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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 21-24, 27-33, and 37-38 are rejected under 35 U.S.C. 103 as being unpatentable over Meacham et al. (hereinafter Meacham) (US 20200380979 A1) in view of Grokop et al. (hereinafter Grokop) (US 20130245986 A1) and Vallabhan et al. (hereinafter Vallabhan) (US 9838784 B2) (see attached copy for paragraph numbers).
Regarding claim 21, Meacham discloses:
A method of performing environment-aware processing of audio data for a mobile device, comprising (Meacham, Title: "CONTEXT AWARE HEARING OPTIMIZATION ENGINE" (Meacham's system performs audio optimization based on information describing user's current environment and device context, thus, teaching environment aware audio processing of a mobile device.)):
and
performing audio processing of the audio data based on the determined scene information (Meacham, Claim 1: "applying one or more audio processing parameters", P[0026]: "selecting a set of audio processing parameters that change the manner in which a user experiences his or her ambient sound environment", P[0054]: "Audio processor 410 is configured to process the ambient audio stream, and optionally the secondary audio stream, in accordance with a processing parameter set." (Meacham determines an action based on context-aware parameters indicating the user's location or situation and applies corresponding filtering, equalization, compression, limiting, echo cancellation, or noise-reduction parameters to an audio stream. The audio processing is therefore performed based on determined scene/context information))
Meacham does not explicitly disclose:
obtaining non-acoustic sensor information of the mobile device;
determining scene information comprising a scene classification indicative of an environment of the mobile device based on the non-acoustic sensor information; and
performing audio processing of the audio data based on the determined scene information when a scene transition between any two of a plurality of scene classifications is detected and further adapted in a transition stage between the two scene classifications according to the specific type of scene transition detected.
However, Grokop discloses:
obtaining non-acoustic sensor information of the mobile device (Grokop, P[0022]: "motion detectors", "raw motion data");
determining scene information comprising a scene classification indicative of an environment of the mobile device based on the non-acoustic sensor information (Grokop, P[0022]: "motion state classifier module" (Grokop processes mobile device motion sensor data to classify the device into states including walking, automobile-stop, and automobile movement states, thus, determining a scene classification indicative of the device's mobility environment from non-acoustic sensor information)); and
when a scene transition between any two of a plurality of scene classifications is detected (Grokop, P[0039]: "probability of transitioning to a different state" (Grokop's classifier distinguishes multiple mobile-device states and determines transitions between those states, including transitions between pedestrian, automobile stop, and automobile movement classification.))
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Meacham in view of Grokop. Doing so would have brought the determination of mobile device’s state from non-acoustic sensors and classification of mobility states of Grokop (Grokop, Abstract, P[0022]) with the audio processing based on user’s contextual environment through selecting sets of audio parameters that change the manner in which user experiences their ambient sound environment of Meacham (Meacham, Abstract, Claim 1), thus, predictably using known mobile device context detection techniques to automatically determine the user’s environment and apply context appropriate audio processing.
The combination of Grokop and Meacham does not explicitly disclose:
wherein the audio processing is adapted
and further adapted in a transition stage between the two scene classifications according to the specific type of scene transition detected
However, Vallabhan discloses:
wherein the audio processing is adapted (Vallabhan, "…switching between modes…" (Vallabhan teaches adapting the audio processing by changing processing modes))
and further adapted in a transition stage between the two scene classifications according to the specific type of scene transition detected (Vallabhan, P(62): "different from the attack and release time used in the normal mode." (Vallabhan applies attack and release processing during the handover between modes expressly varies those processing time according to whether the system is transitioning relative to a noisy mode or a normal mode. Thus, the transition state audio processing is further adapted according to particular modes and the type of transition involved, consequently)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Meacham in view of Grokop and Vallabhan. Doing so would have brought the estimation methods of noise levels associated with the determined scene of Vallabhan (Vallabham, Abstract, P(9)) with the determination of mobile device’s state from non-acoustic sensors and classification of mobility states of Grokop (Grokop, Abstract, P[0022]) with the audio processing based on user’s contextual environment through selecting sets of audio parameters that change the manner in which user experiences their ambient sound environment of Meacham (Meacham, Abstract, Claim 1), thus, predictably using known environmental noise analysis to select audio processing parameters appropriate for the detected environment.
Regarding claim 22, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 21.
