, 2018, 9 pages. |
| Non-Final Office Action dated Apr. 12, 2021, issued in connection with U.S. Appl. No. 16/528,224, filed Jul. 31, 2019, 9 pages. |
| Non-Final Office Action dated Nov. 13, 2019, issued in connection with U.S. Appl. No. 15/984,073, filed May 18, 2018, 18 pages. |
| Non-Final Office Action dated Oct. 13, 2021, issued in connection with U.S. Appl. No. 16/679,538, filed Nov. 11, 2019, 8 pages. |
| Non-Final Office Action dated May 14, 2020, issued in connection with U.S. Appl. No. 15/948,541, filed Apr. 9, 2018, 8 pages. |
| Non-Final Office Action dated Apr. 15, 2020, issued in connection with U.S. Appl. No. 16/138,111, filed Sep. 21, 2018, 15 pages. |
| Non-Final Office Action dated Dec. 15, 2020, issued in connection with U.S. Appl. No. 17/087,423, filed Nov. 2, 2020, 7 pages. |
| Non-Final Office Action dated Nov. 15, 2019, issued in connection with U.S. Appl. No. 16/153,530, filed Oct. 5, 2018, 17 pages. |
| Non-Final Office Action dated Sep. 16, 2021, issued in connection with U.S. Appl. No. 16/879,553, filed May 20, 2020, 24 pages. |
| Non-Final Office Action dated Aug. 17, 2021, issued in connection with U.S. Appl. No. 17/236,559, filed Apr. 21, 2021, 10 pages. |
| Advisory Action dated Jun. 10, 2020, issued in connection with U.S. Appl. No. 15/936,177, filed Mar. 26, 2018, 4 pages. |
| Advisory Action dated Aug. 13, 2021, issued in connection with U.S. Appl. No. 16/271,550, filed Feb. 8, 2019, 4 pages. |
| Advisory Action dated Apr. 23, 2021, issued in connection with U.S. Appl. No. 16/219,702, filed Dec. 13, 2018, 3 pages. |
| Advisory Action dated Apr. 24, 2020, issued in connection with U.S. Appl. No. 15/948,541, filed Apr. 9, 2018, 4 pages. |
| Advisory Action dated Sep. 8, 2021, issued in connection with U.S. Appl. No. 16/168,389, filed Oct. 23, 2018, 4 pages. |
| Advisory Action dated Jun. 9, 2020, issued in connection with U.S. Appl. No. 16/145,275, filed Sep. 28, 2018, 3 pages. |
| Andra et al. Contextual Keyword Spotting in Lecture Video With Deep Convolutional Neural Network. 2017 International Conference on Advanced Computer Science and Information Systems, IEEE, Oct. 28, 2017, 6 pages. |
| Anonymous,. S Voice or Google Now—The Lowdown. Apr. 28, 2015, 9 pages, [online], [retrieved on Nov. 29, 2017], Retrieved from the Internet (URL:http://web.archive.org/web/20160807040123/http://lowdown.carphonewarehouse.com/news/s-voice-or-google-now/29958/). |
| Anonymous: “What are the function of 4 Microphones on iPhone 6S/6S+?”, ETrade Supply, Dec. 24, 2015, XP055646381, Retrieved from the Internet: URL:https://www.etradesupply.com/blog/4-microphones-iphone-6s6s-for/ [retrieved on Nov. 26, 2019]. |
| Audhkhasi Kartik et al. End-to-end ASR-free keyword search from speech. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, Mar. 5, 2017, 7 pages. |
| Australian Patent Office, Australian Examination Report Action dated Apr. 14, 2020, issued in connection with Australian Application No. 2019202257, 3 pages. |
| Australian Patent Office, Australian Examination Report Action dated Oct. 3, 2019, issued in connection with Australian Application No. 2018230932, 3 pages. |
| Australian Patent Office, Australian Examination Report Action dated Apr. 7, 2021, issued in connection with Australian Application No. 2019333058, 2 pages. |
| Australian Patent Office, Australian Examination Report Action dated Aug. 7, 2020, issued in connection with Australian Application No. 2019236722, 4 pages. |
| Australian Patent Office, Examination Report dated Jun. 28, 2021, issued in connection with Australian Patent Application No. 2019395022, 2 pages. |
| Bertrand et al. “Adaptive Distributed Noise Reduction for Speech Enhancement in Wireless Acoustic Sensor Networks” Jan. 2010, 4 pages. |
| Canadian Patent Office, Canadian Examination Report dated Nov. 2, 2021, issued in connection with Canadian Application No. 3067776, 4 pages. |
| Canadian Patent Office, Canadian Examination Report dated Oct. 26, 2021, issued in connection with Canadian Application No. 3072492, 3 pages. |
| Canadian Patent Office, Canadian Examination Report dated Mar. 9, 2021, issued in connection with Canadian Application No. 3067776, 5 pages. |
