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 . See 35 U.S.C. § 100 (note).
Art Rejections
Obviousness
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 1–18 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of US Patent Application Publication 2007/0282860 (published 06 December 2007) (“Athineos”) and US Patent Application Publication 2020/0293574 (published 17 September 2020) (“Urbain”)
Claim 1 is drawn to “a system.” The following table illustrates the correspondence between the claimed system and the Athineos reference.
Claim 1
The Athineos Reference
“1. A system, comprising:
Similarity the Athineos reference describes a method and system for music information retrieval. Athineos at Abs., ¶ 2.
“one or more processors;
“one or more computer-readable media storing computer-executable instructions that, when executed on the one or more processors, cause the one or more processors to perform acts comprising:
Athineos’s system is computer-based and includes computers, or processors, and computer-readable media implemented at a client and server with instructions executed by the computers. Id. at ¶¶ 15, 23, 71, 82.
“generating a song block feature for each song in a plurality of songs, including:
Athineos’s server extracts a set of features (steps 605, 610, 615) from a set of input songs in order to build a database. Id. at ¶ 47, FIG.6. The features include both temporal and spectral features. Id. The features are block features because they correspond to features extracted by a sliding window N. Id.
“extracting time and spectral domain features via a signal window, including at least one of a spectral centroid, a spectral smoothness, a spectral spread, and a spectral dissymmetry,
Athineos applies a short sliding window (e.g., 25 ms) to extract temporal and spectral features, such as a Mel-table representing spectral domain descriptors and mean and covariances (statistical moments) of the Mel values. Id. at ¶¶ 44, 45, FIGs.5A, 5B, 5C. The temporal features are subjected to autoregressive modeling and pseudo-autocorrelation. Id. at ¶ 46, FIG.5C. Other features are contemplated by Athineos. Id. at ¶ 51.
Athineos, however, does not specifically describe the use of spectral centroid, smoothness, spread or dissymmetry.
“generating at least one window feature from the extracted time and spectral domain features, each window feature including at least one of a mean, variance, skewness, and kurtosis,
Athineos further calculates statistical moments, such as means and standard deviations over a larger window N (e.g., 10 seconds). Id. at ¶ 47, FIG.6.
“generating at least one block feature from the at least one window feature, and
“maintaining a list of the at least one block feature for each song in the plurality of songs;
The server creates/generates and stores/maintains an entry for each song’s extracted features and its mean and variance in a relational database (step 620). Id. Athineos stores statistical moments, or window features, such as a mean in order to continuously normalize the database. Id. at ¶ 47.
“normalizing the song block feature;
The server also normalizes the features (steps 630, 635) using the stored mean and the variance for each feature coordinate. Id.
“receiving a request comprising a search key; and
Athineos’s server receives a request for music retrieval from a client. Id. at ¶ 44, FIG.5A. The request is a query seed formed by a clip of a song. Id.
“determining one or more results based on a proximity of the search key to the plurality of songs.”
The server then computes a hash from the query, performs a pre-search, a refinement and renders the results to a client computer. Id. at ¶ 40, FIG.2. The results are selected based on the distance between the query seed and the songs in the database. Id.
Table 1
The table above shows that the Athineos reference describes a system that corresponds closely to the claimed system. However, Athineos does not anticipate the claimed invention because Athineos does not describe the claimed extraction of certain features (i.e., spectral centroid, a spectral smoothness, a spectral spread, and a spectral dissymmetry).
The differences between the claimed invention and the Athineos reference are such that the invention as a whole would have been obvious to one of ordinary skill in the art at the time the Application was effectively filed. The Athineos reference describes several the use of features (MFCC) that may be used for analyzing the similarity between a seed song and songs in its database. Athineos at ¶ 46. Athineos also recognizes that other features may be used. Id. at ¶ 51. In this regard, the Urbain reference, like the Athineos reference, describes several pieces of perceptual information in order to determine the similarity between two songs. Urbain at ¶¶ 12, 48, 56. In particular, Urbain identifies the use of MFCC, as does Athineos. Id. Urbain additionally teaches and suggests the use of other measures including spectral flatness/smoothness. Id. Accordingly, it would have been obvious for one of ordinary skill to modify Athineos’s system and method to extract and use spectral flatness/smoothness as a feature for comparing a seed song to songs in a database. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 2 depends on claim 1, and further requires the following:
“wherein each block feature is generated from a set of window features.”
Athineos likewise computes a block feature over a larger window N (e.g., 10 seconds) based on a set of window features generated by smaller analysis windows (e.g., 25 ms). Athineos at ¶¶ 44–47, FIGs.5, 6. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 3 depends on claim 1, and further requires the following:
“wherein the normalizing of the song block feature further comprises normalizing the at least one block feature.”
