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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-11 and 13-15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claim(s) 1-3, 5-7, 9, and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (US 2018/0139537 A1, previously cited and hereafter Shi) in view of Sakai (US 2017/0201828 A1, previously cited), and further in view of Stephens (US 2008/0013752 A1).
Regarding claim 1, Shi teaches:
“An information processing apparatus comprising:
a filter processing unit configured to perform a filter process on an input signal,” by teaching an audio processing module configured to filter an input signal, such as filtering the input signal with a time domain filter (see Shi, ¶ 0011-0015, 0018, 0025, 0028, and 0030, and figures 1-5 and 8, units 11 and 110), “wherein the input signal includes a sound of a target audio source type” because the input signal includes content, or target audio, that is classified by an analysis module (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412);
“a filter setting unit configured to:
obtain, by an estimation algorithm, an estimation result from the input signal” by teaching that the audio analysis module analyzes the input signal, such as analyzing, or estimating, spectral or temporal characteristics in the time or frequency domain with a content classifier, to determine an analysis, or estimation, result (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412); and
“perform, based on the obtained estimation result, a set process for the filter process” by teaching a filter controller to set the filter coefficients based on the estimated results by the audio analysis module (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412).
However, Shi does not appear to teach the features for “a frequency characteristic correction unit configured to correct, based on a frequency characteristic of an output device that outputs an output signal after the filter process”.
Sakai discloses a digital signal processor for audio with a multi-band equalizer that is set according to presets (see Sakai, abstract). Sakai teaches equalizer presets corresponding to audio genres (see Sakai, ¶ 0003), and teaches the multi-band equalizer applies sound field correction and frequency correction of the output speakers (see Sakai, ¶ 0003 and 0064).
Additionally, Stephens teaches an audio entertainment system equalizer and method for equalizing sound from an external source (see Stephen, abstract). Herein, Stephens teaches equalizer settings for the vehicle radio and equalizer settings provided by the external audio source, such that the external audio source device’s equalizer can be optimized for output devices, such as headsets (see Stephens, ¶ 0005-0007). Stephens teaches that the vehicle audio system accounts for the various equalizing settings and provides a final equalization vector based on the summing (if in decibels) or multiplying of the equalization vectors from the external audio source based on genre and/or song equalization vectors (see Stephens, ¶ 0024-0026 and figure 3, units 12, 22, and 24). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Shi with the teachings of Sakai and the teachings of Stephens for the purpose of providing optimal sound from multiple equalization sources, such as improving the sound reproduced in a specific room or vehicle cabin with various output devices (see Shi, ¶ 0052 in view of Sakai, ¶ 0003, and further in view of Stephens, ¶ 0006-0008 and 0026).
Therefore, the combination of Shi, Sakai, and Stephens makes obvious the features for:
“a frequency characteristic correction unit configured to correct, based on a frequency characteristic of an output device that outputs an output signal after the filter process, the set process for the filter process” (see Sakai, ¶ 0003, in view of Stephens, ¶ 0006-0007, where it is obvious to provide an equalization setting based on the output device, and see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412 in view of Stephens, ¶ 0006-0008 and 0024-0026, where it is obvious to cascade the different equalization settings, such that the set process equalizes based on an estimated genre and the filter is modified by an equalizer for an output device),
“wherein the filter setting unit is further configured to control, based on the corrected set process, the sound of the target audio source type included in the input signal” (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412 in view of Sakai, ¶ 0003 and further in view of Stephens, ¶ 0006-0008 and 0024-0026, where it is obvious to filter based on these features as discussed above).
Regarding claim 2, see the preceding rejection with respect to claim 1 above. The combination makes obvious the “first information processing apparatus according to claim 1, wherein the filter process has a lower delay than the estimation algorithm” by teaching that the filtering and audio analysis are performed in parallel, such that the filtering is performed with little and/or almost no latency (see Shi, ¶ 0028 and 0033).
Regarding claim 3, see the preceding rejection with respect to claim 1 above. The combination makes obvious the “first information processing apparatus according to claim 1, wherein the filter process includes real-time process” by teaching that the filtering and audio analysis are performed in parallel, such that the filtering is processed in real time (see Shi, ¶ 0028 and 0033).
