DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the response to this office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Claim Objections
Claims 1-13 are objected to because of the following informalities:
Claim 1 recited “Apparatus for …” which should be -- An apparatusClaims 2-12 are objected due to the dependencies to claim 1.
Claim 13 recited “Method for …” which should be – A method
Claim 2 further recited “Apparatus according to claim …” which should be – The apparatus Claims 3-4 are objected due to the dependencies to claim 2.
Claims 3-12 are further objected for the at least similar reason as described in claim 2 above since claims 3-12 recited similar deficient features as recited in claim 2. Claims 10, 12 are objected due to the dependency to claims 9, 11, respectively.
Appropriate correction is required.
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.
Claims 1, 5, 8, 11-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Smyth (US 20060045294 A1).
Claim 1. Smyth teaches an apparatus (title and abstract, ln 1-11, a headphone virtualizer processor 123 in figs. 26-28) for determining one or more room acoustics parameters (PRIRs determined with respect to user’s head orientations in figs. 3-5),
wherein the apparatus is configured to acquire microphone data comprising one or more microphone signals (microphone signals 86a, 86b, para 92),
wherein the apparatus is configured to acquire tracking data concerning a position and/or orientation of a user (head movements down to fractions of a millimeter are detected through separating the mixed MLS 98 output and pilot tone 134 in microphone signals 86a/86b via HPF and LPF 136, 135 and analyzing their relative phase or absolute phase in fig. 24, para 198-199),
wherein the apparatus is configured to determine the one or more room acoustics parameters (impulse responses or PRIRs are determined, para 199 and 92, 93, 94 as C PRIR, L PRIR, and R PRIR are determined in figs. 3-5, reverberation characteristics of the environment surrounding the measurement space, para 88, quality of the acquired impulse responses related to the background noise, recording signal chain, para 88) depending on the microphone data (depending on microphone signals 86a/86b in figs. 3-5) and depending on the tracking data (head orientations in figs. 3-5, and through pilot tone and MLS approach in fig. 24 and discussed above).
Claim 13 has been analyzed and rejected according to claim 1 above.
Claim 14 has been analyzed and rejected according to claims 1, 13 above and Smyth further teaches a non-transitory digital storage medium (boot ROM 126 and RAM 125 in fig. 41) having a computer program stored thereon (program implemented by DSP chip, para 269) to perform the method of claim 13 (for performing the PRIR measurements, para 269).
Claim 5: Smyth further teaches, according to claim 1 above, wherein the one or more room acoustics parameters comprise a reverberation time (reverberation time maximized to 100s of milliseconds, para 134).
Claim 8: Smyth further teaches, according to claim 1 above, wherein the tracking data comprises a pitch coordinate, a yaw coordinate and a roll coordinate to label the orientation of the user (head movement measured at yaw, pitch, and roll in three degrees, para 99, and for generating updated PRIR, para 148, 334-336).
Claim 11: Smyth further teaches, according to claim 1 above, wherein the apparatus comprises a microphone arrangement of several microphones (ear canal 209 of human subject 79 in figs. 20-21, including the left ear and the right ear in figs. 3-5) to record the several microphone signals (microphones in the user’s left and right ears in figs. 3-5).
Claim 12: Smyth further teaches, according to claim 11 above, wherein the microphone arrangement is configured to be worn at a user's body (microphones arranged in the user’s canal in fig. 20, para 86).
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 of this title, 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 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Smyth (above) and in view of reference Bharitkar et al. (US 20050094821 A1, hereinafter Bharitkar).
Claim 2: Smyth further teaches, according to claim 1 above, wherein the apparatus is configured to employ a module to determine the one or more room acoustics parameters depending on the microphone data and depending on the tracking data (the discussion in claim 1 above, module 95 including an MLS 98, a processor 97, etc., in figs. 3-5, para 93), except that the module is machine learning.
