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 .
Claims 1-9 were pending. By preliminary amendment on 2/11/26, applicant has amended claims 1-3, 5-9 canceled claim 4, and added claims 10-19. Thus claims 1-3,5-20 are pending. This action is a non-final office action on the merits.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3,5-20 are rejected under 35 USC 101 because they are directed to an abstract idea without significantly more.
Claims 1 and 11 are independent. Claims 11, and 1 are method and device which are statutory classes of invention (Step 1 yes)
Claim 1 is analyzed, the abstract elements are as follows;
storing, in a …, a plurality of stem data extracted from one or more audio data and a plurality of embedding vectors generated from the plurality of stem data; outputting, by … via a pre-processing module, one or more input stem data from an input audio data; generating, by …, one or more input embedding vectors from the one or more input stem data using an …; searching, by …, for one or more similar embedding vectors, among the plurality of embedding vectors stored in the …, based on a similarity calculated between the one or more input embedding vectors and the plurality of embedding vectors; and generating, by …, a recommendation list for at least one of:(i) one or more similar stem data corresponding to the one or more similar embedding vectors, and(ii) one or more similar audio data corresponding to the one or more similar embedding vectors.
Here the technical elements include “process” possibly memory module, AI network module.
Step 2A prong one, claim recites a judicial exception. The claim recites
when a claim recites multiple abstract ideas that fall in the same or different groupings, examiners should consider the limitations together as a single abstract idea. In this case,
the claims are directed to mathematical concepts directed to musical analysis.
Basically the music is being analyzed by a generic processor and equipment.
Step 2A prong one yes
Step 2B does the claim as a who amount to significantly more than the recited exception. Where any element or combination of additional elements adds inventive concept.
Here the additional elements such as processor and IAI module are not enough.
Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept (Step 2B: NO). The claim is not eligible.
Claims 1, 11 do not put any limits on how the signal is received and processed.. The output is “a recommendation list”
Here it is noted that the applicant specification includes fig. 2 a way to make the process faster by not going through the pre-processing module. Also since this example is similar to example 48 claim 1, claims 2, 3 in example 48 were eligible and applicant might be able to emulate this.
The dependent claims 2-3, 5-10,12-19 are rejected because they depend on claims 1, 11 and do not correct he concerns of claims 1, 11.
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.
Claim(s) 1,3, 5,6,8-11,13-15,17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication to 20180276540 Xing
As per claim 11, Xing discloses; A method for music analysis, comprising:
storing, in a memory module,
a plurality of stem data extracted from one or more audio data and a plurality of embedding vectors generated from the plurality of stem data;
xing(0071)
outputting, by a processor via a pre-processing module, one or more input stem data from an input audio data;
Xing(0028, a snippet is like a stem, ie it’s a short portion that can be tagged by features)
generating, by the processor, one or more input embedding vectors from the one or more input stem data using an artificial neural network module;
Xing(0042-44)
searching, by the processor, for one or more similar embedding vectors, among the plurality of embedding vectors stored in the memory module,
Xing(0042-44)
based on a similarity calculated between the one or more input embedding vectors and the plurality of embedding vectors; and Xing(0062)
generating, by the processor, a recommendation list for at least one of:
(i) one or more similar stem data corresponding to the one or more similar embedding vectors, and
(ii) one or more similar audio data corresponding to the one or more similar embedding vectors.
Xing(0043 vectors are used…. To compare music data, make recommendations)
Claim 1 is similar to claim 11.
As per claim 13, Xing discloses; (New) The method according to claim 11,
wherein type of the plurality of stem data include at least one of vocal, drum, bass, piano, and accompaniment.
Xing(0029 music type)
Claim 3 is similar to claim 13
As per 14, Xing discloses;
The method according to claim 13,
wherein the artificial neural network module comprises a plurality of pre-learned artificial neural networks, and
wherein each of the plurality of pre-learned artificial neural networks receives a different type of the plurality of stem data as input and outputs a corresponding embedding vector.
Xing(0043, storage)
Claim 5 is similar to claim 14
As per claim 15, Xing discloses;
The method according to claim 14,
wherein each of the plurality of pre-learned artificial neural networks comprises a convolutional neural network (CNN) based encoder structure. Xing(0028)
Claim 6 is similar to claim 15
As per claims 17, Xing discloses;
The method according to claim 11, further comprising:
outputting, by the processor, an audio embedding vector by inputting the input audio data to an audio artificial neural network without passing
through the preprocessing module;
searching, by the processor, for one or more similar audio embedding vectors, among a plurality of audio embedding vectors stored in the memory module, based on a similarity between the audio embedding vector and the plurality of audio embedding vectors; and
generating the recommendation list based further on the one or more similar audio embedding vectors.
Xing(0062)
Claim 8 is similar to claim 17
As per claim 18, Xing discloses;
The method according to claim 11, further comprising:
storing, in the memory module, a plurality of tagging information corresponding to the plurality of stem data;
outputting, by the processor, one or more input tagging information corresponding to the one or more input stem data;
searching, by the processor, for one or more similar tagging information, among the plurality of tagging information stored in the memory module, based on a similarity between the one or more input tagging information and the plurality of tagging information stored in the memory module; and
generating, by the processor, the recommendation list based further on at least one of:
(i) one or more similar stem data corresponding to the one or more similar tagging information, and
(ii) one or more similar audio data corresponding to the one or more similar tagging information.
Xing(0028, tagging data….., establish song to song similarity, 0062 )
Claim 9 is similar to claim 18
As per claim 10, Xing discloses;
The music analysis device according to claim 9, wherein the tagging information includes at least one of genre information, mood information, instrument information, and music creation time information.
Xing(0028, “one of” requires only one)
Claim 19 is similar to claim 10
Claim(s) 2,12, 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication to 20180276540 Xing in view of US Patent to Wang 12106740
As per claim 12, Xing does not explicitly disclose what Wang teaches;
the method of claim 11,
wherein determining the similarity includes determining the similarity between the one or more input embedding vectors and the plurality of embedding vectors using a Euclidean distance.
Wang(col. 4 lines 25-60, Euclidian distance and vectors)
It would therefore have been obvious to one of ordinary skill before the effective filing date of the invention to combine the music analysis disclosure of Xing with the Euclidian distance teachings of Wang for the motivation of better modeling music. Col. 1 lines 20-25
Claim 2 is similar to claim 12
As per claim 16, Here Xing does not explicitly disclose what Wang teaches;
The method according to claim 11,
wherein the artificial neural network module further comprises a dense layer in which the one or more input embedding vectors and the plurality of embedding vectors are shared, and
wherein calculating the similarity includes calculating the similarity between the one or more input embedding vectors and the plurality of embedding vectors based on the dense layer.
Wang (col. 7 lines 35-40- dense layer)
It would therefore have been obvious to one of ordinary skill before the effective filing date of the invention to combine the music analysis disclosure of Xing with the dense layer teachings of Wang for the motivation of better modeling music. Col. 1 lines 20-25
Claim 7 is similar to claim 16
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Automatic Music Labeling Algorithm based on Tag Depth Analysis, IEEE 2023
Multi-Modal Song Mood Detection with Deep Learning, IEEE 2022
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/BRUCE I EBERSMAN/Primary Examiner, Art Unit 3693