The combination further discloses:
wherein the non-acoustic sensor information is obtained from one or more non-acoustic sensors of the mobile device (Grokop, P[0022]: "One or more motion detectors" (Grokop obtains raw motion data from one or more motion detectors associated with the mobile device and additionally permits data from GPS and WiFi receivers. Those components are non-acoustic sensors of the mobile device from which claimed sensor information is obtained.)).
Regarding claim 23, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 22.
The combination further discloses:
wherein the one or more non-acoustic sensors comprise at least one of:
an accelerometer, a gyroscope, or a Global Navigation Satellite System, GNSS, receiver (Grokop P[0022]: "accelerometers providing accelerometer data.").
Regarding claim 24, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 21.
The combination further discloses:
wherein the determination of the scene information based on the non-acoustic sensor information involves processing of sensor data in the non-acoustic sensor information (Grokop, P[0026]: "features extracted from accelerometer data" (Grokop extracts features (including acceleration over time, standard deviation, pitch, and rotation) from the non-acoustic accelerometer information and processes those features using statistical models to determine likelihoods for multiple device states. The scene classification is therefore determined by processing sensor data contained in the non-acoustic sensor information)).
Regarding claim 27, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 24.
The combination further discloses:
wherein the processing of sensor data in the non-acoustic sensor information comprises:
determining a preliminary scene classification based on the non-acoustic sensor information (Grokop, P[0023]: "The motion state classifier module 18 utilizes the motion data in combination with an associated motion state machine 16 to infer the motion state (e.g., walk, stand, sit, in automobile and stopped, in automobile and moving, etc.)" (Grokop determines a preliminary classification of the mobile device's mobility scene from non-acoustic motion sensor data.)); and
determining a scene score indicative of the environment based on the preliminary scene classification (Grokop, P[0022]: "the motion state classifier module 18 can output a discrete state and/or a set of probabilities for respective possible states." (the probability for the respective classified mobility states constitute scores indicating the likelihood of each mobility environment)).
Regarding claim 28, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 27.
The combination further discloses:
wherein, before the determination of the scene score, the method further comprises post-processing the determined preliminary scene classification (Grokop, P[0027]: "The likelihoods are then passed to a filtering block 208, which utilizes a latency factor L to stabilize the determined device states" (Grokop post processes the initially determined state likelihoods by filtering them to stabilize the scene classification.));
wherein the post-processing involves identifying a transition between different environments (Grokop, P[0025]: "Module 18 may also determine a change in state based on motion data from motion detector(s) 12 and any additional sensor(s) 14, using the transitions and types of states provided by state machine 16." (the determined change between the classified walk, stationary, and automobile states identifies a transition between different mobility environments)); and
wherein the scene score is determined based on the post-processed preliminary scene classification (Grokop, P[0027]: "weighted averages and/or other algorithms can be used to process vector likelihood data into a filtered set of states and/or state likelihoods" (the resulting filtered state likelihoods are scene scores determined from the post processed preliminary state likehoods)).
Regarding claim 29, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 28.
The combination further discloses:
wherein the audio processing involves attack and/or release smoothing of the audio data based on the transition (Vallabhan, P(62): "By appropriately handling the attack and release time, a smooth handover can be achieved. It should be noted, however, that the attack and release time in the noisy mode are different from the attack and release time used in the normal mode." (Vallabhan smooths the audio processing handover between operational modes by controlling attack and release time. Attack and release time used in noisy mode is different from that in normal mode thus applying the smoothing according to the detected mode transition.)).
Regarding claim 30, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 21.
The combination further discloses:
wherein the audio processing is further based on a transition of the scene information (Vallabhan, P(62): "By appropriately handling the attack and release time, a smooth handover can be achieved. It should be noted, however, that the attack and release time in the noisy mode are different from the attack and release time used in the normal mode." (Vallabhan performs the audio processing differently depending on the detected transition between operational modes (based on detected scene transition))) from first scene information indicative of a first environment of the mobile device to second scene information indicative of a second environment of the mobile device that is different from the first environment (Grokop, P[0022]: "infer the motion state (e.g., walk, stand, sit, in automobile and stopped, in automobile and moving, etc.) of the mobile device." (Grokop classifies different environments/states of the mobile device and detects transitions between those different classified environments.
Regarding claim 31, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 21.