Regarding claim 5, see the preceding rejection with respect to claim 1 above. The combination makes obvious the “first information processing apparatus according to claim 1, wherein the estimation algorithm directly estimates the set process for the filter process” by teaching that the analyzer module includes equalization analyzer, and the equalization analyzer determines the equalization level for controlling the filter coefficients in the equalizer filter (see Shi, ¶ 0050-0051 and figure 4, units 12 and 412).
Regarding claim 6, see the preceding rejection with respect to claim 1 above. The combination makes obvious the “first information processing apparatus according to claim 1, wherein the set process one of amplifies each frequency band of the input signal or attenuates the each frequency band of the input signal, and the sound of the target audio source type is one of amplified based on the amplification by the set process or attenuated based on the attenuation by the set process” by teaching the time domain filter acting as an equalization filter that amplifies and/or attenuates each frequency band (see Shi, ¶ 0049 and 0052 and figure 4, unit 110).
Regarding claim 7, see the preceding rejection with respect to claim 1 above. The combination makes obvious the “first information processing apparatus according to claim 1, wherein the filter process is performed by use of an infinite impulse response (IIR) filter” because Sakai makes obvious the use of IIR digital filters for equalization (see Shi, ¶ 0052 in view of Sakai, ¶ 0010 and 0040 and figure 2B), and
“the set process a filter coefficient of the IIR filter” because the filtering is set by changing the filter coefficients (see Shi, ¶ 0052 and see Sakai, ¶ 0041 and figure 2B).
Regarding claim 9, see the preceding rejection with respect to claim 1 above. The combination makes obvious the “first information processing apparatus according to claim 1, further comprising a screen display update unit configured to: receive an instruction for the filter process; and update the set process at one of: a time associated with the received instruction for the filter process, a regular interval, or a time that does not give discomfort to a user, wherein the time that does not give discomfort to the user is associated with determination information” by teaching that the filter is update whenever the processing parameters are received (see Shi, ¶ 0033), and also teaches analyzing the input signal in windowed frames, such as 20 millisecond frames, where the filter is updated at regular intervals according to the selected analysis frame size (see Shi, ¶ 0035 and 0041-0042).
Regarding claim 14, see the preceding rejection with respect to claim 1 above. The combination of Shi, Sakai, and Stephens makes obvious:
“An information processing method” by teaching a process that performs the following equalization filtering of an input signal (see Shi, ¶ 0077 and 0082, and figure 9),
“comprising:
performing a filter process on an input signal” by teaching an audio processing module configured to filter an input signal, such as filtering the input signal with a time domain filter (see Shi, ¶ 0011-0015, 0018, 0025, 0028, and 0030, and figures 1-5 and 8, units 11 and 110), “wherein the input signal includes a sound of a target audio source type” because the input signal includes content, or target audio, that is classified by an analysis module (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412);
“obtaining, by an estimation algorithm, an estimation result from the input signal” by teaching that the audio analysis module analyzes the input signal, such as analyzing, or estimating, spectral or temporal characteristics in the time or frequency domain with a content classifier, to determine an analysis, or estimation, result (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412);
“performing, based on the obtained estimation result, a set process for the filter process” by teaching a filter controller to set the filter coefficients based on the estimated results by the audio analysis module (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412);
“correcting, based on a frequency characteristic of an output device that outputs an output signal after the filter process, the set process for the filter process” (see Sakai, ¶ 0003, in view of Stephens, ¶ 0006-0007, where it is obvious to provide an equalization setting based on the output device, and see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412 in view of Stephens, ¶ 0006-0008 and 0024-0026, where it is obvious to cascade the different equalization settings, such that the set process equalizes based on an estimated genre and the filter is modified by an equalizer for an output device); and
“controlling, based on the corrected set process, the sound of the target audio source type included in the input signal” (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412 in view of Sakai, ¶ 0003 and further in view of Stephens, ¶ 0006-0008 and 0024-0026, where it is obvious to filter based on these features as discussed above).
