Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 06, 2026 has been entered.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on January 27, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
4. The amendment filed on March 06, 2026 has been entered. Claims 1-8, 10-14, 16-21, and 24 have been amended. Claims 1-21 and 24 are pending.
The applicant argues that the prior art of record does not disclose “determining that the one of the plurality of different devices is compatible with outputting the identified media content”. The examiner agrees with this assertion.
Applicant’s arguments with respect to the 35 U.S.C. 103 rejections for claims 1-21 and 24 have been considered but are moot because the arguments are directed towards amended claim language, addressed on new grounds of rejection below.
Claim Rejections - 35 USC § 103
5. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
6. Claims 1-6, 9-17, 20-21, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Torok (U.S. Patent No. 9324322) in view of Lan (U.S. Patent No. 10685669) in view of Kraut (U.S. Publication No. 20100146445) in view of Newendorp (U.S. Publication No. 20170092270).
Regarding claim 1, Torok discloses a method comprising:
receiving, by a computing device, audio comprising a voice command and background noise ([Col 2, Rows 23-24] - The speech enabled device 100 may then monitor for a user command (such as a voice command) [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models);
However, Torok does not disclose determining, based on speech recognition, that a plurality of different devices are configured to respond differently to the voice command.
Lan does teach determining, based on speech recognition, that a plurality of different devices are configured to respond differently to the voice command ([Col 4, Rows 47-61] - An NLU model for the source voice-enabled device may be biased such that text data representing speech utterances is analyzed to determine intents which are tailored for the device capabilities of the source voice-enabled device, such as music steaming intents, shopping intents, alarm/timer intents, etc. Conversely, an NLU model for the secondary voice-enabled device may be trained or biased such that text data representing speech utterances is analyzed to determine intents which are tailored for the device capabilities of the secondary voice-enabled device, such as video steaming intents, music intents, etc. In this way, more ambiguous speech utterances that may be analyzed by the different NLU models may result in different intents depending on which of the source or secondary voice-enabled device is used as a target device);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Torok to include the teaching of Lan by implementing determining, based on speech recognition, that a plurality of different devices are configured to respond differently to the voice command. Doing so allows for multiple voice-enabled devices performing the same task for a user, allowing for an improved user experience (Lan [Col 1, Rows 16-22]).
However, Torok in view of Lan does not teach identifying, based on a comparison of the background noise to a database of audio fingerprints, a mediacontent that produced at least a portion of the background noise;
Kraut does teach identifying, based on a comparison of the background noise to a database of audio fingerprints, a media content that produced at least a portion of the background noise ([0034] - Audio fingerprints of ambient noise for various environments can be stored in the mobile device or on a network accessible by the mobile device. Different actions can be taken for different environments. Thus, the mobile device can identify its local environment by sampling ambient noise present in the local environment, computing an audio fingerprint from the sampled audio noise, comparing the audio fingerprint with reference audio fingerprints stored in a database to find a match, and thus identify a type of ambient noise or environment [0036] - one or more actions can include the automatic invocation and/or display of subtitles and/or closed captions with the currently playing content [0039] - the frequency content of the ambient noise signal can be analyzed to determine if any particular frequency or frequency range exceeds a threshold energy level. In this example, an action may include rebalancing the frequency content of the audio signal (e.g., soundtrack of a video) so that it can be heard more clearly within the ambient noise of the local environment).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Torok in view of Lan to include the teaching of Kraut by implementing identifying, based on a comparison of the background noise to a database of audio fingerprints, a media content that produced at least a portion of the background noise. Doing so allows noise cancellation technology found in some high-end headphones to be moved to the mobile device which may provide higher fidelity audio with ear bud style headphones (Kraut [0039]).
However, Torok in view of Lan in view of Kraut does not teach selecting one of the plurality of different devices, wherein the selecting is based on determining that the one of the plurality of different devices is compatible with outputting the media content;
and causing, based on the selecting, the one of the plurality of different devices to perform an action corresponding to the voice command.
