DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-2, 4, 7-9, 11, 14-16, and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 8-9, and 15-16 of U.S. Patent No. 12165644 in view of Hoffmeister (USPN 10388274).
Since the patent and Hoffmeister are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of initiating and ceasing extraction of audio to send to a remote computing system for further processing. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Claims 3, 10, and 17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, and 15 of U.S. Patent No. 12165644 in view of Hoffmeister and further in view of Vanderschaegen et al. (USPN 10978062).
Since the patent and Vanderschaegen are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of providing notification when a wakeword is detected in the received signal. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Claims 5-6, 12-13, and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, and 15 of U.S. Patent No. 12165644 in view of Hoffmeister and further in view of Ullrich et al. (“Soft Weight-Sharing for Neural Network Compression”, ICLR, 5/9/2017)
Since Hoffmeister and Ullrich are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of utilizing a soft weight-shared neural network model stored in compressed sparse row format. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Claims 1-2, 4, 7-9, 11, 14-16, and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 8-9, and 15-16 of U.S. Patent No. 11100923 in view of Hoffmeister and further in view of Hoffmeister (USPN 10388274).
Since the patent and Hoffmeister are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of initiating and ceasing extraction of audio to send to a remote computing system for further processing. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Claims 3, 10, and 17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, and 15 of U.S. Patent No. 11100923 in view of Hoffmeister and further in view of Vanderschaegen et al. (USPN 10978062).
Since the patent and Vanderschaegen are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of providing notification when a wakeword is detected in the received signal. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Claims 5-6, 12-13, and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, and 15 of U.S. Patent No. 11100923 in view of Hoffmeister and further in view of Ullrich et al. (“Soft Weight-Sharing for Neural Network Compression”, ICLR, 5/9/2017).
Since Hoffmeister and Ullrich are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of utilizing a soft weight-shared neural network model stored in compressed sparse row format. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Claims 1-2, 4, 7-9, 11, 14-16, and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 8-9, and 15-16 of U.S. Patent No. 11790911 in view of Hoffmeister and further in view of Hoffmeister (USPN 10388274).
Since the patent and Hoffmeister are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of initiating and ceasing extraction of audio to send to a remote computing system for further processing. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Claims 3, 10, and 17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, and 15 of U.S. Patent No. 11790911 in view of Hoffmeister and further in view of Vanderschaegen et al. (USPN 10978062).
Since the patent and Vanderschaegen are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of providing notification when a wakeword is detected in the received signal. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Claims 5-6, 12-13, and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, and 15 of U.S. Patent No. 11790911 in view of Hoffmeister and further in view of Ullrich et al. (“Soft Weight-Sharing for Neural Network Compression”, ICLR, 5/9/2017).
Since Hoffmeister and Ullrich are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of utilizing a soft weight-shared neural network model stored in compressed sparse row format. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Claims of application Claims of USPN 12165644
1. (New) A network microphone device comprising: one or more processors; one or more microphones; and data storage having instructions stored thereon that, when executed by the one or more processors, cause the network microphone device to perform operations comprising:
capturing sound data via the one or more microphones;
processing the sound data using a first wake word detection algorithm to identify a candidate wake word in the sound data;
after initiating the extraction, analyzing the sound data using a second wake word detection algorithm to verify presence of the candidate wake word, wherein the second wake word detection algorithm has greater accuracy than the first wake word detection algorithm; and
in response to identifying the candidate wake word, initiating extraction of detected-sound data from a buffer;
ceasing the extraction of the detected-sound data from the buffer in response to determining that the second wake word detection algorithm failed to verify the presence of the candidate wake word.
2. (New) The network microphone device of claim 1, wherein initiating extraction of detected-sound data from the buffer comprises beginning to package the detected-sound data according to a format for transmission to a voice assistant service.
3. (New) The network microphone device of claim 1, wherein the operations further comprise outputting an alert indicating detection of the candidate wake word before analyzing the sound data using the second wake word detection algorithm.
4. (New) The network microphone device of claim 1, wherein the operations further comprise selecting the second wake word detection algorithm from among a plurality of wake word detection algorithms based on the identified candidate wake word.