| Chinese Patent Office, Chinese Office Action and Translation dated Jul. 2, 2021, issued in connection with Chinese Application No. 201880077216.4, 22 pages. |
| Chinese Patent Office, Chinese Office Action and Translation dated Mar. 30, 2021, issued in connection with Chinese Application No. 202010302650.7, 15 pages. |
| Chinese Patent Office, First Office Action and Translation dated May 27, 2021, issued in connection with Chinese Application No. 201880026360.5, 15 pages. |
| Chinese Patent Office, First Office Action and Translation dated Dec. 28, 2020, issued in connection with Chinese Application No. 201880072203.8, 11 pages. |
| Chinese Patent Office, First Office Action and Translation dated Nov. 5, 2019, issued in connection with Chinese Application No. 201780072651.3, 19 pages. |
| Chinese Patent Office, First Office Action dated Feb. 28, 2020, issued in connection with Chinese Application No. 201780061543.6, 29 pages. |
| Chinese Patent Office, Second Office Action and Translation dated May 11, 2020, issued in connection with Chinese Application No. 201780061543.6, 17 pages. |
| Chinese Patent Office, Second Office Action and Translation dated Sep. 23, 2019, issued in connection with Chinese Application No. 201780025028.2, 15 pages. |
| Chinese Patent Office, Second Office Action and Translation dated Mar. 31, 2020, issued in connection with Chinese Application No. 201780072651.3, 17 pages. |
| Chinese Patent Office, Third Office Action and Translation dated Sep. 16, 2019, issued in connection with Chinese Application No. 201780025029.7, 14 pages. |
| Chinese Patent Office, Third Office Action and Translation dated Aug. 5, 2020, issued in connection with Chinese Application No. 201780072651.3, 10 pages. |
| Chung et al. Empirical Evaluation of Gated Recurrent Neural Network on Sequence Modeling. Dec. 11, 2014, 9 pages. |
| Cipriani,. The complete list of OK, Google commands—CNET. Jul. 1, 2016, 5 pages, [online], [retrieved on Jan. 15, 2020]. Retrieved from the Internet: (URL:https://web.archive.org/web/20160803230926/https://www.cnet.com/how-to/complete-list-of-ok-google-commands/). |
| Couke et al. Efficient Keyword Spotting using Dilated Convolutions and Gating, arXiv:1811.07684v2, Feb. 18, 2019, 5 pages. |
| European Patent Office, European EPC Article 94.3 dated Nov. 11, 2021, issued in connection with European Application No. 19784172.9, 5 pages. |
| European Patent Office, European EPC Article 94.3 dated Feb. 23, 2021, issued in connection with European Application No. 17200837.7, 8 pages. |
| European Patent Office, European EPC Article 94.3 dated Feb. 26, 2021, issued in connection with European Application No. 18789515.6, 8 pages. |
| European Patent Office, European Extended Search Report dated Oct. 7, 2021, issued in connection with European Application No. 21193616.6, 9 pages. |
| European Patent Office, European Extended Search Report dated Nov. 25, 2020, issued in connection with European Application No. 20185599.6, 9 pages. |
| European Patent Office, European Extended Search Report dated Feb. 3, 2020, issued in connection with European Application No. 19197116.7, 9 pages. |
| European Patent Office, European Extended Search Report dated Aug. 6, 2020, issued in connection with European Application No. 20166332.5, 10 pages. |
| European Patent Office, European Office Action dated Jul. 1, 2020, issued in connection with European Application No. 17757075.1, 7 pages. |
| European Patent Office, European Office Action dated Jan. 14, 2020, issued in connection with European Application No. 17757070.2, 7 pages. |
| European Patent Office, European Office Action dated Jan. 21, 2021, issued in connection with European Application No. 17792272.1, 7 pages. |
| European Patent Office, European Office Action dated Sep. 23, 2020, issued in connection with European Application No. 18788976.1, 7 pages. |
| European Patent Office, European Office Action dated Oct. 26, 2020, issued in connection with European Application No. 18760101.8, 4 pages. |
| European Patent Office, European Office Action dated Aug. 30, 2019, issued in connection with European Application No. 17781608.9, 6 pages. |
| European Patent Office, European Office Action dated Sep. 9, 2020, issued in connection with European Application No. 18792656.3, 10 pages. |