Newendorp does teach selecting one of the plurality of different devices, wherein the selecting is based on determining that the one of the plurality of different devices is compatible with outputting the media content ([0183] - Because multiple devices may satisfy a task requirement, the second device may additionally or alternatively be identified in accordance with a plurality of prioritization rules. In some examples, the second device may be a device “best suited” to perform the task. For tasks directed to video playback, for instance, the second device may be an electronic device having a largest display. In the illustrated example, the electronic device 602 may determine that the electronic device 606 is best suited for video playback, for instance, based on relative display sizes of the electronic devices 602-606. For tasks directed to mapping functions, the second device may be a mobile device. In some examples, the second device may be identified based on prior use. For example, the device typically used to perform a task may be identified to perform subsequent iterations of the task or similar tasks. In some embodiments, the second device may be identified based on signal strength between the first device and each of the other devices. [0196] - the output is provided by an electronic device of the plurality of electronic devices other than the first electronic device sampling and/or resolving the audio input and a second electronic device performing the task. By way of example, the user may speak into a first electronic device (e.g., mobile phone), and a wearable electronic device of the user (e.g., smart watch) may provide an output, such as vibration indicating that the task is being provided to a third device (e.g., television));
and causing, based on the selecting, the one of the plurality of different devices to perform an action corresponding to the voice command ([0183] - Because multiple devices may satisfy a task requirement, the second device may additionally or alternatively be identified in accordance with a plurality of prioritization rules. In some examples, the second device may be a device “best suited” to perform the task. For tasks directed to video playback, for instance, the second device may be an electronic device having a largest display. In the illustrated example, the electronic device 602 may determine that the electronic device 606 is best suited for video playback, for instance, based on relative display sizes of the electronic devices 602-606. For tasks directed to mapping functions, the second device may be a mobile device. In some examples, the second device may be identified based on prior use. For example, the device typically used to perform a task may be identified to perform subsequent iterations of the task or similar tasks. In some embodiments, the second device may be identified based on signal strength between the first device and each of the other devices. [0196] - the output is provided by an electronic device of the plurality of electronic devices other than the first electronic device sampling and/or resolving the audio input and a second electronic device performing the task. By way of example, the user may speak into a first electronic device (e.g., mobile phone), and a wearable electronic device of the user (e.g., smart watch) may provide an output, such as vibration indicating that the task is being provided to a third device (e.g., television)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Torok in view of Lan in view of Kraut to include the teaching of Newendorp by implementing teach selecting one of the plurality of different devices, wherein the selecting is based on determining that the one of the plurality of different devices is compatible with outputting the media content; and causing, based on the selecting, the one of the plurality of different devices to perform an action corresponding to the voice command. Doing so allows a task to be performed by a device having a particular capability (e.g., functionality) (Newendorp [0182]).
Regarding claim 2, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 1, above.
Torok discloses the method, further comprising generating, based on contextual information associated with the audio, a narrowed search space in the database of audio fingerprints, wherein the selecting the one of the plurality of different devices is based on determining that the one of the plurality of different devices is compatible with outputting media of the narrowed search space ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models).
Regarding claim 3, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 1, above.
Torok discloses the method further comprising:
determining, based on the audio, an identity of a user who spoke the voice command ([Col 11, Rows 55-57] - speaker recognition (770) included in the classifier system 652 to identify whether a new or unrecognized voice is heard by the device 600);
and generating, based on one or more viewing characteristics of the user, a narrowed search space in the database of audio fingerprints, wherein the selecting the one of the plurality of different devices is based on determining that one of the plurality of different devices is compatible with outputting media of the narrowed search space ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models).
Regarding claim 4, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 1, above.
Torok discloses the method, further comprising:
receiving a video image associated with the audio ([Col 3, Rows 22-23] - The ASR device 200 may also include a video output device 216 for displaying images);
identifying one or more visual objects in the video image ([Col 8, Rows 12-22] - The generation of “output 420 may be a wide variety of activities. For example, the output may correspond to a user watching a video, listening to music, playing a video game, etc. The output may be sound output via audio output device 214, video output by video output device 216 or some com bination thereof. Likewise, it may be an output streamed out onto a network (e.g., 1102 in FIG. 11) via input/output device interfaces 202. The output may, for example, originate with one or more applications running on controller/processor 204, or may be an output stream of data stored in memory 206 or in storage 208);
and generating, based on the one or more visual objects, a narrowed search space in the database of audio fingerprints, wherein the selecting the one of the plurality of different devices is based on determining that one of the plurality of different devices is compatible with outputting media of the narrowed search space ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models).
Regarding claim 5, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 1, above.
Torok discloses the method, further comprising:
receiving information indicating a quality of the media content ([Col 8, Rows 12-22] - The generation of “output 420 may be a wide variety of activities. For example, the output may correspond to a user watching a video, listening to music, playing a video game, etc. The output may be sound output via audio output device 214, video output by video output device 216 or some com bination thereof. Likewise, it may be an output streamed out onto a network (e.g., 1102 in FIG. 11) via input/output device interfaces 202. The output may, for example, originate with one or more applications running on controller/processor 204, or may be an output stream of data stored in memory 206 or in storage 208);
and generating, based on the quality, a narrowed search space in the database of audio fingerprints, wherein the selecting the one of the plurality of different devices is based on determining that one of the plurality of different devices is compatible with outputting media of the narrowed search space ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models).