5. (New) The network microphone device of claim 1, wherein processing the sound data using the first wake word detection algorithm comprises applying a compressed neural network model to the sound data to identify the candidate wake word.
6. (New) The network microphone device of claim 5, wherein the compressed neural network model comprises a soft weight-shared neural network model stored in compressed sparse row format.
7. (New) The network microphone device of claim 1, wherein analyzing the sound data using the second wake word detection algorithm comprises activating a wake word engine from a low-power state to process the sound data while maintaining other wake word engines in the low-power state.
All other claims are similar to the above claims.
1. A network microphone device, comprising: one or more processors; one or more microphones; and data storage having instructions stored thereon that, when executed by the one or more processors, cause the network microphone device to perform operations comprising:
capturing sound data via the one or more microphones;
identifying, using a keyword spotting algorithm, a candidate wake word in the sound data;
based on identification of the candidate wake word in the sound data via the keyword spotting algorithm, selecting a first wake-word detection algorithm from among a plurality of wake-word detection algorithms stored on the network microphone device, wherein the first wake-word detection algorithm is associated with a first voice assistant service and another of the plurality of wake-word detection algorithms is associated with a second voice assistant service different from the first; after selecting the first wake-word detection algorithm, and without using another of the plurality of wake-word detection algorithms, using the first wake-word detection algorithm to analyze the sound data to confirm detection of the candidate wake word identified via the keyword spotting algorithm, wherein the first wake-word detection algorithm is configured to determine whether the candidate wake word is present in the sound data with a higher accuracy than the keyword spotting algorithm; and in response to confirming the detection of the candidate wake word, processing a voice utterance of the sound data to determine an intent.
The patent fails to explicitly disclose these limitations. However, Hoffsmeister teaches these limitations (see rejection below).
2. The network microphone device of claim 1, wherein processing the voice utterance comprises transmitting the voice utterance to one or more remote computing devices associated with the first voice assistant service.
The patent fails to explicitly disclose these limitations. However, Vanderschaegen teaches these limitations (see rejection below).
The patent claim 1 above teaches dependent claim 4 of application
The patent fails to explicitly disclose these limitations. However, Ullrich teaches these limitations (see rejection below).
The patent fails to explicitly disclose these limitations. However, Ullrich teaches these limitations (see rejection below).
The patent’s claim 1 above teaches dependent claim 7 of the application.
3. The network microphone device of claim 1, wherein processing the voice utterance is performed locally.
4. The network microphone device of claim 1, wherein analyzing the sound data to confirm detection of the candidate wake word using the using the first wake-word detection algorithm, and without using another of the wake-word detection algorithms comprises: activating a first wake-word engine to process the sound data using the first wake-word detection algorithm while a second wake-word engine configured to process the sound data using another of the wake-word detection algorithms is in an inactive state.
5. The network microphone device of claim 1, wherein identifying the candidate wake word comprises applying a neural network model to the sound data.
6. The network microphone device of claim 1, further comprising, after processing the voice utterance, receiving, via the network microphone device, a selection of media content related to the voice utterance.
7. The network microphone device of claim 1, wherein the plurality of wake-word detection algorithms comprises: the first wake-word detection algorithm; and a second wake-word detection algorithm configured to perform a local function of the network microphone device.
All other claims are similar to the above claims.
Claims of application Claims of USPN 11100923
1. (New) A network microphone device comprising: one or more processors; one or more microphones; and data storage having instructions stored thereon that, when executed by the one or more processors, cause the network microphone device to perform operations comprising:
capturing sound data via the one or more microphones;
processing the sound data using a first wake word detection algorithm to identify a candidate wake word in the sound data;
after initiating the extraction, analyzing the sound data using a second wake word detection algorithm to verify presence of the candidate wake word, wherein the second wake word detection algorithm has greater accuracy than the first wake word detection algorithm.
in response to identifying the candidate wake word, initiating extraction of detected-sound data from a buffer;
ceasing the extraction of the detected-sound data from the buffer in response to determining that the second wake word detection algorithm failed to verify the presence of the candidate wake word.
2. (New) The network microphone device of claim 1, wherein initiating extraction of detected-sound data from the buffer comprises beginning to package the detected-sound data according to a format for transmission to a voice assistant service.