Athineos’s server also normalizes the features (steps 630, 635) using the stored mean and the variance for each feature coordinate. Athineos at ¶ 47. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 4 depends on claim 1, and further requires the following:
“wherein the time and spectral domain features further comprise one or more of: a zero crossing rate; a first order autocorrelation; an energy level; and a linear regression.”
Athineos describes extracting an autocorrelation, autoregression model and energy among other features. Athineos at ¶¶ 46, 51. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 5 depends on claim 4, and further requires the following:
“further comprising: generating textual song metadata from the time and spectral domain features.”
Similarly, Athineos describes the use of extracted features to recognize similar songs and present a client with textual representations of matching music files. Athineos at ¶ 40, FIG.2. Athineos also describes the use of speech recognition to produce textual lyrics from utterances included in audio. Id. at ¶ 80. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 6 depends on claim 1, and further requires the following:
“wherein the search key is the identity of a song.”
Athineos provides a text-based search feature, so that a user may enter text pertaining to lyrics of a song that identify that song. Athineos at ¶¶ 70, 71. This reasonably suggests looking up songs based on other textual data, such as the identity, or name of the song, as done in other prior art systems. See id. at ¶¶ 3, 69 (describing the prior art use of name-based searching and the use of user-provided labels to further refine searching). For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 7 depends on claim 1, and further requires the following:
“wherein the search key is music.”
Athineos describes a query seed as including music clipped from an audio recording. Athineos at ¶ 44. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 8 depends on claim 1, and further requires the following:
“wherein the receiving of a request comprising a search key further comprises a second search key and determining the one or more results comprises averaging one or more time and spectral domain features of each search key.”
Likewise, Athineos describes structuring a more complex query by combining features from multiple seed clips. Athineos at ¶¶ 6, 15. This reasonably suggests combining features through any known technique for mathematically combining vectors, such as averaging. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 9 depends on claim 1, and further requires the following:
“further comprising: selecting an embedded artwork for a song based on a heuristic score.”
Athineos selects artwork to present as a search result based on the closeness of match (i.e., a heuristic score) between a seed query and a song in the database. Athineos at ¶ 40. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 10 is drawn to “a method.” The following table illustrates the correspondence between the claimed system and the Athineos reference.
Claim 10
The Athineos Reference
“10. A method comprising:
Similarity the Athineos reference describes a method and system for music information retrieval. Athineos at Abs., ¶ 2.
“generating a song block feature for each song in a plurality of songs, including:
Athineos’s server extracts a set of features (steps 605, 610, 615) from a set of input songs in order to build a database. Id. at ¶ 47, FIG.6. The features include both temporal and spectral features. Id. The features are block features because they correspond to features extracted by a sliding window N. Id.
“extracting time and spectral domain features via a signal window, including at least one of a spectral centroid, a spectral smoothness, a spectral spread, and a spectral dissymmetry,
Athineos applies a short sliding window (e.g., 25 ms) to extract temporal and spectral features, such as a Mel-table representing spectral domain descriptors and mean and covariances (statistical moments) of the Mel values. Id. at ¶¶ 44, 45, FIGs.5A, 5B, 5C. The temporal features are subjected to autoregressive modeling and pseudo-autocorrelation. Id. at ¶ 46, FIG.5C. Other features are contemplated by Athineos. Id. at ¶ 51.
Athineos, however, does not specifically describe the use of spectral centroid, smoothness, spread or dissymmetry.
“generating at least one window feature from the extracted time and spectral domain features, each window feature including at least one of a mean, variance, skewness, and kurtosis,
Athineos further calculates statistical moments, such as means and standard deviations over a larger window N (e.g., 10 seconds). Id. at ¶ 47, FIG.6.
“generating at least one block feature from the at least one window feature, and
“maintaining a list of the at least one block feature for each song in the plurality of songs;
The server creates/generates and stores/maintains an entry for each song’s extracted features and its mean and variance in a relational database (step 620). Id. Athineos stores statistical moments, or window features, such as a mean in order to continuously normalize the database. Id. at ¶ 47.
“normalizing the song block feature;
The server also normalizes the features (steps 630, 635) using the stored mean and the variance for each feature coordinate. Id.
“receiving a request comprising a search key; and
Athineos’s server receives a request for music retrieval from a client. Id. at ¶ 44, FIG.5A. The request is a query seed formed by a clip of a song. Id.
“determining one or more results based on a proximity of the search key to the plurality of songs.”
The server then computes a hash from the query, performs a pre-search, a refinement and renders the results to a client computer. Id. at ¶ 40, FIG.2. The results are selected based on the distance between the query seed and the songs in the database. Id.
Table 2
The table above shows that the Athineos reference describes a system that corresponds closely to the claimed system. However, Athineos does not anticipate the claimed invention because Athineos does not describe the claimed extraction of certain features (i.e., spectral centroid, a spectral smoothness, a spectral spread, and a spectral dissymmetry).