Regarding claim 15, see the preceding rejection with respect to claim 1 above. The combination of Shi, Sakai, and Stephens makes obvious:
“A non-transitory computer-readable medium having stored thereon, computer-executable instructions which, when executed by a processor, cause the processor to execute operations, the operations” by teaching the medium comprising software and/or firmware for performing the following process by a computer system (see Shi, ¶ 0074-0076 and 0085, and figure 10),
“comprising:
performing a filter process on an input signal” by teaching an audio processing module configured to filter an input signal, such as filtering the input signal with a time domain filter (see Shi, ¶ 0011-0015, 0018, 0025, 0028, and 0030, and figures 1-5 and 8, units 11 and 110), “wherein the input signal includes a sound of a target audio source type” because the input signal includes content, or target audio, that is classified by an analysis module (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412);
“obtaining, by an estimation algorithm, an estimation result from the input signal” by teaching that the audio analysis module analyzes the input signal, such as analyzing, or estimating, spectral or temporal characteristics in the time or frequency domain with a content classifier, to determine an analysis, or estimation, result (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412);
“performing, based on the obtained estimation result, a set process for the filter process” by teaching a filter controller to set the filter coefficients based on the estimated results by the audio analysis module (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412);
“correcting, based on a frequency characteristic of an output device that outputs an output signal after the filter process, the set process for the filter process” (see Sakai, ¶ 0003, in view of Stephens, ¶ 0006-0007, where it is obvious to provide an equalization setting based on the output device, and see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412 in view of Stephens, ¶ 0006-0008 and 0024-0026, where it is obvious to cascade the different equalization settings, such that the set process equalizes based on an estimated genre and the filter is modified by an equalizer for an output device); and
“controlling, based on the corrected set process, the sound of the target audio source type included in the input signal” (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412 in view of Sakai, ¶ 0003 and further in view of Stephens, ¶ 0006-0008 and 0024-0026, where it is obvious to filter based on these features as discussed above).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Shi, Sakai, and Stephens as applied to claim 1 above, and further in view of Morsy et al. (US 2023/0335091 A1, previously cited and hereafter Morsy)
Regarding claim 4, see the preceding rejection with respect to claim 1 above. The combination of Shi, Sakai, and Stephens makes obvious the information processing apparatus according to claim 1, wherein the estimation algorithm estimates the type of the target audio from the input signal (see Shi, ¶ 0050). However, the combination does not appear to teach the estimation algorithm separates the sound of the target audio source type from the input signal.
Morsy discloses a method and device for decomposing, recombining, and playing audio data (see Morsy, abstract). Morsy teaches a device for applying one or more audio effects to the separated audio signals, such as applying equalizer effects to one or more decomposed tracks before recombining the decomposed tracks into a processed audio output (see Morsy, ¶ 0131-0133 and figure 3, units 51-1 and 51-2). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify the combination of Shi, Sakai, and Stephens with the teachings of Morsy to apply one or more desired audio effects to different types of target audio, such as applying desired effects to vocals in a song (see Shi, ¶ 0050 and 0054 in view of Morsy, ¶ 0176-0178).
Therefore, the combination of Shi, Sakai, Stephens, and Morsy makes obvious the “first information processing apparatus according to claim 1, wherein the estimation algorithm estimates and separates the sound of the target audio source type from the input signal” by making it obvious to decompose audio into different types and recombine the detected types of audio into a different processed audio signal (see Shi, ¶ 0050 and 0054 in view of Morsy, ¶ 0006 and 0176-0178), and
“the filter setting unit is further configured to perform, based on a frequency characteristic of the sound of the target audio source type separated by the estimation algorithm, the set process for the filter process” by making it obvious to apply different equalization effects to the desired type of audio (see Shi, ¶ 0050-0052 in view of Morsy, ¶ 0133 and 0176-0178).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Shi, Sakai, and Stephens as applied to claim 1 above, and further in view of Seo et al. (US 2017/0070817 A1, previously cited and hereafter Seo).
Regarding claim 8, see the preceding rejection with respect to claim 1 above. The combination of Shi, Sakai, and Stephens makes obvious the information processing apparatus according to claim 1, where the estimation algorithm is a content classifier that analyzes the input audio signal spectrum or the temporal characteristics of the signal in the frequency or time domain (see Shi, ¶ 0050). However, the combination does not appear to teach that the estimation algorithm uses a trained neural network.