Bharitkar teaches an analogous field of endeavor by disclosing an apparatus for determining one or more room acoustics parameters (title and abstract, ln 1-8 and a multiple-listener equalization system in fig. 4) and wherein determining the one or more room acoustics parameters (a weighted average of the room acoustical responses as a general response and determined by spatial average of individual room responses of listeners in the room, para 13, room acoustical correction filter from the general response, para 13, a cluster containing room acoustical response having a centroid and the general response from the at least one centroid, para 27) depending on the microphone data and depending on the tracking data (the individual room acoustic responses recorded upon listener positions, para 62) and wherein machine learning is employed to determine the one or more room acoustics parameters depending on the microphone data and depending on the tracking data (the general response is determined based on adaptive learning method including neural-nets, recursive least squares, LMS, etc., or a combination of thereof, para 69, and also neural network based algorithm, para 85) for benefits of improving the performance in the estimation of the room acoustic parameters (by representing room acoustic responses not just for single listener, but also simultaneously multiple listeners at different positions in figs. 1, 5 and in a cost effective manner for real-time applications, para 90).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the machine learning employed to determine the one or more room acoustics parameters depending on the microphone data and depending on the tracking data, as taught by Bharitkar, to determine the one or more room acoustics parameters depending on the microphone data and depending on the tracking data by using the module in the apparatus, as taught by Smyth, for the benefits discussed above.
Claim 3: the combination of Smyth and Bharitkar further teaches, according to claim 2 above, wherein the apparatus is configured to employ machine learning in that the apparatus is configured to employ a neural network (Bharitkar, the neural network applied in generating corrected room acoustic response and discussed in claim 2 above).
Claim 4: the combination of Smyth and Bharitkar further teaches, according to claim 2 above, wherein the apparatus is configured to employ the remote server processing (Smyth, remote server 226 in fig. 48) and machine learning (Bharitkar, adaptive learning method including neural-nets, recursive least squares, etc., para 69, and also neural network based algorithm, para 85), except further teaches wherein the apparatus is configured to employ cloud-based processing for the machine learning.
An Official Notice is taken that a cloud-based processing for machine learning is notoriously well-known in the art for benefits of improving the system performance by utilizing powerful cloud computation capabilities with rich resources assessable in the clouds in a cost-effective manner, e.g., creating a specific scalability and collaboration for a specific application.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the cloud-based processing for the machine learning, as taught by the well-known in the art, to the remote server processing and the machine learning in the apparatus, as taught by the combination of Smyth and Bharitkar, for the benefits discussed above.
Claims 6-7, 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Smyth (above) and in view of reference Leppanen et al. (US 20190180731 A1, hereinafter Leppanen).
Claim 6: Smyth teaches, according to claim 1 above, the one or more room acoustics parameters (the discussed in claim 1 above, including impulse responses or PRIRs, reverberation characteristics of the environment surrounding the measurement space, recording signal chain, etc., para 88), except explicitly teaching wherein the one or more room acoustics parameters comprise a direct-to-reverberant ratio.
Leppanen teaches an analogous field of endeavor by disclosing an apparatus for determining one or more room acoustics parameters (title and abstract, ln 1-14 and the method implemented in a reality system in fig. 12) and wherein one or more room acoustics parameters are disclosed (determined by dry lavalier signal 230 received by close-up microphones in fig. 2 and listener position, para 46) to comprise a direct-to-reverberant ratio (direct-to-wet ratio applied to the dry lavalier signal, para 46 and as one of room acoustics parameters and related to the spatial distance, para 59 and the spatial distance of the sounds and referred to relative to the user or listener’s position, para 82) for benefits of improving the accuracy of the acoustic scene realism (by applying scene geometry and change to adjust signal for the created experiences, para 50 and by providing accuracy evaluation of calculated difference of the geometries, para 70 and in an enhanced 6-DoF volumetric virtual reality, para 2, 49, 54).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the direct-to-reverberant ratio that has been included in the one or more rooms acoustics parameters, as taught by Leppanen, to the one or more room acoustics parameters in the apparatus, as taught by Smyth, for the benefits discussed above.
Claim 7: Smyth further teaches, according to claim 1 above, wherein the tracking data comprises a pitch coordinate, a yaw coordinate and a roll coordinate to label the position of the user (head movement measured at yaw, pitch, and roll in three degrees, para 99, and for generating updated PRIR, para 148, 334-336), except an x-coordinate, a y-coordinate, and a z-coordinate to label the position of the user.