The differences between the claimed invention and the Athineos reference are such that the invention as a whole would have been obvious to one of ordinary skill in the art at the time the Application was effectively filed. The Athineos reference describes several the use of features (MFCC) that may be used for analyzing the similarity between a seed song and songs in its database. Athineos at ¶ 46. Athineos also recognizes that other features may be used. Id. at ¶ 51. In this regard, the Urbain reference, like the Athineos reference, describes several pieces of perceptual information in order to determine the similarity between two songs. Urbain at ¶¶ 12, 48, 56. In particular, Urbain identifies the use of MFCC, as does Athineos. Id. Urbain additionally teaches and suggests the use of other measures including spectral flatness. Id. Accordingly, it would have been obvious for one of ordinary skill to modify Athineos’s system and method to extract and use spectral flatness/smoothness as a feature for comparing a seed song to songs in a database. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 11 depends on claim 10, and further requires the following:
“wherein each block feature is generated from a set of window features.”
Athineos likewise computes a block feature over a larger window N (e.g., 10 seconds) based on a set of window features generated by smaller analysis windows (e.g., 25 ms). Athineos at ¶¶ 44–47, FIGs.5, 6. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 12 depends on claim 10, and further requires the following:
“wherein the normalizing of the song block feature further comprises normalizing the at least one block feature.”
Athineos’s server also normalizes the features (steps 630, 635) using the stored mean and the variance for each feature coordinate. Athineos at ¶ 47. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 13 depends on claim 10, and further requires the following:
“wherein the time and spectral domain features further comprise one or more of: a zero crossing rate; a first order autocorrelation; an energy level; and a linear regression.”
Athineos describes extracting an autocorrelation, autoregression model and energy among other features. Athineos at ¶¶ 46, 51. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 14 depends on claim 13, and further requires the following:
“further comprising: generating textual song metadata from the time and spectral domain features.”
Similarly, Athineos describes the use of extracted features to recognize similar songs and present a client with textual representations of matching music files. Athineos at ¶ 40, FIG.2. Athineos also describes the use of speech recognition to produce textual lyrics from utterances included in audio. Id. at ¶ 80. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 15 depends on claim 10, and further requires the following:
“wherein the search key is the identity of a song.”
Athineos provides a text-based search feature, so that a user may enter text pertaining to lyrics of a song that identify that song. Athineos at ¶¶ 70, 71. This reasonably suggests looking up songs based on other textual data, such as the identity, or name of the song, as done in other prior art systems. See id. at ¶¶ 3, 69 (describing the prior art use of name-based searching and the use of user-provided labels to further refine searching). For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 16 depends on claim 10, and further requires the following:
“wherein the search key is music.”
Athineos describes a query seed as including music clipped from an audio recording. Athineos at ¶ 44. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 17 depends on claim 10, and further requires the following:
“wherein the receiving of a request comprising a search key further comprises a second search key and determining the one or more results comprises averaging time and spectral domain features of each search key.”
Likewise, Athineos describes structuring a more complex query by combining features from multiple seed clips. Athineos at ¶¶ 6, 15. This reasonably suggests combining features through any known technique for mathematically combining vectors, such as averaging. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Claim 18 depends on claim 10, and further requires the following:
“further comprising: selecting an embedded artwork for a song based on a heuristic score.”
Athineos selects artwork to present as a search result based on the closeness of match (i.e., a heuristic score) between a seed query and a song in the database. Athineos at ¶ 40. For the foregoing reasons, the combination of the Athineos and the Urbain references makes obvious all limitations of the claim.
Double Patenting
Legal Basis
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
Obviousness-Type Double Patenting
Claims 1 and 10 are rejected on the ground of nonstatutory double patenting as being unpatentable over the claims of US Patent 12,067,051 (the ‘051 Patent). Although the claims at issue are not identical, they are not patentably distinct from each other.
Claims 1 and 10 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over the claims of US Patent Application 18/800,499 (the ‘499 Application). Although the claims at issue are not identical, they are not patentably distinct from each other.
The following table illustrates the correspondence between claim 1 of this Application and claim 1 of the ‘051 Patent.