Regarding claim 6, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 1, above.
Torok discloses the method, further comprising:
receiving information indicating a content source currently in use ([Col 6, Rows 57-65] - Alternatively, post front-end processed data (Such as feature vectors) may be received by the AR module 230 from another source besides the internal AFE 250. For example, another entity may process audio data into feature vectors and transmit that information to the ASR device 200 through the input/output device interfaces 202. Feature vectors may arrive at the ASR device 200 encoded, in which case they may be decoded prior to processing by the speech recognition engine 232);
determining content items available from the content source ([Col 6, Rows 57-65] - Alternatively, post front-end processed data (Such as feature vectors) may be received by the AR module 230 from another source besides the internal AFE 250. For example, another entity may process audio data into feature vectors and transmit that information to the ASR device 200 through the input/output device interfaces 202. Feature vectors may arrive at the ASR device 200 encoded, in which case they may be decoded prior to processing by the speech recognition engine 232);
and generating, based on the content items available from the content source, a narrowed search space in the database of audio fingerprints, wherein the selecting the one of the plurality of different devices is based on determining that one of the plurality of different devices is compatible with outputting media of the narrowed search space ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models).
Regarding claim 9, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 1, above.
Torok discloses the method, wherein the selecting the one of the plurality of different devices is further based on plurality of environmental conditions ([Col 10, Rows 20-23] - any of several modification actions may be undertaken depending upon the action or actions associated with the acoustic fingerprint/model, a context-based rule set [Col 10, Rows 20-23] - any of several modification actions may be undertaken depending upon the action or actions associated with the acoustic fingerprint/ model, a context-based rule set).
Regarding claim 10, Torok in view of Lan in view of Kraut teaches all of the limitations discussed in claim 1, above.
Torok discloses the method, further comprising:
receiving information indicating an application currently in use ([Col 8, Rows 33-40] - the acoustic fingerprint engine 242 might trigger a predefined interrupt of controller/processor 204 or send a higher-level command to an application programming inter face to trigger the modification of output via either the operating system or an application running on controller/processor 204, or send a message signal via a network connection provided by input/output device interfaces 202);
and generating, based on the application, a narrowed search space in the database of audio fingerprints, wherein selecting the one of the plurality of different devices is based on determining that one of the plurality of different devices is compatible with outputting media of the narrowed search space ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models).
Regarding claim 11, Torok discloses a method comprising:
receiving, by a computing device, recorded audio ([Col 2, Rows 23-24] - The speech enabled device 100 may then monitor for a user command (such as a voice command) [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models);
determining, based on speech recognition, that the recorded audio comprises ([Col 2, Rows 23-24] - The speech enabled device 100 may then monitor for a user command (such as a voice command) [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models).
However, Torok does not disclose an ambiguous voice command;
Lan does teach an ambiguous voice command ([Col 4, Rows 49-51, 57-65] – determine intents which are tailored for the device capabilities of the source voice-enabled device, such as music streaming intents…In this way, more ambiguous speech utterances that may be analyzed by the different NLU models may result in different intents depending on which of the source or secondary voice-enabled device is used as a target device. For instance, the remote system may analyze text data representing a speech utterance of “bread” using an NLU model of the source voice-enabled device and determine an intent for the source voice-enabled device to add a loaf of bread to a shopping list.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Torok to include the teaching of Lan by implementing an ambiguous voice command. Doing so allows for multiple voice-enabled devices performing the same task for a user, allowing for an improved user experience (Lan [Col 1, Rows 16-22]).
However, Torok in view of Lan does not teach identifying a media content that produced at least a portion of the background noise.
Kraut does teach identifying a video content that produced at least a portion of the background noise ([0034] - Audio fingerprints of ambient noise for various environments can be stored in the mobile device or on a network accessible by the mobile device. Different actions can be taken for different environments. Thus, the mobile device can identify its local environment by sampling ambient noise present in the local environment, computing an audio fingerprint from the sampled audio noise, comparing the audio fingerprint with reference audio fingerprints stored in a database to find a match, and thus identify a type of ambient noise or environment [0036] - one or more actions can include the automatic invocation and/or display of subtitles and/or closed captions with the currently playing content [0039] - the frequency content of the ambient noise signal can be analyzed to determine if any particular frequency or frequency range exceeds a threshold energy level. In this example, an action may include rebalancing the frequency content of the audio signal (e.g., soundtrack of a video) so that it can be heard more clearly within the ambient noise of the local environment);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Torok in view of Lan to include the teaching of Kraut by implementing identifying a video content that produced at least a portion of the background noise. Doing so allows noise cancellation technology found in some high-end headphones to be moved to the mobile device which may provide higher fidelity audio with ear bud style headphones (Kraut [0039]).