| European Patent Office, Examination Report dated Jul. 15, 2021, issued in connection with European Patent Application No. 19729968.8, 7 pages. |
| European Patent Office, Extended Search Report dated Aug. 13, 2021, issued in connection with European Patent Application No. 21164130.3, 11 pages. |
| European Patent Office, Extended Search Report dated May 16, 2018, issued in connection with European Patent Application No. 17200837.7, 11 pages. |
| European Patent Office, Extended Search Report dated Jul. 25, 2019, issued in connection with European Patent Application No. 18306501.0, 14 pages. |
| European Patent Office, Extended Search Report dated May 29, 2020, issued in connection with European Patent Application No. 19209389.6, 8 pages. |
| European Patent Office, Summons to Attend Oral Proceedings mailed on Dec. 20, 2019, issued in connection with European Application No. 17174435.2, 13 pages. |
| Final Office Action dated Jul. 23, 2021, issued in connection with U.S. Appl. No. 16/439,046, filed Jun. 12, 2019, 12 pages. |
| Final Office Action dated Feb. 10, 2021, issued in connection with U.S. Appl. No. 16/219,702, filed Dec. 13, 2018, 9 pages. |
| Final Office Action dated Feb. 10, 2021, issued in connection with U.S. Appl. No. 16/402,617, filed May 3, 2019, 13 pages. |
| Final Office Action dated Nov. 10, 2020, issued in connection with U.S. Appl. No. 16/600,644, filed Oct. 14, 2019, 19 pages. |
| Final Office Action dated Dec. 11, 2019, issued in connection with U.S. Appl. No. 16/227,308, filed Dec. 20, 2018, 10 pages. |
| Final Office Action dated Sep. 11, 2019, issued in connection with U.S. Appl. No. 16/178,122, filed Nov. 1, 2018, 13 pages. |
| Final Office Action dated May 13, 2020, issued in connection with U.S. Appl. No. 16/153,530, filed Oct. 5, 2018, 20 pages. |
| Final Office Action dated Jul. 15, 2021, issued in connection with U.S. Appl. No. 16/153,530, filed Oct. 5, 2018, 22 pages. |
| Final Office Action dated Jun. 15, 2021, issued in connection with U.S. Appl. No. 16/819,755, filed Mar. 16, 2020, 12 pages. |
| Final Office Action dated Oct. 15, 2020, issued in connection with U.S. Appl. No. 16/109,375, filed Aug. 22, 2018, 9 pages. |
| Final Office Action dated May 18, 2020, issued in connection with U.S. Appl. No. 16/177,185, filed Oct. 31, 2018, 16 pages. |
| Final Office Action dated May 21, 2020, issued in connection with U.S. Appl. No. 15/989,715, filed May 25, 2018, 21 pages. |
| Final Office Action dated Feb. 22, 2021, issued in connection with U.S. Appl. No. 15/936,177, filed Mar. 26, 2018, 20 pages. |
| Final Office Action dated Feb. 22, 2021, issued in connection with U.S. Appl. No. 16/213,570, filed Dec. 7, 2018, 12 pages. |
| Final Office Action dated Jun. 22, 2020, issued in connection with U.S. Appl. No. 16/179,779, filed Nov. 2, 2018, 16 pages. |
| Final Office Action dated Mar. 23, 2020, issued in connection with U.S. Appl. No. 16/145,275, filed Sep. 28, 2018, 11 pages. |
| Final Office Action dated Feb. 24, 2020, issued in connection with U.S. Appl. No. 15/936,177, filed Mar. 26, 2018, 20 pages. |
| Final Office Action dated Nov. 29, 2021, issued in connection with U.S. Appl. No. 17/236,559, filed Apr. 21, 2021, 11 pages. |
| Final Office Action dated Jun. 4, 2021, issued in connection with U.S. Appl. No. 16/168,389, filed Oct. 23, 2018, 38 pages. |
| Final Office Action dated Oct. 4, 2021, issued in connection with U.S. Appl. No. 16/806,747, filed Mar. 2, 2020, 17 pages. |
| Final Office Action dated Feb. 7, 2020, issued in connection with U.S. Appl. No. 15/948,541, filed Apr. 9, 2018, 8 pages. |
| Final Office Action dated Jun. 8, 2021, issued in connection with U.S. Appl. No. 16/271,550, filed Feb. 8, 2019, 41 pages. |
| Final Office Action dated Sep. 8, 2020, issued in connection with U.S. Appl. No. 16/213,570, filed Dec. 7, 2018, 12 pages. |
| First Action Interview Office Action dated Mar. 8, 2021, issued in connection with U.S. Appl. No. 16/798,967, filed Feb. 24, 2020, 4 pages. |
| First Action Interview Office Action dated Jun. 15, 2020, issued in connection with U.S. Appl. No. 16/213,570, filed Dec. 7, 2018, 4 pages. |