3. (New) The network microphone device of claim 1, wherein the operations further comprise outputting an alert indicating detection of the candidate wake word before analyzing the sound data using the second wake word detection algorithm.
4. (New) The network microphone device of claim 1, wherein the operations further comprise selecting the second wake word detection algorithm from among a plurality of wake word detection algorithms based on the identified candidate wake word.
5. (New) The network microphone device of claim 1, wherein processing the sound data using the first wake word detection algorithm comprises applying a compressed neural network model to the sound data to identify the candidate wake word.
6. (New) The network microphone device of claim 5, wherein the compressed neural network model comprises a soft weight-shared neural network model stored in compressed sparse row format.
7. (New) The network microphone device of claim 1, wherein analyzing the sound data using the second wake word detection algorithm comprises activating a wake word engine from a low-power state to process the sound data while maintaining other wake word engines in the low-power state.
All other claims are similar to the above claims.
1. A method comprising:
capturing sound data via a network microphone device;
identifying, via the network microphone device, using a keyword spotting algorithm, a candidate wake word in the sound data, the network microphone device comprising a plurality of wake-world engines in a low-power or no-power state;
based on identification of the candidate wake word in the sound data, selecting a first wake-word engine from the plurality of wake-word engines, wherein the first wake-word engine is associated with a first voice assistant service and another of the plurality of wake-word engines is associated with a second voice assistant service different from the first, wherein selecting the first wake-word engine comprises activating the first wake-word engine from the low-power or no-power state to a high-power state, while the other(s) of the plurality of wake-word engines remain(s) in the low-power or no-power state; with the first wake-word engine, analyzing the sound data to confirm detection of a wake word, wherein the first wake-word engine is configured to determine whether the candidate wake work is present in the sound data with a higher accuracy than the keyword spotting algorithm; and in response to confirming the detection of the wake word, transmitting a voice utterance of the sound data to one or more remote computing devices associated with the first voice assistant service.
The patent fails to explicitly disclose these limitations. However, Hoffsmeister teaches these limitations (see rejection below).
Claim 1 above teaches claim 2 of the application.
The patent fails to explicitly disclose these limitations. However, Vanderschaegen teaches these limitations (see rejection below).
Claim 1 above teaches the application’s dependent claim 4.
The patent fails to explicitly disclose these limitations. However, Ullrich teaches these limitations (see rejection below).
The patent fails to explicitly disclose these limitations. However, Ullrich teaches these limitations (see rejection below).
Claim 1 above teaches the application’s dependent claim 7.
2. The method of claim 1, wherein identifying the candidate wake word comprises determining a probability that the candidate wake word is present in the sound data.
3. The method of claim 1, wherein the first wake-word engine is associated with the candidate wake word, and wherein another of the plurality of wake-word engines is associated with one or more additional wake words.
4. The method of claim 1, wherein identifying the candidate wake word comprises applying a neural network model to the sound data.
5. The method of claim 4, wherein the neural network model comprises a compressed neural network model stored locally on the network microphone device.
6. The method of claim 1, further comprising, after transmitting the voice utterance, receiving, via the network microphone device, a selection of media content related to the voice utterance.
7. The method of claim 1, wherein the plurality of wake-word engines comprises: the first wake-word engine; and a second wake-word engine configured to perform a local function of the network microphone device.
All other claims are similar to the above claims.
Claim of application Claims of USPN 11790911
1. (New) A network microphone device comprising: one or more processors; one or more microphones; and data storage having instructions stored thereon that, when executed by the one or more processors, cause the network microphone device to perform operations comprising:
capturing sound data via the one or more microphones;
processing the sound data using a first wake word detection algorithm to identify a candidate wake word in the sound data;
after initiating the extraction, analyzing the sound data using a second wake word detection algorithm to verify presence of the candidate wake word, wherein the second wake word detection algorithm has greater accuracy than the first wake word detection algorithm; and
in response to identifying the candidate wake word, initiating extraction of detected-sound data from a buffer;
ceasing the extraction of the detected-sound data from the buffer in response to determining that the second wake word detection algorithm failed to verify the presence of the candidate wake word.