However, Torok in view of Lan in view of Kraut does not teach resolving the ambiguous voice command, by selecting one of the plurality of different devices, wherein the selecting is based on determining that the one of the plurality of different devices is compatible with outputting the media content;
causing, based on the resolving the ambiguous voice command, the one of the plurality of different devices to perform an action corresponding to the ambiguous voice command.
Newendorp does resolving the ambiguous voice command, by selecting one of the plurality of different devices, wherein the selecting is based on determining that the one of the plurality of different devices is compatible with outputting the media content ([0183] - Because multiple devices may satisfy a task requirement, the second device may additionally or alternatively be identified in accordance with a plurality of prioritization rules. In some examples, the second device may be a device “best suited” to perform the task. For tasks directed to video playback, for instance, the second device may be an electronic device having a largest display. In the illustrated example, the electronic device 602 may determine that the electronic device 606 is best suited for video playback, for instance, based on relative display sizes of the electronic devices 602-606. For tasks directed to mapping functions, the second device may be a mobile device. In some examples, the second device may be identified based on prior use. For example, the device typically used to perform a task may be identified to perform subsequent iterations of the task or similar tasks. In some embodiments, the second device may be identified based on signal strength between the first device and each of the other devices. [0196] - the output is provided by an electronic device of the plurality of electronic devices other than the first electronic device sampling and/or resolving the audio input and a second electronic device performing the task. By way of example, the user may speak into a first electronic device (e.g., mobile phone), and a wearable electronic device of the user (e.g., smart watch) may provide an output, such as vibration indicating that the task is being provided to a third device (e.g., television));
causing, based on the resolving the ambiguous voice command, the one of the plurality of different devices to perform an action corresponding to the ambiguous voice command ([0183] - Because multiple devices may satisfy a task requirement, the second device may additionally or alternatively be identified in accordance with a plurality of prioritization rules. In some examples, the second device may be a device “best suited” to perform the task. For tasks directed to video playback, for instance, the second device may be an electronic device having a largest display. In the illustrated example, the electronic device 602 may determine that the electronic device 606 is best suited for video playback, for instance, based on relative display sizes of the electronic devices 602-606. For tasks directed to mapping functions, the second device may be a mobile device. In some examples, the second device may be identified based on prior use. For example, the device typically used to perform a task may be identified to perform subsequent iterations of the task or similar tasks. In some embodiments, the second device may be identified based on signal strength between the first device and each of the other devices. [0196] - the output is provided by an electronic device of the plurality of electronic devices other than the first electronic device sampling and/or resolving the audio input and a second electronic device performing the task. By way of example, the user may speak into a first electronic device (e.g., mobile phone), and a wearable electronic device of the user (e.g., smart watch) may provide an output, such as vibration indicating that the task is being provided to a third device (e.g., television)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Torok in view of Lan in view of Kraut to include the teaching of Newendorp by implementing resolving the ambiguous voice command, by selecting one of the plurality of different devices, wherein the selecting is based on determining that the one of the plurality of different devices is compatible with outputting the media content; causing, based on the resolving the ambiguous voice command, the one of the plurality of different devices to perform an action corresponding to the ambiguous voice command. Doing so allows a task to be performed by a device having a particular capability (e.g., functionality) (Newendorp [0182]).
Regarding claim 12, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 11, above.
Torok discloses the method, wherein the identifying the video content comprises:
generating a narrowed audio fingerprint search space based on contextual information associated with the recorded audio ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models);
and determining, from the narrowed audio fingerprint search space, the media content ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models).
Regarding claim 13, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 11, above.
Torok discloses the method, wherein the identifying the video content comprises:
generating a narrowed audio fingerprint search space based on information indicating content items available from a content service ([Col 6, Rows 57-65] - Alternatively, post front-end processed data (Such as feature vectors) may be received by the AR module 230 from another source besides the internal AFE 250. For example, another entity may process audio data into feature vectors and transmit that information to the ASR device 200 through the input/output device interfaces 202. Feature vectors may arrive at the ASR device 200 encoded, in which case they may be decoded prior to processing by the speech recognition engine 232);
and determining, from the narrowed audio fingerprint search space, the media content ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models).
Regarding claim 14, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 11, above.