The combination further discloses:
wherein the scene information comprising a scene classification is indicative of one of:
an indoor environment, an outdoor environment, a transportation environment, or a flight environment (Meacham, P[0075]: "Examples of situations include “airplane cabin,” “dinner,” “concert,” “subway,” “urban street,” “siren,” and “crying baby.").
Regarding claim 32, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 21.
The combination further discloses:
The method according to claim 21, wherein the audio processing involves dialog enhancement (Meacham, P[0030]: "the personal computing device can turn on beam forming so that when the user turns his or her ear to the speaker, the user can selectively hear the speaker…This allows the user to converse with the speaker" (Meacham enhances speaker's dialogue relative to surrounding sounds so that user can selectively hear and converse with the speaker)).
Regarding claim 33, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 32.
The combination further discloses:
wherein the dialog enhancement comprises:
determining at least one elementary dialog enhancement parameter based on the determined scene information (Meacham, P[0026]: "Based on the ambient audio stream and a combination of the current contextual state, one or more stored local contextual states, and/or one or more stored global contextual states, personal computing device 120 can determine one or more actions… the one or more actions can include selecting a set of audio processing parameters that change the manner in which a user experiences his or her ambient sound environment" (Meacham selects audio processing parameters based on determined contextual or scene information. The selected parameters that enhance a speaker's audibility correspond to elementary dialogue enhancement parameters)) and optionally, based on at least one predetermined dialog enhancement setting profile (Meacham, P[0066]: "The contextual state memory 430 is configured to store dozens or hundreds or an even larger number of action sets 432. Each action set 432 may be associated with at least one indicator, where an “indicator” is data indicating conditions or circumstances where the associated action set 432 is appropriate for selection." (Meacham stores predetermined action sets containing processing parameter sets and selects the appropriate stored set for the detected contextual environment thus, teaching a predetermined enhancement setting profile)).
Regarding claim 37, claim 37 recites the apparatus corresponding to the method described in claim 21 and is rejected for the same reasons as above.
Grokop further discloses:
An apparatus, comprising a processor and a memory coupled to the processor, wherein the processor is adapted to cause the apparatus (Grokop, P[0070], P[0081]):
Regarding claim 38, claim 38 recites the non-transitory computer-readable storage medium corresponding to the method described in claim 21 and is rejected for the same reasons as above.
Grokop further discloses:
A non-transitory computer-readable storage medium storing a program for performing environment-aware processing of audio data for a mobile device, the program comprising instructions that, when executed by a processor, cause the processor to (Grokop, P[0067]):
Claims 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over Meacham et al. (hereinafter Meacham) (US 20200380979 A1) in view of Grokop et al. (hereinafter Grokop) (US 20130245986 A1), Vallabhan et al. (hereinafter Vallabhan) (US 9838784 B2) (see attached copy for paragraph numbers) and Sarathy et al. (hereinafter Sarathy) (US 20230096949 A1).
Regarding claim 25, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 24.
The combination of Meacham, Grokop, and Vallabhan does not explicitly disclose:
wherein the processing of sensor data in the non-acoustic sensor information comprises:
pre-processing the non-acoustic sensor information by at least one of:
aligning timestamps of sensor data in the non-acoustic sensor information stemming from different non-acoustic sensors, or identifying invalid sensor data in the non-acoustic sensor information
However, Sarathy discloses:
wherein the processing of sensor data in the non-acoustic sensor information comprises:
pre-processing the non-acoustic sensor information by at least one of:
aligning timestamps of sensor data in the non-acoustic sensor information stemming from different non-acoustic sensors, or identifying invalid sensor data in the non-acoustic sensor information (Sarathy, P[0034]: "updated timestamps using a common shared time base" (Sarathy receives data originating from different sensor sources, time stamps each data stream according to its source time base, synchronizes the streams, and updates their timestamps onto a common shared time base. This teaches preprocessing non acoustic multiple sensor information by aligning timestamps of sensor data stemming from different sensors. Because the claim recites alternatives, it is unnecessary to establish the separate "identifying invalid sensor data" alternative)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Meacham in view of Grokop, Vallabhan, and Sarathy. Doing so would have brought the sensor stream synchronization methods using updated time stamps (Sarathy, Abstract, P[0034]) with the estimation methods of noise levels associated with the determined scene of Vallabhan (Vallabham, Abstract, P(9)) with the determination of mobile device’s state from non-acoustic sensors and classification of mobility states of Grokop (Grokop, Abstract, P[0022]) with the audio processing based on user’s contextual environment through selecting sets of audio parameters that change the manner in which user experiences their ambient sound environment of Meacham (Meacham, Abstract, Claim 1), thus, predictably applying known multi-sensor preprocessing techniques to provide temporally synchronized sensor information and thus improve the reliability of the resulting scene classification.