2. (New) The network microphone device of claim 1, wherein initiating extraction of detected-sound data from the buffer comprises beginning to package the detected-sound data according to a format for transmission to a voice assistant service.
3. (New) The network microphone device of claim 1, wherein the operations further comprise outputting an alert indicating detection of the candidate wake word before analyzing the sound data using the second wake word detection algorithm.
4. (New) The network microphone device of claim 1, wherein the operations further comprise selecting the second wake word detection algorithm from among a plurality of wake word detection algorithms based on the identified candidate wake word.
5. (New) The network microphone device of claim 1, wherein processing the sound data using the first wake word detection algorithm comprises applying a compressed neural network model to the sound data to identify the candidate wake word.
6. (New) The network microphone device of claim 5, wherein the compressed neural network model comprises a soft weight-shared neural network model stored in compressed sparse row format.
7. (New) The network microphone device of claim 1, wherein analyzing the sound data using the second wake word detection algorithm comprises activating a wake word engine from a low-power state to process the sound data while maintaining other wake word engines in the low-power state.
All other claims are similar to these claims.
1. A method comprising:
capturing sound data via a network microphone device;
identifying, using a keyword spotting algorithm, a candidate wake word in the sound data;
based on identification of the candidate wake word in the sound data via the keyword spotting algorithm, selecting a first wake-word detection algorithm from among a plurality of wake-word detection algorithms stored on the network microphone device, wherein the first wake-word detection algorithm is associated with a first voice assistant service and another of the plurality of wake-word detection algorithms is associated with a second voice assistant service different from the first; after selecting the first wake-word detection algorithm, and without using another of the plurality of wake-word detection algorithms, using the first wake-word detection algorithm to analyze the sound data to confirm detection of the candidate wake word identified via the keyword spotting algorithm, wherein the first wake-word detection algorithm is configured to determine whether the candidate wake word is present in the sound data with a higher accuracy than the keyword spotting algorithm; and in response to confirming the detection of the candidate wake word, transmitting a voice utterance of the sound data to one or more remote computing devices associated with the first voice assistant service.
The patent fails to explicitly disclose these limitations. However, Hoffsmeister teaches these limitations (see rejection below).
Claim 1 above teaches dependent claim 2 of the application.
The patent fails to explicitly disclose these limitations. However, Vanderschaegen teaches these limitations (see rejection below).
Claim 1 above teaches dependent claim 4 of the application.
The patent fails to explicitly disclose these limitations. However, Ullrich teaches these limitations (see rejection below).
The patent fails to explicitly disclose these limitations. However, Ullrich teaches these limitations (see rejection below).
Claim 1 above teaches claim 7 of the application.
2. The method of claim 1, wherein analyzing the sound data to confirm detection of the candidate wake word using the using the first wake-word detection algorithm, and without using another of the wake-word detection algorithms comprises: activating a first wake-word engine to process the sound data using the first wake-word detection algorithm while a second wake-word engine configured to process the sound data using another of the wake-word detection algorithms is in an inactive state.
3. The method of claim 1, wherein identifying the candidate wake word comprises determining a probability that the candidate wake word is present in the sound data.
4. The method of claim 1, wherein the first wake-word detection algorithm is associated with the candidate wake word, and wherein another of the plurality of wake-word detection algorithms is associated with one or more additional wake words.
5. The method of claim 1, wherein identifying the candidate wake word comprises applying a neural network model to the sound data.
6. The method of claim 5, wherein the neural network model comprises a compressed neural network model stored locally on the network microphone device.
7. The method of claim 1, further comprising, after transmitting the voice utterance, receiving, via the network microphone device, a selection of media content related to the voice utterance.
8. The method of claim 1, wherein the plurality of wake-word detection algorithms comprises: the first wake-word detection algorithm; and a second wake-word detection algorithm configured to perform a local function of the network microphone device.
All other claims are similar to these claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 8-9, and 15-16 are rejected under 35 U.S.C. 102(a)(2) as being (a)(2) by Hoffmeister (USPN 10388274).