Torok discloses the method, wherein the identifying comprises:
generating a narrowed audio fingerprint search space based on recognizing a visual object in an image of a screen of the content output device ([Col 8, Rows 12-22] - The generation of “output 420 may be a wide variety of activities. For example, the output may correspond to a user watching a video, listening to music, playing a video game, etc. The output may be sound output via audio output device 214, video output by video output device 216 or some com bination thereof. Likewise, it may be an output streamed out onto a network (e.g., 1102 in FIG. 11) via input/output device interfaces 202. The output may, for example, originate with one or more applications running on controller/processor 204, or may be an output stream of data stored in memory 206 or in storage 208);
and determining, from the narrowed audio fingerprint search space, the media content ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background Sounds may utilize the same pattern recognition system but with different models).
Regarding claim 15, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 11, above.
Torok discloses the method, further comprising storing information associating the ambiguous voice command with a plurality of different voice-enabled devices, wherein the information indicates one or more context conditions for each of the different voice-enabled devices ([Col 9, Rows 9-12] - How the system responds to a recognized noise may be different for different sounds, with the preferences being associated with the model used to recognize the noise. [Col 15, Rows 5-7] - one device may capture input audio from a plurality of audio capture devices 212).
Regarding claim 16, Torok discloses a method comprising:
receiving, by a computing device, audio comprising a voice command and background noise ([Col 2, Rows 23-24] - The speech enabled device 100 may then monitor for a user command (such as a voice command [Col 4, Rows 32-34] - the acoustic fingerprinting engine 242 of the classifier system 252 to recognize background sounds may utilize the same pattern recognition system but with different models);
determining, based on speech recognition, that the voice command comprises a request for content recommendation ([Abstract] - “received audio…may trigger a chance in device operation…”. [Col 4, Rows 38-42] - “The speech recognition engine 232 transcribes audio data into text data representing the words of the speech contained in the audio data. The text data may then be used by other components for various purposes, such as executing system commands, inputting data, etc.”. [Col 9, Rows 9-12] - How the system responds to a recognized noise may be different for different sounds, with the preferences being associated with the model used to recognize the noise. [Col 15, Rows 5-7] - one device may capture input audio from a plurality of audio capture devices 212);
and causing display of the list ([Col 3, Rows 22-23] - The ASR device 200 may also include a video output device 216 for displaying images).
However, Torok does not disclose generating a list of other video content items that are similar to the media content.
Lan does teach generating a list of other video content items that are similar to the media content ([Col 4, Rows 47-61] - An NLU model for the source voice-enabled device may be biased such that text data representing speech utterances is analyzed to determine intents which are tailored for the device capabilities of the source voice-enabled device, such as music steaming intents, shopping intents, alarm/timer intents, etc. Conversely, an NLU model for the secondary voice-enabled device may be trained or biased such that text data representing speech utterances is analyzed to determine intents which are tailored for the device capabilities of the secondary voice-enabled device, such as video steaming intents, music intents, etc. In this way, more ambiguous speech utterances that may be analyzed by the different NLU models may result in different intents depending on which of the source or secondary voice-enabled device is used as a target device [Col 5, Rows 53-58] - Thus, the remote system may use device-state data of the voice-enabled devices in order to determine which of the two intents is appropriate for responding to the speech utterance, generate a command based on the selected intent, and send the command to the voice-enabled device whose intent was selected (e.g., the target device).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Torok to include the teaching of Lan by implementing generating a list of other video content items that are similar to the video content item. Doing so allows for multiple voice-enabled devices performing the same task for a user, allowing for an improved user experience (Lan [Col 1, Rows 16-22]).
However, Torok in view of Lan does not teach identifying, based on a comparison of the background noise to a database of audio fingerprints, a media content that produced at least a portion of the background noise.
Kraut does teach identifying, based on a comparison of the background noise to a database of audio fingerprints, a media content that produced at least a portion of the background noise ([0034] - Audio fingerprints of ambient noise for various environments can be stored in the mobile device or on a network accessible by the mobile device. Different actions can be taken for different environments. Thus, the mobile device can identify its local environment by sampling ambient noise present in the local environment, computing an audio fingerprint from the sampled audio noise, comparing the audio fingerprint with reference audio fingerprints stored in a database to find a match, and thus identify a type of ambient noise or environment [0036] - one or more actions can include the automatic invocation and/or display of subtitles and/or closed captions with the currently playing content [0039] - the frequency content of the ambient noise signal can be analyzed to determine if any particular frequency or frequency range exceeds a threshold energy level. In this example, an action may include rebalancing the frequency content of the audio signal (e.g., soundtrack of a video) so that it can be heard more clearly within the ambient noise of the local environment).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Torok in view of Lan to include the teaching of Kraut by implementing identifying, based on a comparison of the background noise to a database of audio fingerprints, a video content item that produced at least a portion of the background noise. Doing so allows noise cancellation technology found in some high-end headphones to be moved to the mobile device which may provide higher fidelity audio with ear bud style headphones (Kraut [0039]).