Regarding claim 26, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 24.
The combination of Meacham, Grokop, and Vallabhan does not explicitly disclose:
wherein the processing of sensor data in the non-acoustic sensor information comprises:
refining the non-acoustic sensor information by at least one of:
resampling or filtering of sensor data in the non-acoustic sensor information interpolation resampling block
However, Sarathy discloses:
wherein the processing of sensor data in the non-acoustic sensor information comprises:
refining the non-acoustic sensor information by at least one of:
resampling or filtering of sensor data in the non-acoustic sensor information interpolation resampling block (Sarathy, P[0034]: "interpolation resampling block" (Sarathy passes multi-sensor data through an interpolation resampling block that performs up conversion or down conversion sample rate adjustments and outputs synchronized data on a common time based. This refines the sensor information by resampling the sensor data. Because the claim requires only resampling or filtering, the resampling disclosure fully satisfies the limitation without separately establishing filtering.)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Meacham in view of Grokop, Vallabhan, and Sarathy. Doing so would have brought the sensor stream synchronization methods using updated time stamps (Sarathy, Abstract, P[0034]) with the estimation methods of noise levels associated with the determined scene of Vallabhan (Vallabham, Abstract, P(9)) with the determination of mobile device’s state from non-acoustic sensors and classification of mobility states of Grokop (Grokop, Abstract, P[0022]) with the audio processing based on user’s contextual environment through selecting sets of audio parameters that change the manner in which user experiences their ambient sound environment of Meacham (Meacham, Abstract, Claim 1), thus, predictably applying known multi-sensor preprocessing techniques to provide temporally synchronized sensor information and thus improve the reliability of the resulting scene classification.
Claim 34 is rejected under 35 U.S.C. 103 as being unpatentable over Meacham et al. (hereinafter Meacham) (US 20200380979 A1) in view of Grokop et al. (hereinafter Grokop) (US 20130245986 A1), Vallabhan et al. (hereinafter Vallabhan) (US 9838784 B2) (see attached copy for paragraph numbers) and Paludan-Muller et al. (hereinafter Paludan-Muller) (US 20060204025 A1).
Regarding claim 34, the combination of Meacham, Grokop, and Vallabhan disclose the method according to claim 33.
The combination of Meacham, Grokop, and Vallabhan does not explicitly disclose:
wherein the dialog enhancement further comprises:
determining an estimated noise level based on the determined scene information;
However, Paludan-Muller discloses:
wherein the dialog enhancement further comprises:
determining an estimated noise level based on the determined scene information (Paluden-Muller, P[0033]: "said signal processing means having a table with sets of acoustic processing parameters associated with a set of stored noise classes and noise levels", "classifying a background noise component in the audio signal, estimating a level of a background noise component in the audio signal, retrieving from the table a set of signal processing parameters according to the classification and the level of background noise" (Paludan-Muller determines an acoustic environment/noise classification and an associated estimate background-noise level for use in speech enhancing audio processing. The determined noise class corresponds to the claimed scene information, and the estimated background noise level is determined and used in association with that classified scene.)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Meacham in view of Groko, Vallabham, and Paludan-Muller. Doing so would have brought the noise-classification and level estimation techniques taught by Paludan-Muller (Paludan-Muller, Abstract, P[0033]) estimation methods of noise levels associated with the determined scene of Vallabhan (Vallabham, Abstract, P(9)) with the determination of mobile device’s state from non-acoustic sensors and classification of mobility states of Grokop (Grokop, Abstract, P[0022]) with the audio processing based on user’s contextual environment through selecting sets of audio parameters that change the manner in which user experiences their ambient sound environment of Meacham (Meacham, Abstract, Claim 1), thus, predictably using known environmental noise analysis to select audio-processing parameters appropriate for the detected environment.