Regarding claims 1, 8, and 15, Hoffmeister discloses a network microphone device, method, and non-transitory CRM comprising: one or more processors; one or more microphones; and data storage having instructions stored thereon that, when executed by the one or more processors, cause the network microphone device to perform operations (figure 18, microphones, memory, and processor) comprising:
capturing sound data via the one or more microphones (figure 2, device 110 capturing input audio 11);
processing the sound data using a first wake word detection algorithm to identify a candidate wake word in the sound data (figure 2, wakeword detection module 220);
in response to identifying the candidate wake word, initiating extraction of detected-sound data from a buffer (col. 5, lines 1-26, “Following detection of a wakeword (which may be a word sequence rather than a single word), the device sends audio data 111 corresponding to the utterance, to a server 120 that includes an ASR module 250”);
after initiating the extraction, analyzing the sound data using a second wake word detection algorithm to verify presence of the candidate wake word, wherein the second wake word detection algorithm has greater accuracy than the first wake word detection algorithm (col. 16, lines 49-56, “if a wakeword is determined to have been detected, ASR output verification may be performed to confirm wakeword detection. If the wakeword is not confirmed (i.e., the wakeword detection is determined to have a confidence below a threshold), a device may not wake. Whereas if the wakeword is confirmed (i.e., the wakeword detection is determined to have a confidence above the threshold), the device may wake”; col. 31, line 67 to col. 32, line 1, simple keyword spotting algorithm at client device; col. 21, lines 11-35, server side employs a more powerful and accurate algorithm to detect wakeword by employing ASR algorithm); and
ceasing the extraction of the detected-sound data from the buffer in response to determining that the second wake word detection algorithm failed to verify the presence of the candidate wake word (col. 21, lines 11-35, “If the wakeword is not confirmed, the server 120 may send a message to the local device 110 to discontinue sending further audio data or may simply discard further audio data received from the local device 110.”).
Regarding claims 2, 9, and 16, Hoffmeister further discloses wherein initiating extraction of detected-sound data from the buffer comprises beginning to package the detected-sound data according to a format for transmission to a voice assistant service (col. 5, lines 12-17 and col. 7, lines 1-27; packaging by processing the audio signal into a particular type of data before sending to the server).
Claim Rejections - 35 USC § 103
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.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hoffmeister in view of Vanderschaegen et al. (USPN 10978062, hereinafter Vanderschaegen).
Regarding claims 3, 10, and 17, Hoffmeister fails to explicitly disclose, however, Vanderschaegen teaches wherein the operations further comprise outputting an alert indicating detection of the candidate wake word before analyzing the sound data using the second wake word detection algorithm (col. 16, lines 36-49, “In response to the detection of the wakeword, the wakeword service notifies the SIM that a new utterance is in the process of being detected”).
Since Hoffmeister and Vanderschaegen are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of providing notification when a wakeword is detected in the received signal. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Claims 5-6, 12-13, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hoffmeister in view of Ullrich et al. (“Soft Weight-Sharing for Neural Network Compression”, ICLR, 5/9/2017)
Regarding claims 5-6, 12-13, and 19-20, Hoffmeister further discloses wherein processing the sound data using the first wake word detection algorithm comprises applying a neural network model to the sound data to identify the candidate wake word (col. 5, line 59 to col. 6, line 20, the use of NN to detect wakeword). Hoffmeister fails to explicitly disclose that the neural network is a compressed neural network and wherein the compressed neural network model comprises a soft weight-shared neural network model stored in compressed sparse row format. However, Ullrich teaches that the neural network is a compressed neural network (section 4, compressed neural network) and wherein the compressed neural network model comprises a soft weight-shared neural network model stored in compressed sparse row format (sections 4.1-4.3, “neural networks with soft weight-sharing and factorized Dirac posteriors”; appendix A, “store the weights in regular compressed sparse-row (CSR)format”).
Since Hoffmeister and Ullrich are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of utilizing a soft weight-shared neural network model stored in compressed sparse row format. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Allowable Subject Matter
Claims 4, 7, 11, 14, and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lockhart et al. (USPN 10186265) teach a multi-layer keyword detection method that is considered pertinent to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUYEN X VO whose telephone number is (571)272-7631. The examiner can normally be reached M-F, 8-4.
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/HUYEN X VO/Primary Examiner, Art Unit 2656
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