However, Torok in view of Lan in view of Kraut does not teach selecting one of the plurality of different devices, wherein the selecting is based on determining that the one of the plurality of different devices is compatible with outputting the media content;
and generating a list of other media content items that are similar to the media content and are capable of being output on the one of the plurality of different devices.
Newendorp does teach selecting one of the plurality of different devices, wherein the selecting is based on determining that the one of the plurality of different devices is compatible with outputting the media content ([0183] - Because multiple devices may satisfy a task requirement, the second device may additionally or alternatively be identified in accordance with a plurality of prioritization rules. In some examples, the second device may be a device “best suited” to perform the task. For tasks directed to video playback, for instance, the second device may be an electronic device having a largest display. In the illustrated example, the electronic device 602 may determine that the electronic device 606 is best suited for video playback, for instance, based on relative display sizes of the electronic devices 602-606. For tasks directed to mapping functions, the second device may be a mobile device. In some examples, the second device may be identified based on prior use. For example, the device typically used to perform a task may be identified to perform subsequent iterations of the task or similar tasks. In some embodiments, the second device may be identified based on signal strength between the first device and each of the other devices. [0196] - the output is provided by an electronic device of the plurality of electronic devices other than the first electronic device sampling and/or resolving the audio input and a second electronic device performing the task. By way of example, the user may speak into a first electronic device (e.g., mobile phone), and a wearable electronic device of the user (e.g., smart watch) may provide an output, such as vibration indicating that the task is being provided to a third device (e.g., television));
and generating a list of other media content items that are similar to the media content and are capable of being output on the one of the plurality of different devices ([0183] - Because multiple devices may satisfy a task requirement, the second device may additionally or alternatively be identified in accordance with a plurality of prioritization rules. In some examples, the second device may be a device “best suited” to perform the task. For tasks directed to video playback, for instance, the second device may be an electronic device having a largest display. In the illustrated example, the electronic device 602 may determine that the electronic device 606 is best suited for video playback, for instance, based on relative display sizes of the electronic devices 602-606. For tasks directed to mapping functions, the second device may be a mobile device. In some examples, the second device may be identified based on prior use. For example, the device typically used to perform a task may be identified to perform subsequent iterations of the task or similar tasks. In some embodiments, the second device may be identified based on signal strength between the first device and each of the other devices. [0196] - the output is provided by an electronic device of the plurality of electronic devices other than the first electronic device sampling and/or resolving the audio input and a second electronic device performing the task. By way of example, the user may speak into a first electronic device (e.g., mobile phone), and a wearable electronic device of the user (e.g., smart watch) may provide an output, such as vibration indicating that the task is being provided to a third device (e.g., television)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to modify the teaching of Torok in view of Lan in view of Kraut to include the teaching of Newendorp by implementing teach selecting one of the plurality of different devices, wherein the selecting is based on determining that the one of the plurality of different devices is compatible with outputting the media content; and generating a list of other media content items that are similar to the media content and are capable of being output on the one of the plurality of different devices. Doing so allows a task to be performed by a device having a particular capability (e.g., functionality) (Newendorp [0182]).
Regarding claim 17, Torok in view of Lan in view of Kraut in view of Newendorp teaches all limitations of claim 16, above.
Torok discloses the method, wherein the identifying the video content item comprises:
generating a narrowed search space based on contextual information associated with the audio ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly);
and identifying, from the narrowed search space, the media content ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly).
Regarding claim 20, Torok in view of Lan in view of Kraut in view of Newendorp teaches all limitations of claim 16, above.
Torok discloses the method, wherein the identifying comprises:
receiving information indicating an application currently in use ([Col 8, Rows 33-40] - the acoustic fingerprint engine 242 might trigger a predefined interrupt of controller/processor 204 or send a higher-level command to an application programming inter face to trigger the modification of output via either the operating system or an application running on controller/processor 204, or send a message signal via a network connection provided by input/output device interfaces 202);
and determining a search space comprising the other media content associated with the application ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly);
and searching the search space to find a match between the background noise and an audio of the media content in the search space ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly).
Regarding claim 21, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 1, above.
Torok discloses the method, wherein the identifying comprises:
determining that the one of the plurality of different devices is outputting the media content while receiving the voice command ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly);
identifying a title of the media content ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly).
Regarding claim 24, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 1, above.
