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 .
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Sheen (US 2016/0073210 A1).
Regarding claim 1, Sheen anticipates:
“An audio device, comprising at least one acoustic transducer and a controller” (see Sheen, ¶ 0036 and 0038, figure 1, units 104 and 126, figure 2, units 200, 202, and 212), “wherein the controller is configured to:
render microphone calibration audio via the at least one acoustic transducer” by teaching an audio signal (e.g., Sheen’s third audio signal that is a test or measurement signal) that is played by a playback device in order to calibrate a microphone of a network device (see Sheen, ¶ 0137, 0141, and 0144-0147 and figure 9, step 902);
“wirelessly receive a microphone calibration data set corresponding to the microphone calibration audio” by teaching that a computing device, or server, receives the first audio signal, which corresponds to the test signal detected by the microphone (see Sheen, ¶ 0144, 0156, and 0166), the server identifies a calibration algorithm for the microphone and the playback device receives the identified calibration algorithm (see Sheen, ¶ 0156, 0158, and 0168), and Sheen teaches that the server, network device comprising said microphone, and the playback device are wirelessly networked (see Sheen, ¶ 0046 and figure 2, units 214 and 216) and;
“render sound optimization audio via the at least one acoustic transducer” by teaching that an audio signal is played back for a calibrated microphone device in order to identify an audio processing algorithm to apply to the audio signal (see Sheen, ¶ 0111-0112, 0114-0116, and 0169, figure 7, step 702, and figure 11, step 1108);
“wirelessly receive a sound optimization data set corresponding to the sound optimization audio at a first listening location” by teaching that the playback device receives the data captured by the calibrated microphone (see Sheen, ¶ 0046 and 0118-0120, figure 2, units 214 and 216, and figure 7, step 704);
“generate an equalization parameter based on the sound optimization data set and the microphone calibration data set” by teaching that the device generates a desired frequency response with an identified audio processing algorithm (see Sheen, ¶ 0097-0100 and 0120, figure 7, step 706, and figure 11, step 1108);
“generate an optimized output signal based on an output audio data set and the equalization parameter” by teaching that the identified audio processing algorithm is applied to the audio for output (see Sheen, ¶ 0121, and figure 7, step 708); and
“render optimized output audio via the at least one acoustic transducer based on the optimized output signal” by teaching that the processed audio is output for a user to hear (see Sheen, ¶ 0121, and figure 7, step 708).
Regarding claim 2, see the preceding rejection with respect to claim 1 above. Sheen anticipates the “audio device of claim 1, further comprising a reference microphone configured to capture a reference audio data set corresponding to the microphone calibration audio, wherein the equalization parameter is further based on the reference audio data set” by teaching that the playback device has a microphone with a known characteristic (see Sheen, ¶ 0038, 0135, 0138, and 0141, figure 2, units 200 and 220, figure 9, step 904, and figure 10, units 1006 and 1008).
Regarding claim 3, see the preceding rejection with respect to claim 1 above. Sheen anticipates the “audio device of claim 1, wherein the microphone calibration data set and/or the sound optimization data set is received via Wi-Fi” by teaching that the server, network device comprising said microphone, and the playback device are wirelessly networked (see Sheen, ¶ 0046 and figure 2, units 214 and 216), where the server identifies a calibration algorithm for the microphone and the playback device receives the identified calibration algorithm (see Sheen, ¶ 0156, 0158, and 0168).
Regarding claim 4, see the preceding rejection with respect to claim 1 above. Sheen anticipates the “audio device of claim 1, wherein the microphone calibration data set corresponds to audio captured proximate to the audio device” by teaching that the audio device (i.e., the network device) is within a predetermined distance of the playback device in order to capture audio for the calibration (see Sheen, ¶ 0141 and 0166, figure 9, step 902, and figure 11, step 1102).
Regarding claim 5, see the preceding rejection with respect to claim 1 above. Sheen anticipates the “audio device of claim 1, wherein the audio device is a soundbar” by teaching that the playback device is a SONOS PLAYBAR, or a soundbar for use with a television (see Sheen, ¶ 0036, 0049, and 0054, figure 1, unit 104, and figure 2, unit 200).
Regarding claim 6, see the preceding rejection with respect to claim 1 above. Sheen anticipates the “audio device of claim 1, wherein the microphone calibration data set is wirelessly received from a mobile device” by teaching that the server, network device comprising said microphone, and the playback device are wirelessly networked (see Sheen, ¶ 0046 and figure 2, units 214 and 216), where the server identifies a calibration algorithm for the microphone and the playback device receives the identified calibration algorithm that has been recorded by the mobile device (i.e., the network device) (see Sheen, ¶ 0156, 0158, and 0168).