Seo discloses an apparatus for controlling sound and for training a genre recognition model, where an equalizer is set according to a determined genre (see Seo, abstract). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify the combination of Shi, Sakai, and Stephens with the teachings of Seo for the purpose of using different signal classification methods and expecting similar or better results (see Shi, ¶ 0050 and 0054 in view of Seo, ¶ 0031-0034).
Therefore, the combination of Shi, Sakai, Stephens, and Seo makes obvious the “first information processing apparatus according to claim 1, wherein a neural network is trained with the input signal, and the estimation algorithm uses the trained neural network to obtain the estimation result” by making it obvious to use a learning algorithm, such as a neural network, or deep-learning, to estimate the genre of the input signal, where the learning algorithm is trained on an input signal (see Shi, ¶ 0050 in view of Seo, ¶ 0009-0014 and 0031).
Claim(s) 10-11 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Shi, Sakai, and Stephens as applied to claim 1 above, and further in view of Eng (US 2008/0002839 A1, previously cited).
Regarding claim 10, see the preceding rejection with respect to claim 1 above. The combination of Shi, Sakai, and Stephens makes obvious the information processing apparatus according to claim 1, where the filtering is updated (see Shi, ¶ 0052). However, Shi does not appear to teach an output device that indicates a difference between before and after the update.
Eng discloses a smart equalizer that allows a user to set an equalizer and save the equalizer settings as metadata associated with an audio file (see Eng, abstract). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify the combination of Shi, Sakai, and Stephens with the teachings of Eng for the purpose of providing updated equalizer settings to other devices without having to re-input preferred equalizer settings (see Eng, ¶ 0002 and 0021).
Therein, the combination of Shi, Sakai, Stephens, and Eng makes obvious the “first information processing apparatus according to claim 1, further comprising a screen display update unit configured to: update the set process; and control the output device to output information indicating a difference between before and after the update of the set process” by making it obvious to allow a user to input preferred equalizer settings for audio files of a specific genre (see Shi, ¶ 0050-0052 in view of Eng, ¶ 0021-0022), and the combination makes obvious to display prior equalization settings and to display the updated adjustments made to the equalization (see Eng, ¶ 0022).
Regarding claim 11, see the preceding rejection with respect to claim 10 above. The combination makes obvious the “first information processing apparatus according to claim 10, wherein the output device includes a display device, and
the display device displays an image of a user-operable equalizer including the information indicating the difference” by making obvious to display prior equalization settings and to display the updated adjustments made to the equalization (see Eng, ¶ 0022).
Regarding claim 13, see the preceding rejection with respect to claim 1 above. The combination of Shi, Sakai, and Stephens makes obvious the information processing apparatus according to claim 1, where the filter setting is determined by a detected genre (see Shi, ¶ 0050-0052). However, the combination does not appear to teach that the setting of the filtering is determined by another information processing apparatus and obtained via a network.
Eng discloses a smart equalizer that allows a user to set an equalizer and save the equalizer settings as metadata associated with an audio file (see Eng, abstract). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify the combination of Shi, Sakai, and Stephens makes obvious with the teachings of Eng for the purpose of providing updated equalizer settings to other devices without having to re-input preferred equalizer settings (see Eng, ¶ 0002 and 0021).
Therein, the combination of Shi, Sakai, Stephens, and Eng makes obvious the “first information processing apparatus according to claim 1, wherein a second information processing apparatus determines the set process for the filter process” (see Shi, ¶ 0026-0027 and 0049-0051, and figure 4, units 12, 410, and 412 in view of Stephens, ¶ 0006-0008 and 0024-0026, where it is obvious to cascade the different equalization settings, such that the set process equalizes based on an estimated genre and the filter is modified by an equalizer for an output device), “and the first information processing apparatus is configured to obtain the set process from the second information processing apparatus via a network” by making it obvious to synchronize a user’s preferred equalization settings, and makes obvious that devices communicate over networks to synchronize the settings (see Eng, ¶ 0017-0018 and 0027).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fan S Tsang can be reached at (571)272-7547. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Daniel R Sellers/Primary Examiner, Art Unit 2694
Prosecution Insights