| First Action Interview Office Action dated Jun. 2, 2020, issued in connection with U.S. Appl. No. 16/109,375, filed Aug. 22, 2018, 10 pages. |
| First Action Interview Office Action dated Jan. 22, 2020, issued in connection with U.S. Appl. No. 15/989,715, filed May 25, 2018, 3 pages. |
| Hans Speidel: “Chatbot Training: How to use training data to provide fully automated customer support”, Jun. 29, 2017, pp. 1-3, XP055473185, Retrieved from the Internet: URL:https://www.crowdguru.de/wp-content/uploads/Case-Study-Chatbot-training-How-to-use-training-data-to-provide-fully-automated-customer-support.pdf [retrieved on May 7, 2018]. |
| Indian Patent Office, Examination Report dated May 24, 2021, issued in connection with Indian Patent Application No. 201847035595, 6 pages. |
| Indian Patent Office, Examination Report dated Feb. 25, 2021, issued in connection with Indian Patent Application No. 201847035625, 6 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Apr. 1, 2021, issued in connection with International Application No. PCT/US2019/052129, filed on Sep. 20, 2019, 13 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Jul. 1, 2021, issued in connection with International Application No. PCT/US2019/067576, filed on Dec. 19, 2019, 8 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Aug. 10, 2021, issued in connection with International Application No. PCT/US2020/017150, filed on Feb. 7, 2020, 20 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Dec. 10, 2020, issued in connection with International Application No. PCT/US2019/033945, filed on May 25, 2018, 7 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Mar. 10, 2020, issued in connection with International Application No. PCT/US2018/050050, filed on Sep. 7, 2018, 7 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Apr. 15, 2021, issued in connection with International Application No. PCT/US2019/054332, filed on Oct. 2, 2019, 9 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Jan. 15, 2019, issued in connection with International Application No. PCT/US2017/042170, filed on Jul. 14, 2017, 7 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Jan. 15, 2019, issued in connection with International Application No. PCT/US2017/042227, filed on Jul. 14, 2017, 7 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Mar. 25, 2021, issued in connection with International Application No. PCT/US2019/050852, filed on Sep. 12, 2019, 8 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Aug. 27, 2019, issued in connection with International Application No. PCT/US2018/019010, filed on Feb. 21, 2018, 9 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Mar. 31, 2020, issued in connection with International Application No. PCT/US2018/053517, filed on Sep. 28, 2018, 10 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Feb. 5, 2019, issued in connection with International Application No. PCT/US2017/045521, filed on Aug. 4, 2017, 7 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Feb. 5, 2019, issued in connection with International Application No. PCT/US2017/045551, filed on Aug. 4, 2017, 9 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Jan. 7, 2021, issued in connection with International Application No. PCT/US2019/039828, filed on Jun. 28, 2019, 11 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Apr. 8, 2021, issued in connection with International Application No. PCT/US2019/052654, filed on Sep. 24, 2019, 7 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Apr. 8, 2021, issued in connection with International Application No. PCT/US2019/052841, filed on Sep. 25, 2019, 8 pages. |
| International Bureau, International Preliminary Report on Patentability and Written Opinion, dated Apr. 8, 2021, issued in connection with International Application No. PCT/US2019/053253, filed on Sep. 26, 2019, 10 pages. |
| Notice of Allowance dated Oct. 11, 2019, issued in connection with U.S. Appl. No. 16/437,476, filed Jun. 11, 2019, 9 pages. |
| Notice of Allowance dated Sep. 11, 2019, issued in connection with U.S. Appl. No. 16/154,071, filed Oct. 8, 2018, 5 pages. |
| Notice of Allowance dated Aug. 12, 2021, issued in connection with U.S. Appl. No. 16/819,755, filed Mar. 16, 2020, 6 pages. |
