CTNF 18/524,950 CTNF 84606 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-4, 6, 9, 11-14, 16, and 19-20 are rejected under 35 U.S.C. 102( a)(1)/(a)(2 ) as being anticipated by Li (CN 109584897), hereinafter Li (referencing the provided English Translation) . With regard to claim 1, Li discloses A method for training at least one machine learning model (page 13 - collecting a large amount of audio data and training the audio separation model), the method comprising: receiving first audio data; receiving second audio data; identifying, using labels associated with segments of the first audio data, non-target background audio segments in the first audio data; identifying, using labels associated with segments of the second audio data, non-target background audio segments in the second audio data (page 14, receiving audio data streams and labeling the background sounds with sound tags); generating a first augmented training data set by replacing the identified non-target background audio segments in the first audio data with the identified non-target background audio segments in the second audio data; generating a second augmented training data set by replacing the identified non-target background audio segments in the second audio data with the identified non-target background audio segments in the first audio data (page 15-16, inserting the background sound audio data into different audio streams) ; and training at least one machine learning model using the first augmented training data set and the second augmented training data set (page 14, the audio data is collected and training the background sound classification model until the classification model is converged). With regard to claim 2, Li teaches the method of claim 1, wherein the at least one machine learning model includes a sound event detection model (page 15, obtaining the background sounds, corresponding to detecting them). With regard to claim 3, Li teaches the method of claim 1, wherein the first audio data is associated with a first domain and the second audio data is associated with a second domain (page 16, background data containing multiple background sound labels, corresponding to different domains). With regard to claim 4 Li teaches the method of claim 3, wherein the first domain is different from the second domain (page 14-15, background sounds, music, sea waves, etc., corresponding to different domains). With regard to claim 6, Li teaches the method of claim 1, wherein at least one of the first audio data and the second audio data is associated with real-world data (page 15-16, real world data such as music). With regard to claim 9, Li teaches the method of claim 1, wherein the non-target background audio segments in the first audio data and the non-target background audio segments in the second audio data are associated with at least one of non-target silence and non-target noise (page 16, judging whether there is no background audio data or background audio data, corresponding to non-target silence or noise). Claims 11-14, 16, and 19 correspond to claims 1-4, 6, and 19, and are analyzed accordingly. Claim 20 corresponds to claim 1, and is analyzed accordingly . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 7-8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Turpault (“Sound Event Detection in Domestic Environments with Weakly Labeled Data and Soundscape Synthesis), hereinafter Turpault (reference submitted by applicant via IDS) . With regard to claims 7 and 8, Li teaches the method of claim 1, however fails to teach wherein the labels associated with the segments of the first audio data includes at least one of strong labels and weak labels and wherein the labels associated with the segments of the second audio data includes at least one of strong labels and weak labels. Turpault teaches wherein the labels associated with the segments of the first audio data includes at least one of strong labels and weak labels and wherein the labels associated with the segments of the second audio data includes at least one of strong labels and weak labels (page 1, strongly labeled data and weakly labeled data). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the system of Li which teaches detecting background audio data with the teaching of Turpault of strongly and weak labeled data as known in the art techniques of labeling data based on the confidence level in order to train the model of Li with more confidence. Claims 17-18 correspond to claims 7-8, and are analyzed accordingly . 07-21-aia AIA Claim s 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Samel et al (US 2022/0335045), hereinafter Samel . With regard to claim 5, Li teaches the method of claim 1, however fails to teach wherein at least one of the first audio data and the second audio data is associated with synthetic data. Samel teaches wherein at least one of the first audio data and the second audio data is associated with synthetic data ([0057], synthetic audio data is identified). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the system of Li which teaches detecting background audio data with the teaching of Samel of synthetic data in order to train the model as disclosed by Samel in the cited section. Claim 15 corresponds to claim 5 and is analyzed accordingly . 07-21-aia AIA Claim s 10 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Padegimaite et al (CN 112368547), hereinafter Padegimaite (referenced to provided English Translation) . With regard to claim 10, Li teaches the method of claim 1, however fails to teach wherein the at least one machine learning model is associated with at least one aspect of operation of a vehicle. Padegimaite teaches detecting background audio as it pertains to a vehicle (page 6, detecting background audio in a vehicle). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the system of Li which teaches detecting background audio and training the model with the teaching of Padegimaite of detecting background data in a vehicle in order to train the model of Li for use in a vehicle application. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK D FEATHERSTONE whose telephone number is (571)270-3750. The examiner can normally be reached Monday-Friday 9:00AM - 5:00PM. 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, John Cottingham can be reached at 571-272-1400 . 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. /MARK D FEATHERSTONE/Supervisory Patent Examiner, Art Unit 2111 Application/Control Number: 18/524,950 Page 2 Art Unit: 2111 Application/Control Number: 18/524,950 Page 3 Art Unit: 2111 Application/Control Number: 18/524,950 Page 4 Art Unit: 2111 Application/Control Number: 18/524,950 Page 5 Art Unit: 2111 Application/Control Number: 18/524,950 Page 6 Art Unit: 2111 Application/Control Number: 18/524,950 Page 7 Art Unit: 2111
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