Leppanen teaches an analogous field of endeavor by disclosing an apparatus for determining one or more room acoustics parameters (title and abstract, ln 1-14 and the method implemented in a reality system in fig. 12) and also teaches tracking data comprises not just pitch coordinate, the yaw coordinate and the roll coordinate to label the position of the user (freely move in a Euclidean space x, y, and z and rotate their head in Yaw, pitch, and roll, para 2), but also a x-coordinate, a y-coordinate, and a z-coordinate to able the position of the user as well (a listener position 1110 and source position 1106 in Cartesian coordinates x, y, z, para 82) for the benefits discussed in claim 6 above.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the tracking data comprising the x-coordinate, the y-coordinate, and the z-coordinate to label the position of the user, as taught by Leppanen, to the tracking data in the apparatus, as taught by Smyth, for the benefits discussed above.
Claim 9: Smyth further teaches, according to claim 1 above, wherein the apparatus is configured to transform the one or more microphone signals (analog to digital transform for 2 channels by ADC 99 in fig. 41 and para 269), except
wherein the apparatus is configured to transform the one or more microphone signals from a time domain into a frequency domain,
wherein the apparatus is configured to extract one or more features of the one or more microphone signals in the frequency domain, and
wherein the apparatus is configured to determine the one or more room acoustics parameters depending on the one or more features.
Leppanen further teaches that the apparatus (the discussion in claim 6 above) and
wherein the apparatus is configured to transform the one or more microphone signals (microphone signals from sound source 1, and source 2 in fig. 1) from a time domain into a frequency domain (via STFT 120 in fig. 1),
wherein the apparatus is configured to extract one or more features of the one or more microphone signals in the frequency domain (time-aligned room impulse responses for calculating a mean squared error and differently weighted parts of RIR based on importance after the STFT in fig. 10, para 76 or wet projection 140 for creating a wet version of the external microphone signal, para 29, or time-frequency mask 160 used for better matching the wet signal to the wet far-field microphone signals in fig. 1, para 30), and
wherein the apparatus is configured to determine the one or more room acoustics parameters depending on the one or more features (one of RIRs 240 and gRIRs 710 is determined by a selection based on the comparison result, para 98 or wet signal is generated based on the TF mask to better match to the far-field microphone signals, para 30) for the benefits of improving a performance of generating room impulse response (adaptively relied on positions in the obtained geometry over time for a more immersive experience, para 102) and an efficiency (automatically obtaining room impulse response for different parts of a room with high efficiency compared to exhaustive RIR measurements at different portions of the room, para 100).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied wherein the apparatus is configured to transform the one or more microphone signals from the time domain into the frequency domain, to extract the one or more features of the one or more microphone signals in the frequency domain, and to determine the one or more room acoustics parameters depending on the one or more features, as taught by as taught by Leppanen, to transform the one or more microphone signals by the apparatus, as taught by Smyth, for the benefits discussed above.
Claim 10: the combination of Smyth and Leppanen further teaches, according to claim 9 above, wherein the apparatus is configured to employ a server processing (Smyth, a remote conference server 226 in a teleconference scenario in fig. 48) for extracting one or more features (Smyth, time-aligned impulses represented by time-aligned PRIR based on the head movement signal for PRIR interpolation 236 at the server 226 in fig. 48, the time-aligned RIR is applied for generating interpolated RIRs, para 126, i.e., via the PRIR interpolation 236 in fig. 48, para 265 and Leppanen, the time-aligned room impulse responses, for calculating the mean squared error and differently weighted parts of RIR based on importance after the STFT in fig. 10, para 76 or wet projection 140 for creating a wet version of the external microphone signal, para 29, or time-frequency mask 160 used for better matching the wet signal to the wet far-field microphone signals in fig. 1, para 30), except wherein a server processing is cloud-based processing.
An Official Notice is taken that a server processing being located remotely in cloud-based processing is notoriously well-known in the art for benefits of improving the system performance by utilizing powerful cloud computation capabilities with rich resources assessable in the clouds in a cost-effective manner, e.g., creating a specific scalability and collaboration for a specific application.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied wherein the server processing is the cloud-based processing, as taught by the well-known in the art, to the remote server processing in the apparatus, as taught by the combination of Smyth and Leppanens, for the benefits discussed above.
Conclusion
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/LESHUI ZHANG/
Primary Examiner,
Art Unit 2695