| Notice of Allowance dated May 12, 2021, issued in connection with U.S. Appl. No. 16/402,617, filed May 3, 2019, 8 pages. |
| Notice of Allowance dated Jan. 13, 2020, issued in connection with U.S. Appl. No. 16/192,126, filed Nov. 15, 2018, 6 pages. |
| Notice of Allowance dated Jan. 13, 2021, issued in connection with U.S. Appl. No. 16/539,843, filed Aug. 13, 2019, 5 pages. |
| Notice of Allowance dated Nov. 13, 2020, issued in connection with U.S. Appl. No. 16/131,409, filed Sep. 14, 2018, 11 pages. |
| Notice of Allowance dated Aug. 14, 2020, issued in connection with U.S. Appl. No. 16/598,125, filed Oct. 10, 2019, 5 pages. |
| Notice of Allowance dated Jan. 14, 2021, issued in connection with U.S. Appl. No. 17/087,423, filed Nov. 2, 2020, 8 pages. |
| Notice of Allowance dated Jan. 15, 2020, issued in connection with U.S. Appl. No. 16/439,009, filed Jun. 12, 2019, 9 pages. |
| Notice of Allowance dated Oct. 15, 2019, issued in connection with U.S. Appl. No. 16/437,437, filed Jun. 11, 2019, 9 pages. |
| Notice of Allowance dated Oct. 15, 2020, issued in connection with U.S. Appl. No. 16/715,713, filed Dec. 16, 2019, 9 pages. |
| Notice of Allowance dated Oct. 15, 2021, issued in connection with U.S. Appl. No. 16/213,570, filed Dec. 7, 2018, 8 pages. |
| Notice of Allowance dated Sep. 15, 2021, issued in connection with U.S. Appl. No. 16/685,135, filed Nov. 15, 2019, 10 pages. |
| Notice of Allowance dated Apr. 16, 2021, issued in connection with U.S. Appl. No. 16/798,967, filed Feb. 24, 2020, 16 pages. |
| Notice of Allowance dated Feb. 17, 2021, issued in connection with U.S. Appl. No. 16/715,984, filed Dec. 16, 2019, 8 pages. |
| Notice of Allowance dated Jun. 17, 2020, issued in connection with U.S. Appl. No. 16/141,875, filed Sep. 25, 2018, 6 pages. |
| Notice of Allowance dated Dec. 18, 2019, issued in connection with U.S. Appl. No. 16/434,426, filed Jun. 7, 2019, 13 pages. |
| Notice of Allowance dated Feb. 18, 2020, issued in connection with U.S. Appl. No. 16/022,662, filed Jun. 28, 2018, 8 pages. |
| Notice of Allowance dated Mar. 18, 2021, issued in connection with U.S. Appl. No. 16/177,185, filed Oct. 31, 2018, 8 pages. |
| Notice of Allowance dated Aug. 19, 2020, issued in connection with U.S. Appl. No. 16/271,560, filed Feb. 8, 2019, 9 pages. |
| Notice of Allowance dated Mar. 19, 2021, issued in connection with U.S. Appl. No. 17/157,686, filed Jan. 25, 2021, 11 pages. |
| Notice of Allowance dated Dec. 2, 2020, issued in connection with U.S. Appl. No. 15/989,715, filed May 25, 2018, 11 pages. |
| Notice of Allowance dated Dec. 2, 2021, issued in connection with U.S. Appl. No. 16/841,116, filed Apr. 6, 2020, 5 pages. |
| Notice of Allowance dated Sep. 2, 2020, issued in connection with U.S. Appl. No. 16/214,711, filed Dec. 10, 2018, 9 pages. |
| Notice of Allowance dated Jul. 20, 2020, issued in connection with U.S. Appl. No. 15/984,073, filed May 18, 2018, 12 pages. |
| Notice of Allowance dated Oct. 20, 2021, issued in connection with U.S. Appl. No. 16/439,032, filed Jun. 12, 2019, 8 pages. |
| Notice of Allowance dated Apr. 21, 2021, issued in connection with U.S. Appl. No. 16/145,275, filed Sep. 28, 2018, 8 pages. |
| Notice of Allowance dated Feb. 21, 2020, issued in connection with U.S. Appl. No. 16/416,752, filed May 20, 2019, 6 pages. |
| Notice of Allowance dated Jan. 21, 2020, issued in connection with U.S. Appl. No. 16/672,764, filed Nov. 4, 2019, 10 pages. |
| Notice of Allowance dated Jan. 21, 2021, issued in connection with U.S. Appl. No. 16/600,644, filed Oct. 14, 2019, 7 pages. |
| Notice of Allowance dated Oct. 21, 2019, issued in connection with U.S. Appl. No. 15/946,585, filed Apr. 5, 2018, 5 pages. |
| Notice of Allowance dated Jul. 22, 2020, issued in connection with U.S. Appl. No. 16/131,409, filed Sep. 14, 2018, 13 pages. |
| Notice of Allowance dated Jul. 22, 2020, issued in connection with U.S. Appl. No. 16/790,621, filed Feb. 13, 2020, 10 pages. |
| Notice of Allowance dated Nov. 22, 2021, issued in connection with U.S. Appl. No. 16/834,483, filed Mar. 30, 2020, 10 pages. |
| Notice of Allowance dated Aug. 23, 2021, issued in connection with U.S. Appl. No. 16/109,375, filed Aug. 22, 2018, 10 pages. |
| Notice of Allowance dated Jun. 23, 2021, issued in connection with U.S. Appl. No. 16/814,844, filed Mar. 10, 2020, 8 pages. |