Torok does not disclose the method, wherein the voice command corresponds to a media command and a temperature command, and the method futher comprises selecting the media command based on recognizing that the media content was being output while receiving the voice command.
Kraut does teach the method, wherein the voice command corresponds to a media command and a temperature command, and the method futher comprises selecting the media command based on recognizing that the media content was being output while receiving the voice command ([0041] - Sensors, devices, and subsystems can be coupled to the peripherals interface 506 to facilitate multiple functionalities. For example, a motion sensor 510, a light sensor 512, and a proximity sensor 514 can be coupled to the peripherals interface 506 to facilitate the orientation, lighting, and proximity functions described with respect to FIG. 1. Other sensors 516 can also be connected to the peripherals interface 506, such as a positioning system (e.g., GPS receiver), a temperature sensor, a biometric sensor, or other sensing device, to facilitate related functionalities).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Torok in view of Lan to incorporate the teachings of Kraut in order to implement the method, wherein the voice command corresponds to a media command and a temperature command, and the method futher comprises selecting the media command based on recognizing that the media content was being output while receiving the voice command. Doing so allows noise cancellation technology found in some high-end headphones to be moved to the mobile device which may provide higher fidelity audio with ear bud style headphones (Kraut [0039]).
7. Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Torok (U.S. Patent No. 9324322) in view of Lan (U.S. Patent No. 10685669) in view of Kraut (U.S. Publication No. 20100146445) in view of Newendorp (U.S. Publication No. 20170092270) in view of Mahmood (U.S. Publication No. 20210090575).
Regarding claim 7, Torok in view of Lan in view of Kraut in view of Newendorp teaches all of the limitations discussed in claim 1, above.
However, Torok does not disclose the method, wherein the determining that a plurality of different devices are configured to respond differently to the voice command comprises determining that the voice command corresponds to:
adjusting an audio volume of a content output device;
and adjusting a temperature setting on a thermostat,
and wherein selecting the one of the plurality of different devices is further based on selecting between the content output device and the thermostat.
Mahmood does teach he method, wherein the determining that a plurality of different devices are configured to respond differently to the voice command comprises determining that the voice command corresponds to:
adjusting an audio volume of a content output device ([0031] - assistant may be configured to preface the performance of smart vehicle actions (e.g., roll windows up and down, alter internal vehicle environment temperature, etc.) with certain editorial content. [0066] - the TTS component 280 varies parameters such as frequency, volume, and noise to generate audio data including an artificial speech waveform);
and adjusting a temperature setting on a thermostat ([0031] - assistant may be configured to preface the performance of smart vehicle actions (e.g., roll windows up and down, alter internal vehicle environment temperature, etc.) with certain editorial content. [0066] - the TTS component 280 varies parameters such as frequency, volume, and noise to generate audio data including an artificial speech waveform),
and wherein selecting the one of the plurality of different devices is further based on selecting between the content output device and the thermostat ([0031] - assistant may be configured to preface the performance of smart vehicle actions (e.g., roll windows up and down, alter internal vehicle environment temperature, etc.) with certain editorial content. [0066] - the TTS component 280 varies parameters such as frequency, volume, and noise to generate audio data including an artificial speech waveform).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Torok in view of Lan to incorporate the teachings of Mahmood in order to implement the method, wherein the determining that a plurality of different devices are configured to respond differently to the voice command comprises determining that the voice command corresponds to: adjusting an audio volume of a content output device; and adjusting a temperature setting on a thermostat, and wherein selecting the one of the plurality of different devices is further based on selecting between the content output device and the thermostat. Doing so allows an increased user experience with the NLP system (Mahmood [0032]).
Regarding claim 8, Torok in view of Lan in view of Kraut in view of Newendorp discloses all of the limitations discussed in claim 1, above.
However, Torok does not disclose the method, wherein the determining that a plurality of different devices are configured to respond differently to the voice command comprises determining that the voice command corresponds to:
adjusting an audio volume of a content output device;
and adjusting a temperature setting on a thermostat, and wherein the identifying the media content is further based on:
a current temperature in a room associated with the audio;
a current volume level of the audio;
or one or more content sources or applications currently in use by the content output device.