Claims 35-36 are rejected under 35 U.S.C. 103 as being unpatentable over Meacham et al. (hereinafter Meacham) (US 20200380979 A1) in view of Grokop et al. (hereinafter Grokop) (US 20130245986 A1), Vallabhan et al. (hereinafter Vallabhan) (US 9838784 B2) (see attached copy for paragraph numbers), Paludan-Muller et al. (hereinafter Paludan-Muller) (US 20060204025 A1), and Fuchs et al. (hereinafter Fuchs) (WO 2019081089 A1) (see attached copy for page numbers).
Regarding claim 35, the combination of Meacham, Grokop, Vallabhan, and Paludan-Muller disclose the method according to claim 34.
The combination of Meacham, Grokop, Vallabhan, and Paludan-Muller does not explicitly disclose:
wherein the estimated noise level is determined based on noise statistics and/or histogram information corresponding to the determined scene information
However, Fuchs discloses:
wherein the estimated noise level is determined based on noise statistics and/or histogram information corresponding to the determined scene information (Fuchs, Page 15: "The noise statistics is computed as follows:", Page 6: "the noise relationship and/or information estimator is configured to provide the statistical relationships and/or information regarding noise in the form of a matrix establishing relationships of variance, covariance, correlation and/or autocorrelation values associated to the noise" (Fuchs determines then noise information from noise statistics)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Meacham in view of Groko, Vallabham, Paludan-Muller, and Fuchs. Doing so would have brought the estimated noise level calculation using noise statistics from Fuchs (Fuchs, Abstract, Page 15) noise-classification and level estimation techniques taught by Paludan-Muller (Paludan-Muller, Abstract, P[0033]) estimation methods of noise levels associated with the determined scene of Vallabhan (Vallabham, Abstract, P(9)) with the determination of mobile device’s state from non-acoustic sensors and classification of mobility states of Grokop (Grokop, Abstract, P[0022]) with the audio processing based on user’s contextual environment through selecting sets of audio parameters that change the manner in which user experiences their ambient sound environment of Meacham (Meacham, Abstract, Claim 1), thus, predictably applying known statistical noise-estimation techniques to provide a more accurate characterization of the noise associated with the determined scene.
Regarding claim 36, the combination of Meacham, Grokop, Vallabhan, and Paludan-Muller disclose the method according to claim 34.
The combination of Meacham, Grokop, Vallabhan, and Paludan-Muller does not explicitly disclose:
wherein the dialog enhancement further comprises:
refining the elementary dialog enhancement parameter based on the estimated noise level to determine a refined dialog enhancement parameter for use in dialog enhancement applied to the audio data
However, Fuchs discloses:
wherein the dialog enhancement further comprises:
refining the elementary dialog enhancement parameter based on the estimated noise level (Fuchs, Page 15: "The normalized covariance and the estimated gain are employed together to obtain the estimate of the current frequency sample.", "The gain is computed during the post-filtering based on the already processed vaiues" (Fuchs refines the enhancement processing using the estimated noise information by computing an updated enhancement gain)) to determine a refined dialog enhancement parameter for use in dialog enhancement applied to the audio data (Fuchs, Page 15: "normalized covariance and the estimated gain are employed together to obtain the estimate of the current frequency sample. " (teaches refining those enhancement parameters based on the estimated noise information before applying them to the audio signal)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Meacham in view of Groko, Vallabham, Paludan-Muller, and Fuchs. Doing so would have brought the estimated noise level calculation using noise statistics from Fuchs (Fuchs, Abstract, Page 15) noise-classification and level estimation techniques taught by Paludan-Muller (Paludan-Muller, Abstract, P[0033]) estimation methods of noise levels associated with the determined scene of Vallabhan (Vallabham, Abstract, P(9)) with the determination of mobile device’s state from non-acoustic sensors and classification of mobility states of Grokop (Grokop, Abstract, P[0022]) with the audio processing based on user’s contextual environment through selecting sets of audio parameters that change the manner in which user experiences their ambient sound environment of Meacham (Meacham, Abstract, Claim 1), thus, predictably applying known statistical noise-estimation techniques to provide a more accurate characterization of the noise associated with the determined scene.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHASHIDHAR S MANOHARAN whose telephone number is (571)272-6772. The examiner can normally be reached M-F 8:00-4:00.
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/SHASHIDHAR SHANKAR MANOHARAN/Examiner, Art Unit 2655
/ANDREW C FLANDERS/Supervisory Patent Examiner, Art Unit 2655
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