| Notice of Allowance dated Oct. 25, 2021, issued in connection with U.S. Appl. No. 16/723,909, filed Dec. 20, 2019, 11 pages. |
| Notice of Allowance dated Aug. 26, 2020, issued in connection with U.S. Appl. No. 15/948,541, filed Apr. 9, 2018, 9 pages. |
| Notice of Allowance dated May 26, 2021, issued in connection with U.S. Appl. No. 16/927,670, filed Jul. 13, 2020, 10 pages. |
| Notice of Allowance dated Apr. 27, 2020, issued in connection with U.S. Appl. No. 16/700,607, filed Dec. 2, 2019, 10 pages. |
| Notice of Allowance dated May 28, 2021, issued in connection with U.S. Appl. No. 16/524,306, filed Jul. 29, 2019, 9 pages. |
| Notice of Allowance dated Jan. 29, 2021, issued in connection with U.S. Appl. No. 16/290,599, filed Mar. 1, 2019, 9 pages. |
| Notice of Allowance dated Jun. 29, 2020, issued in connection with U.S. Appl. No. 16/216,357, filed Dec. 11, 2018, 8 pages. |
| Notice of Allowance dated Mar. 29, 2021, issued in connection with U.S. Appl. No. 16/600,949, filed Oct. 14, 2019, 9 pages. |
| Notice of Allowance dated May 29, 2020, issued in connection with U.S. Appl. No. 16/148,879, filed Oct. 1, 2018, 6 pages. |
| Notice of Allowance dated Sep. 29, 2021, issued in connection with U.S. Appl. No. 16/876,493, filed May 18, 2020, 5 pages. |
| Notice of Allowance dated Jun. 3, 2021, issued in connection with U.S. Appl. No. 16/876,493, filed May 18, 2020, 7 pages. |
| Notice of Allowance dated Mar. 30, 2020, issued in connection with U.S. Appl. No. 15/973,413, filed May 7, 2018, 5 pages. |
| Notice of Allowance dated Oct. 30, 2019, issued in connection with U.S. Appl. No. 16/131,392, filed Sep. 14, 2018, 9 pages. |
| Notice of Allowance dated Oct. 30, 2020, issued in connection with U.S. Appl. No. 16/528,016, filed Jul. 31, 2019, 10 pages. |
| Notice of Allowance dated Jun. 4, 2021, issued in connection with U.S. Appl. No. 16/528,265, filed Jul. 31, 2019, 17 pages. |
| Notice of Allowance dated Mar. 4, 2020, issued in connection with U.S. Appl. No. 16/444,975, filed Jun. 18, 2019, 10 pages. |
| Notice of Allowance dated Feb. 5, 2020, issued in connection with U.S. Appl. No. 16/178,122, filed Nov. 1, 2018, 9 pages. |
| Notice of Allowance dated Feb. 6, 2020, issued in connection with U.S. Appl. No. 16/227,308, filed Dec. 20, 2018, 7 pages. |
| Notice of Allowance dated Apr. 7, 2020, issued in connection with U.S. Appl. No. 15/098,760, filed Apr. 14, 2016, 7 pages. |
| Notice of Allowance dated Apr. 7, 2020, issued in connection with U.S. Appl. No. 16/147,710, filed Sep. 29, 2018, 15 pages. |
| Notice of Allowance dated Jun. 7, 2021, issued in connection with U.S. Appl. No. 16/528,224, filed Jul. 31, 2019, 9 pages. |
| Notice of Allowance dated Nov. 8, 2021, issued in connection with U.S. Appl. No. 17/008,104, filed Aug. 31, 2020, 9 pages. |
| Oord et al. WaveNet: A Generative Model for Raw Audio. Arxiv.org, Cornell University Library, Sep. 12, 2016, 15 pages. |
| Optimizing Siri on HomePod in Far-Field Settings. Audio Software Engineering and Siri Speech Team, Machine Learning Journal vol. 1, Issue 12. https://machinelearning.apple.com/2018/12/03/optimizing-siri-on-homepod-in-far-field-settings.html. Dec. 2018, 18 pages. |
| Parada et al. Contextual Information Improves OOV Detection in Speech. Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Jun. 2, 2010, 9 pages. |
| Pre-Appeal Brief Decision dated Jun. 2, 2021, issued in connection with U.S. Appl. No. 16/213,570, filed Dec. 7, 2018, 2 pages. |
| Preinterview First Office Action dated Mar. 25, 2020, issued in connection with U.S. Appl. No. 16/109,375, filed Aug. 22, 2018, 6 pages. |
| Preinterview First Office Action dated Sep. 30, 2019, issued in connection with U.S. Appl. No. 15/989,715, filed May 25, 2018, 4 pages. |
| Preinterview First Office Action dated May 7, 2020, issued in connection with U.S. Appl. No. 16/213,570, filed Dec. 7, 2018, 5 pages. |
| Preinterview First Office Action dated Jan. 8, 2021, issued in connection with U.S. Appl. No. 16/798,967, filed Feb. 24, 2020, 4 pages. |
| Renato De Mori. Spoken Language Understanding: A Survey. Automatic Speech Recognition & Understanding, 2007. IEEE, Dec. 1, 2007, 56 pages. |