Mahmood does teach the method, wherein the determining that a plurality of different devices are configured to respond differently to the voice command comprises determining that the voice command corresponds to:
adjusting an audio volume of a content output device ([0066] - the TTS component 280 varies parameters such as frequency, volume, and noise to generate audio data including an artificial speech waveform);
and adjusting a temperature setting on a thermostat, and wherein the identifying the media content is further based on ([0031] - assistant may be configured to preface the performance of smart vehicle actions (e.g., roll windows up and down, alter internal vehicle environment temperature, etc.) with certain editorial content):
a current temperature in a room associated with the audio ([0044] - the second data may include temperature information);
a current volume level of the audio ([0066] – the TTS component 280 varies parameters such as frequency, volume, and noise to generate audio data including an artificial speech waveform);
or one or more content sources or applications currently in use by the content output device ([0026] - An assistant may be configured to have, for example, a unique voice (e.g., TTS configurations and/or recorded user speech), editorial content (e.g. , TTS - generated audio output to a user prior to content provided by a skill system and TTS-generated audio output to a user after content provided by a skill system), skill system capabilities, a “personality” (e.g., programmed to use positive, optimistic, and/or other language that gives the perception of the system to having a distinctive personality), and/or specific access permissions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Torok in view of Lan to incorporate the teachings of Mahmood in order to implement the method, wherein the determining that a plurality of different devices are configured to respond differently to the voice command comprises determining that the voice command corresponds to: adjusting an audio volume of a content output device; and adjusting a temperature setting on a thermostat, and wherein the identifying the video content is further based on: a current temperature in a room associated with the audio; a current volume level of the audio; or one or more content sources or applications currently in use by the content output device. Doing so allows an increased user experience with the NLP system (Mahmood [0032]).
8. Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Torok (U.S. Patent No. 9324322) in view of Lan (U.S. Patent No. 10685669) in view of Kraut (U.S. Publication No. 20100146445) in view of Newendorp (U.S. Publication No. 20170092270) in view of Sanchez (U.S. Publication No. 20200021894).
Regarding claim 18, Torok in view of Lan in view of Kraut in view of Newendorp discloses all of the limitations discussed in claim 16, above.
Torok discloses the method, wherein the identifying the video content item comprises:
searching the search space to find a match between the background noise and an audio of the media content in the search space ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly).
However, Torok does not disclose determining, based on identifying one or more objects in an image of a screen of the one of the plurality of different devices outputting the media content, a genre of the media content;
determining a search space associated with the genre.
Sanchez does teach determining, based on identifying one or more objects in an image of a screen of the one of the plurality of different devices outputting the media content, a genre of the media content ([0016] - media guidance application may identify…graphical commentary (e.g., drawings, images, markups, etc. [0086] – the guidance data may include… genre or category information, actor information, logo data);
determining a search space associated with the genre ([0086] – the guidance data may include… genre or category information, actor information, logo data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Torok in view of Lan in view of Kraut in view of Newendorp to incorporate the teachings of Sanchez in order to implement determining, based on identifying one or more objects in an image of a screen of the one of the plurality of different devices outputting the media content, a genre of the media content; determining a search space associated with the genre. Doing so allows a user to navigate among and locate desired content selections (Sanchez [0086]).
Regarding claim 19, Torok in view of Lan in view of Kraut in view of Newendorp discloses all of the limitations discussed in claim 16, above.
Torok discloses the method, wherein the identifying comprises:
searching the search space to find a match between the background noise and an audio of the media content in the search space ([Col 5, Rows 22-32] - The speech recognition engine 232 compares the speech component of received audio data with the acoustic, language, and other data models and information stored in the speech Storage 234 for recognizing the speech contained in the original audio data. Similarly, the acoustic fingerprint engine 242 compares audio interruption data with acoustic fingerprints/ acoustic models stored in fingerprint storage 244, searching for a match that exceeds a baseline degree of certainty. Some common acoustic fingerprints/models (e.g. doorbell) may be preprogrammed, while others may be learned on-the-fly).
However, Torok does not disclose identifying, from an image of a screen of a content output device, a logo;
determining a search space comprising content items associated with the logo.
Sanchez does teach identifying, from an image of a screen of a content output device, a logo ([0016] - media guidance application may identify…graphical commentary (e.g., drawings, images, markups, etc. [0086] – the guidance data may include… genre or category information, actor information, logo data);
determining a search space comprising content items associated with the logo ([0086] – the guidance data may include… genre or category information, actor information, logo data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Torok in view of Lan in view of Kraut in view of Newendorp to incorporate the teachings of Sanchez in order to implement identifying, from an image of a screen of a content output device, a logo; determining a search space comprising content items associated with the logo. Doing so allows a user to navigate among and locate desired content selections (Sanchez [0086]).
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Duncan (U.S. Publication No. 20210352380) teaches characterizing content for audio-video dubbing and other transformations. Wang (U.S. Publication No. 20020083060) teaches system and methods for recognizing sound and music signals in high noise and distortion.
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/ETHAN DANIEL KIM/
Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658