Regarding claim 7, see the preceding rejection with respect to claim 1 above. Sheen anticipates the “audio device of claim 1, wherein the sound optimization data set is wirelessly received from a mobile device” by teaching that the server, network device comprising said microphone, and the playback device are wirelessly networked (see Sheen, ¶ 0046 and figure 2, units 214 and 216), where the playback device receives the data captured by the calibrated microphone of the mobile device (i.e., the network device) (see Sheen, ¶ 0046 and 0118-0120, figure 2, units 214 and 216, and figure 7, step 704).
Regarding claim 8, see the preceding rejection with respect to claim 1 above. Sheen anticipates the “audio device of claim 1, wherein the controller is further configured to:
render second sound optimization audio via the at least one acoustic transducer” by teaching another audio signal played by a second playback device (see Sheen, ¶ 0109-0110); and
“wirelessly receive a second sound optimization data set corresponding to the second sound optimization audio at a second listening location, wherein the equalization parameter is further based on the second sound optimization data set” by teaching that the playback device wirelessly receives more data captured by the calibrated microphone to apply a second audio processing algorithm to the second playback device (see Sheen, ¶ 0046 and 0109-0113, figure 2, units 214 and 216).
Regarding claim 9, see the preceding rejection with respect to claim 1 above. Sheen anticipates the “audio device of claim 1, wherein the microphone calibration audio and/or the sound optimization audio are within a frequency range of 20 Hz to 20 kHz” by teaching that the playback audio and/or the microphone audio comprises a typical human hearing range (see Sheen, ¶ 0044 and 0057).
Regarding claim 10, Sheen anticipates:
“A method for optimizing output audio rendered by an audio device, comprising:
rendering microphone calibration audio via at least one acoustic transducer of the audio device” by teaching an audio signal (e.g., Sheen’s third audio signal that is a test or measurement signal) that is played by a playback device in order to calibrate a microphone of a network device (see Sheen, ¶ 0137, 0141, and 0144-0147 and figure 9, step 902);
“wirelessly receiving, via the audio device, a microphone calibration data set corresponding to the microphone calibration audio” by teaching that a computing device, or server, receives the first audio signal, which corresponds to the test signal detected by the microphone (see Sheen, ¶ 0144, 0156, and 0166), the server identifies a calibration algorithm for the microphone and the playback device receives the identified calibration algorithm (see Sheen, ¶ 0156, 0158, and 0168), and Sheen teaches that the server, network device comprising said microphone, and the playback device are wirelessly networked (see Sheen, ¶ 0046 and figure 2, units 214 and 216);
“rendering sound optimization audio via the at least one acoustic transducer of the audio device” by teaching that an audio signal is played back for a calibrated microphone device in order to identify an audio processing algorithm to apply to the audio signal (see Sheen, ¶ 0111-0112, 0114-0116, and 0169, figure 7, step 702, and figure 11, step 1108);
“wirelessly receiving, via the audio device, a sound optimization data set corresponding to the sound optimization audio at a first listening location” by teaching that the playback device receives the data captured by the calibrated microphone (see Sheen, ¶ 0046 and 0118-0120, figure 2, units 214 and 216, and figure 7, step 704);
“generating, via a controller of the audio device, an equalization parameter based on the sound optimization data set and the microphone calibration data set” by teaching that the device generates a desired frequency response with an identified audio processing algorithm (see Sheen, ¶ 0097-0100 and 0120, figure 7, step 706, and figure 11, step 1108);
“generating, via the controller of the audio device, an optimized output signal based on an output audio data set and the equalization parameter” by teaching that the identified audio processing algorithm is applied to the audio for output (see Sheen, ¶ 0121, and figure 7, step 708); and
“rendering the optimized output audio via the at least one acoustic transducer of the audio device based on the optimized output signal” by teaching that the processed audio is output for a user to hear (see Sheen, ¶ 0121, and figure 7, step 708).
Regarding claim 11, see the preceding rejection with respect to claim 10 above. Sheen anticipates the “method of claim 10, wherein the microphone calibration data set is captured by a mobile device arranged proximate to the audio device” by teaching that the audio device (i.e., the network device) is within a predetermined distance of the playback device in order to capture audio for the calibration (see Sheen, ¶ 0141 and 0166, figure 9, step 902, and figure 11, step 1102).