| Rottondi et al., “An Overview on Networked Music Performance Technologies,” IEEE Access, vol. 4, pp. 8823-8843, 2016, DOI: 10.1109/ACCESS.2016.2628440, 21 pages. |
| Rybakov et al. Streaming keyword spotting on mobile devices, arXiv:2005.06720v2, Jul. 29, 2020, 5 pages. |
| Shan et al. Attention-based End-to-End Models for Small-Footprint Keyword Spotting, arXiv:1803.10916v1, Mar. 29, 2018, 5 pages. |
| Snips: How to Snips—Assistant creation & Installation, Jun. 26, 2017, 6 pages. |
| Speidel, Hans. Chatbot Training: How to use training data to provide fully automated customer support. Retrieved from the Internet: URL: https://www.crowdguru.de/wp-content/uploads/Case-Study-Chatbox-training-How-to-use-training-data-to-provide-fully-automated-customer-support.pdf. Jun. 29, 2017, 4 pages. |
| Stemmer et al. Speech Recognition and Understanding on Hardware-Accelerated DSP. Proceedings of Interspeech 2017: Show & Tell Contribution, Aug. 20, 2017, 2 pages. |
| Tsung-Hsien Wen et al: “A Network-based End-to-End Trainable Task-oriented Dialogue System”, Corr Arxiv, vol. 1604.04562v1, Apr. 15, 2016, pp. 1-11, XP055396370, Stroudsburg, PA, USA. |
| Wen et al. A Network-based End-to-End Trainable Task-oriented Dialogue System, Corr (Arxiv), Apr. 15, 2016, 11 pages. |
| Wu et al. End-to-End Recurrent Entity Network for Entity-Value Independent Goal-Oriented Dialog Learning. DSTC6—Dialog System Technology Challenges, Dec. 10, 2017, 5 pages. |
| Xiaoguang et al. “Robust Small-Footprint Keyword Spotting Using Sequence-To-Sequence Model with Connectionist Temporal Classifier”, 2019 IEEE, Sep. 28, 2019, 5 pages. |
| Xu et al. An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking. ARXIV.org, Cornell University Library, May 3, 2018, 10 pages. |
| Yamaha DME Designer 3.0 Owner's Manual; Copyright 2008, 501 pages. |
| Zaykovskiy, Dmitry. Survey of the Speech Recognition Techniques for Mobile Devices. Proceedings of Specom 2006, Jun. 25, 2006, 6 pages. |
| Non-Final Office Action dated Sep. 17, 2020, issued in connection with U.S. Appl. No. 16/600,949, filed Oct. 14, 2019, 29 pages. |
| Non-Final Office Action dated Aug. 18, 2021, issued in connection with U.S. Appl. No. 16/845,946, filed Apr. 10, 2020, 14 pages. |
| Non-Final Office Action dated Oct. 18, 2019, issued in connection with U.S. Appl. No. 15/098,760, filed Apr. 14, 2016, 27 pages. |
| Non-Final Office Action dated Sep. 18, 2019, issued in connection with U.S. Appl. No. 16/179,779, filed Nov. 2, 2018, 14 pages. |
| Non-Final Office Action dated Dec. 19, 2019, issued in connection with U.S. Appl. No. 16/147,710, filed Sep. 29, 2018, 10 pages. |
| Non-Final Office Action dated Feb. 19, 2020, issued in connection with U.S. Appl. No. 16/148,879, filed Oct. 1, 2018, 15 pages. |
| Non-Final Office Action dated Sep. 2, 2020, issued in connection with U.S. Appl. No. 16/290,599, filed Mar. 1, 2019, 17 pages. |
| Non-Final Office Action dated Sep. 2, 2021, issued in connection with U.S. Appl. No. 16/947,895, filed Aug. 24, 2020, 16 pages. |
| Non-Final Office Action dated Jun. 20, 2019, issued in connection with U.S. Appl. No. 15/946,585, filed Apr. 5, 2018, 10 pages. |
| Non-Final Office Action dated Jan. 21, 2020, issued in connection with U.S. Appl. No. 16/214,711, filed Dec. 10, 2018, 9 pages. |
| Non-Final Office Action dated Jan. 21, 2020, issued in connection with U.S. Appl. No. 16/598,125, filed Oct. 10, 2019, 25 pages. |
| Non-Final Office Action dated Oct. 21, 2019, issued in connection with U.S. Appl. No. 15/973,413, filed May 7, 2018, 10 pages. |
| Non-Final Office Action dated Jul. 22, 2020, issued in connection with U.S. Appl. No. 16/145,275, filed Sep. 28, 2018, 11 pages. |
| Non-Final Office Action dated Sep. 22, 2020, issued in connection with U.S. Appl. No. 16/539,843, filed Aug. 13, 2019, 7 pages. |
| Non-Final Office Action dated Jun. 23, 2021, issued in connection with U.S. Appl. No. 16/439,032, filed Jun. 12, 2019, 13 pages. |
| Non-Final Office Action dated Nov. 23, 2020, issued in connection with U.S. Appl. No. 16/524,306, filed Jul. 29, 2019, 14 pages. |
| Non-Final Office Action dated Sep. 23, 2020, issued in connection with U.S. Appl. No. 16/177,185, filed Oct. 31, 2018, 17 pages. |
