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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/10/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1-10 and 12-21 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.
Claims 1-2, 4, 6-10, 12-14 and 16-20 have been amended, claim 21 has been newly added and claim 11 has been previously canceled.
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 1, 5-9, 12, 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Binal (US 2023/0163742) in view of Carrigan (US 2022/0374197).
Regarding claim 1, Binal discloses a computer-implemented method, comprising:
receiving, by at least one computer processor, an audio signal associated with a content, wherein the audio signal comprises a plurality of portions that collectively span a duration of the audio signal (receiving audio wherein the audio is associated with video; see at least paragraphs 0170, 0181-0182 and 0189);
preprocessing the audio signal to generate preprocessed audio data (the audio signal is analyzed; see at least paragraphs 0190-0191);
determining, using the preprocessed audio data, an audio class, wherein determined audio classes indicate corresponding audio modes for playing (determine type of audio; see at least paragraphs 0056, 0054, 0181, 0190-0192 and 0235); and
causing playback of the audio signal in accordance with the corresponding audio modes (broadcast the audio to loudspeaker; see at least paragraph 0181 and 0194).
Binal discloses the audio signal and the audio classes; as above, but is not clear about wherein the plurality of portions are classifiable into a plurality of audio classes, an audio class for each portion and playback the audio such that different portions of the audio signal are played using different audio modes.
Carrigan discloses the above missing limitation; using the same audio (e.g. different portions of the same audio) to demonstrate different audio output modes, by rendering a first portion of the audio using one audio output mode and a second portion of the same audio using another audio output mode; see at least paragraphs 0235-0236 and 0277.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Binal by the teachings of Carrigan by having the above limitations for the purpose of changing a current audio output mode of a set of one or more audio output devices; see at least the Abstract.
Regarding claim 5, Binal in view of Carrigan disclose the computer-implemented method of claim 1, wherein preprocessing the audio signal comprises at least one of generating audio samples from the audio signal, converting the audio signal from a time-domain to a frequency domain, or generating a spectrogram associated with the audio signal (Binal; generating spectral plots; see at least Figs. 5-9 and paragraphs 0190-0192).
Regarding claim 6, Binal in view of Carrigan disclose the computer-implemented method of claim 1, wherein the audio class determined for each portion is used to determine one or more parameters for processing the corresponding portion of the audio signal (Binal; see at least paragraphs 0056, 0054, 0181, 0190-0192 and 0235 in combination with Carrigan’s portions; see at least the rejection of claim 1).
Regarding claim 7, Binal in view of Carrigan disclose the computer-implemented method of claim 1, wherein the audio class determined for each portion is used to select a digital signal processing (DSP) algorithm for audio quality (AQ) enhancement of the corresponding portion of the audio signal (Binal; see at least paragraphs 0175-0177, 0179 and 0181 in combination with Carrigan’s portions; see at least the rejection of claim 1).
Regarding claim 8, Binal in view of Carrigan disclose the computer-implemented method of claim 1, wherein the audio class determined for each portion is used to select one or more parameters of a digital signal processing (DSP) algorithm for audio quality (AQ) enhancement of the corresponding portion of the audio signal (Binal; see at least paragraphs 0175-0177, 0179 and 0181 in combination with Carrigan’s portions; see at least the rejection of claim 1).
Regarding claim 9, Binal in view of Carrigan disclose the computer-implemented method of claim 1, wherein the audio class determined for each portion of the plurality of portions is used to select an audio mode for playing the corresponding portion of the audio signal on a media device or a display device (Binal; see at least paragraphs 0054 and 0235 in combination with Carrigan’s portions; see at least the rejection of claim 1).
Claim 12 is rejected on the same grounds as claim 1.
Claim 15 is rejected on the same grounds as claim 5.
Claim 16 is rejected on the same grounds as claim 7.
Claim 17 is rejected on the same grounds as claim 8.
Claim 18 is rejected on the same grounds as claim 9.
Claim 20 is rejected on the same grounds as claim 1.
Claims 2-4, 10, 13-14, 19 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Binal in view of Carrigan and further in view of Gupta (US 11,617,008).
Regarding claim 2, Binal in view of Carrigan disclose the computer-implemented method of claim 1, and determining the audio class for each portion of the plurality of portions; as above, but is not clear about using an artificial intelligence (AI) classification model.
Gupta discloses machine learning models may be used for audio event recognition by a media classification system; see at least col. 8, line 52-col. 8, line 5.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Binal in view of Carrigan by the teachings of Gupta by having the above limitations for the purpose of media classification using local and global audio features; see at least the Abstract.
Regarding claim 3, Binal in view of Carrigan and further in view of Gupta disclose the computer-implemented method of claim 2, wherein the AI classification model comprises one or more gated recurrent unit (GRU) blocks (Gupta; see at least col. 8, line 52-col. 8, line 5).
Regarding claim 4, Binal in view of Carrigan and further in view of Gupta disclose the computer-implemented method of claim 3, wherein determining the audio class for each portion of the plurality of portions further comprises determining a number of the one or more GRU blocks used for classifying the preprocessed audio data (Gupta; see at least col. 8, line 52-col. 8, line 5).
Regarding claim 10, Binal in view of Carrigan disclose the computer-implemented method of claim 1, wherein determining the audio class for each portion of the plurality of portions comprises using metadata associated with the audio signal to classify the preprocessed audio data; as above, but is not clear about using artificial intelligence (AI) classification model.
Gupta discloses machine learning models may be used for audio event recognition by a media classification system; see at least col. 8, line 52-col. 8, line 5.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Binal in view of Carrigan by the teachings of Gupta by having the above limitations for the purpose of media classification using local and global audio features; see at least the Abstract.
Claim 13 is rejected on the same grounds as claims 2 and 3.
Claim 14 is rejected on the same grounds as claim 4.
Claim 19 is rejected on the same grounds as claim 10.
Regarding claim 21, Binal in view of Carrigan and further in view of Gupta disclose the computer-implemented method of claim 2, wherein:
each portion of the plurality of portions corresponds to a temporally bounded segment of the audio signal, and
determining the audio class for each portion comprises classifying the temporally bounded segments independently (the portions of Carrigan; see at least the rejection of claim 1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YASSIN ALATA whose telephone number is (571)270-5683. The examiner can normally be reached Mon-Fri 7-4 ET.
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/YASSIN ALATA/Primary Examiner, Art Unit 2426