Claim 1
The ‘051 Patent
“1. A system, comprising:
“1. A system, comprising:
“one or more processors;
“one or more processors;
“one or more computer-readable media storing computer-executable instructions that, when executed on the one or more processors, cause the one or more processors to perform acts comprising:
“one or more computer-readable media storing computer-executable instructions that, when executed on the one or more processors, cause the one or more processors to perform acts comprising:
“generating a song block feature for each song in a plurality of songs, including:
“generating a song block feature for each song in a plurality of songs, including:
“extracting time and spectral domain features via a signal window,
“extracting time and spectral domain features via a sliding signal windows,
“including at least one of a spectral centroid, a spectral smoothness, a spectral spread, and a spectral dissymmetry,
“including a spectral centroid, a spectral smoothness, a spectral spread, and a spectral dissymmetry,
“generating at least one window feature from the extracted time and spectral domain features, each window feature including at least one of a mean, variance, skewness, and kurtosis,
“generating a plurality of window features from the extracted time and spectral domain features, each window feature including a mean, variance, skewness, and kurtosis,
“generating at least one block feature from the at least one window feature, and
“generating a plurality of block features from the plurality of window features, and
“maintaining a list of the at least one block feature for each song in the plurality of songs;
“maintaining a list of block features for each song in the plurality of songs;
“normalizing the song block feature;
“normalizing the song block feature;
“receiving a request comprising a search key; and
“receiving a request comprising a search key; and
“determining one or more results based on a proximity of the search key to the plurality of songs.”
“determining one or more results based on a proximity of the search key to the plurality of songs.”
Table 3
As seen in the table, the claims are not drawn to the same invention because the claims of this Application are broader in scope than claim 1 of the ‘051 Patent. Therefore, ‘051 would anticipate claim 1 of this Application if it were available as prior art. Similar comparisons may be made with claims 8, 29 and 41 of this Application and claims 1 and 10 of the ‘051 Patent. Applicant is further advised that further correspondence exists among this Application’s numerous dependent claims and the dependent claims of the ‘051 Patent.
The following table illustrates the correspondence between claim 1 of this Application and claim 1 of the ‘499 Application.
Claim 1
The ‘499 Application
“1. A system, comprising:
“1. A system, comprising:
“one or more processors;
“one or more processors;
“one or more computer-readable media storing computer-executable instructions that, when executed on the one or more processors, cause the one or more processors to perform acts comprising:
“one or more computer-readable media storing computer-executable instructions that, when executed on the one or more processors, cause the one or more processors to perform acts comprising:
“generating a song block feature for each song in a plurality of songs, including:
“generating one or more block feature vectors for each song of a plurality of songs, including by:
“extracting time and spectral domain features via a signal window,
“extracting time and spectral domain descriptors,
“[the features] including at least one of a spectral centroid, a spectral smoothness, a spectral spread, and a spectral dissymmetry,
“wherein the descriptors include one or more of: a zero crossing rate, a first order autocorrelation, an energy level, a linear regression, a spectral centroid, a spectral smoothness, a spectral spread, and a spectral dissymmetry,
“generating at least one window feature from the extracted time and spectral domain features, each window feature including at least one of a mean, variance, skewness, and kurtosis,
“[generating] one or more statistical moments of the extracted time and spectral domain descriptors, and
“generating at least one block feature from the at least one window feature, and
N/A (implied by the claimed maintaining function).
“maintaining a list of the at least one block feature for each song in the plurality of songs;
“maintaining a list of block feature vectors for each song in the plurality of songs;
“normalizing the song block feature;
“normalizing the block feature vectors;
“receiving a request comprising a search key; and
“receiving a request comprising a search key; and
“determining one or more results based on a proximity of the search key to the plurality of songs.”
“determining one or more results based on a proximity of the search key to the plurality of songs.”
Table 4
As seen in the table, the claims are not drawn to the same invention because the claims of this Application are narrower in scope than claim 1 of the ‘499 Application. However, the differences are taught and suggested by the prior art, such as the Athineos and Urbain references as shown in the obviousness rejection of claim 1, incorporated herein. Therefore, the combination of claim 1 of the ‘499 Application and the Athineos and Urbain references would make obvious claim 1 of this Application if the ‘499 Application were available as prior art. Similar comparisons may be made with claims 8, 29 and 41 of the ‘499 Application and claims 1 and 10 of this Application. Applicant is further advised that further correspondence exists among this Application’s numerous dependent claims and the dependent claims of the ‘499 Application.
Summary
A timely filed terminal disclaimer in compliance with 37 C.F.R. § 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 C.F.R. § 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 C.F.R. § 1.111(a). For a reply to final Office action, see 37 C.F.R. § 1.113(c). A request for reconsideration while not provided for in 37 C.F.R. § 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Objections
The drawings are objected to under 37 C.F.R. § 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the processors, media and steps/functions performed by the processors must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
Corrected drawing sheets in compliance with 37 C.F.R. § 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 C.F.R. § 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WALTER F BRINEY III whose telephone number is (571)272-7513. The examiner can normally be reached M-F 8 am-4:30 pm.
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/Walter F Briney III/
/CAROLYN R EDWARDS/Supervisory Patent Examiner, Art Unit 2692
Walter F Briney IIIPrimary ExaminerArt Unit 2692
5/21/202626
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