| Non-Final Office Action dated Oct. 26, 2021, issued in connection with U.S. Appl. No. 16/736,725, filed Jan. 7, 2020, 12 pages. |
| Non-Final Office Action dated Mar. 27, 2020, issued in connection with U.S. Appl. No. 16/790,621, filed Feb. 13, 2020, 8 pages. |
| Non-Final Office Action dated May 27, 2020, issued in connection with U.S. Appl. No. 16/715,713, filed Dec. 16, 2019, 14 pages. |
| Non-Final Office Action dated Oct. 27, 2020, issued in connection with U.S. Appl. No. 16/213,570, filed Dec. 7, 2018, 13 pages. |
| Non-Final Office Action dated Oct. 27, 2020, issued in connection with U.S. Appl. No. 16/715,984, filed Dec. 16, 2019, 14 pages. |
| Non-Final Office Action dated Oct. 27, 2020, issued in connection with U.S. Appl. No. 16/819,755, filed Mar. 16, 2020, 8 pages. |
| Non-Final Office Action dated Oct. 28, 2019, issued in connection with U.S. Appl. No. 16/145,275, filed Sep. 28, 2018, 11 pages. |
| Non-Final Office Action dated Oct. 28, 2021, issued in connection with U.S. Appl. No. 16/378,516, filed Apr. 8, 2019, 10 pages. |
| Non-Final Office Action dated Oct. 28, 2021, issued in connection with U.S. Appl. No. 17/247,736, filed Dec. 21, 2020, 12 pages. |
| Non-Final Office Action dated Mar. 29, 2021, issued in connection with U.S. Appl. No. 16/528,265, filed Jul. 31, 2019, 18 pages. |
| Non-Final Office Action dated Nov. 29, 2021, issued in connection with U.S. Appl. No. 16/989,350, filed Aug. 10, 2020, 15 pages. |
| Non-Final Office Action dated Sep. 29, 2020, issued in connection with U.S. Appl. No. 16/402,617, filed May 3, 2019, 12 pages. |
| Non-Final Office Action dated Dec. 3, 2020, issued in connection with U.S. Appl. No. 16/145,275, filed Sep. 28, 2018, 11 pages. |
| Non-Final Office Action dated Aug. 4, 2020, issued in connection with U.S. Appl. No. 16/600,644, filed Oct. 14, 2019, 30 pages. |
| Non-Final Office Action dated Nov. 5, 2021, issued in connection with U.S. Appl. No. 16/153,530, filed Oct. 5, 2018, 21 pages. |
| Non-Final Office Action dated Apr. 6, 2020, issued in connection with U.S. Appl. No. 16/424,825, filed May 29, 2019, 22 pages. |
| Non-Final Office Action dated Jan. 6, 2021, issued in connection with U.S. Appl. No. 16/439,046, filed Jun. 12, 2019, 13 pages. |
| Non-Final Office Action dated Mar. 6, 2020, issued in connection with U.S. Appl. No. 16/141,875, filed Sep. 25, 2018, 8 pages. |
| Non-Final Office Action dated Sep. 8, 2020, issued in connection with U.S. Appl. No. 15/936,177, filed Mar. 26, 2018, 19 pages. |
| Non-Final Office Action dated Apr. 9, 2021, issued in connection with U.S. Appl. No. 16/780,483, filed Feb. 3, 2020, 45 pages. |
| Non-Final Office Action dated Feb. 9, 2021, issued in connection with U.S. Appl. No. 16/806,747, filed Mar. 2, 2020, 16 pages. |
| Non-Final Office Action dated Sep. 9, 2020, issued in connection with U.S. Appl. No. 16/168,389, filed Oct. 23, 2018, 29 pages. |
| Notice of Allowance dated Aug. 10, 2021, issued in connection with U.S. Appl. No. 17/157,686, filed Jan. 25, 2021, 9 pages. |
| Notice of Allowance dated Aug. 2, 2021, issued in connection with U.S. Appl. No. 16/660,197, filed Oct. 22, 2019, 7 pages. |
| Notice of Allowance dated Mar. 31, 2021, issued in connection with U.S. Appl. No. 16/813,643, filed Mar. 9, 2020, 11 pages. |
| Notice of Allowance dated Aug. 4, 2021, issued in connection with U.S. Appl. No. 16/780,483, filed Feb. 3, 2020, 5 pages. |
| Notice of Allowance dated Dec. 2, 2019, issued in connection with U.S. Appl. No. 15/718,521, filed Sep. 28, 2017, 15 pages. |
| Notice of Allowance dated Jun. 1, 2021, issued in connection with U.S. Appl. No. 16/219,702, filed Dec. 13, 2018, 8 pages. |
| Notice of Allowance dated Jun. 1, 2021, issued in connection with U.S. Appl. No. 16/685,135, filed Nov. 15, 2019, 10 pages. |
| Notice of Allowance dated Sep. 1, 2021, issued in connection with U.S. Appl. No. 15/936,177, filed Mar. 26, 2018, 22 pages. |
| Notice of Allowance dated Aug. 10, 2020, issued in connection with U.S. Appl. No. 16/424,825, filed May 29, 2019, 9 pages. |
| Notice of Allowance dated Feb. 10, 2021, issued in connection with U.S. Appl. No. 16/138,111, filed Sep. 21, 2018, 8 pages. |
| Number | Date | Country | |
|---|---|---|---|
| 20200395006 A1 | Dec 2020 | US |