Regarding claim 12, see the preceding rejection with respect to claim 11 above. Sheen anticipates the “method of claim 11, wherein the mobile device is arranged on top of the audio device” by teaching that the mobile device (i.e., the network device) is on top of the playback device (see Sheen, figure 10, units 1006 and 1010).
Regarding claim 13, see the preceding rejection with respect to claim 10 above. Sheen anticipates the “method of claim 10, further comprising:
capturing, via a reference microphone of the audio device, a reference audio data set corresponding to the microphone calibration audio, wherein the equalization parameter is further based on the reference audio data set” by teaching that the playback device has a microphone with a known characteristic (see Sheen, ¶ 0038, 0135, 0138, and 0141, figure 2, units 200 and 220, figure 9, step 904, and figure 10, units 1006 and 1008).
Regarding claim 14, see the preceding rejection with respect to claim 10 above. Sheen anticipates the “method of claim 10, wherein the microphone calibration data set and/or the sound optimization data set is received via Wi-Fi” by teaching that the server, network device comprising said microphone, and the playback device are wirelessly networked (see Sheen, ¶ 0046 and figure 2, units 214 and 216), where the server identifies a calibration algorithm for the microphone and the playback device receives the identified calibration algorithm (see Sheen, ¶ 0156, 0158, and 0168).
Regarding claim 15, see the preceding rejection with respect to claim 10 above. Sheen anticipates the “method of claim 10, wherein the audio device is a soundbar” by teaching that the playback device is a SONOS PLAYBAR, or a soundbar for use with a television (see Sheen, ¶ 0036, 0049, and 0054, figure 1, unit 104, and figure 2, unit 200).
Regarding claim 16, see the preceding rejection with respect to claim 10 above. Sheen anticipates the “method of claim 10, wherein the sound optimization data set is captured by a mobile device positioned at the first listening location” by teaching that the audio device (i.e., the network device) is within a predetermined distance of the playback device in order to capture audio for the calibration (see Sheen, ¶ 0141 and 0166, figure 9, step 902, and figure 11, step 1102).
Regarding claim 17, see the preceding rejection with respect to claim 10 above. Sheen anticipates the “method of claim 10, wherein the sound optimization data set is wirelessly received from a mobile device” by teaching that the server, network device comprising said microphone, and the playback device are wirelessly networked (see Sheen, ¶ 0046 and figure 2, units 214 and 216), where the playback device receives the data captured by the calibrated microphone of the mobile device (i.e., the network device) (see Sheen, ¶ 0046 and 0118-0120, figure 2, units 214 and 216, and figure 7, step 704).
Regarding claim 18, see the preceding rejection with respect to claim 10 above. Sheen anticipates the “method of claim 10, further comprising:
rendering second sound optimization audio via the at least one acoustic transducer of the audio device” by teaching another audio signal played by a second playback device (see Sheen, ¶ 0109-0110); and
“wirelessly receiving, via the audio device, a second sound optimization data set corresponding to the second sound optimization audio at a second listening location, wherein the equalization parameter is further based on the second sound optimization data set” by teaching that the playback device wirelessly receives more data captured by the calibrated microphone to apply a second audio processing algorithm to the second playback device (see Sheen, ¶ 0046 and 0109-0113, figure 2, units 214 and 216).
Regarding claim 19, see the preceding rejection with respect to claim 18 above. Sheen anticipates the “method of claim 18, wherein the second sound optimization data set is captured by a mobile device positioned at the second listening location” by teaching that the mobile device detects audio at two different locations for two different playback devices (see Sheen, ¶ 0081, 0097-0100, and 0108-0110, and figure 6, units 602, 604, and 606).
Regarding claim 20, see the preceding rejection with respect to claim 10 above. Sheen anticipates the “method of claim 10, wherein the microphone calibration audio and/or the sound optimization audio are within a frequency range of 20 Hz to 20 kHz” by teaching that the playback audio and/or the microphone audio comprises a typical human hearing range (see Sheen, ¶ 0044 and 0057).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Davis et al. (US 2023/0061896 A1 and hereafter Davis), teaches a method and apparatus for location-based audio signal compensation (see Davis, abstract, figures 1-11, and ¶ 0003-0006 and 0031).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Daniel R Sellers whose telephone number is (571)272-7528. The examiner can normally be reached Mon - Fri 10:00-4:00.
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/Daniel R Sellers/ Primary Examiner, Art Unit 2694
