CTNF 18/279,595 CTNF 100516 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. Priority Application is a continuation of PCT Application No. PCT/JP2021/007627, filed on March 01, 2021. Information Disclosure Statement The information disclosure statement (IDS) submitted on 08/30/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 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. 07-15-03-aia AIA Claim(s) 1 , 3 , 6 , and 9 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang et al. Pub. No.: US 20210241177 A1 . Regarding Claim 1 Wang teaches A learning device comprising a processor configured to execute operations comprising: generating a new training data set on a basis of attribute information of an existing training data set; (Wang, Paragraph 0035-0036, teaches the generating of a new training data set that comprised the attribute data, the training data sets are used to train a sequence of model continuously so the first model will have training data then the next model will be trained with that training data in addition to the new training data to create a new model, so the training attribute data from the existing training data set is used to help create and gather data for the next data set. ) and new model by additionally learning the new training data set for an existing model. ( Wang, Paragraph 0035-0036, teaches the training of a new model that was trained off an existing model that then gets additional data from a new data set and is retrained to create a new model.) Regarding Claim 3 Wang teaches The learning device according to claim 1, wherein the generating the new training data set further comprises adding training data having a same attribute as that of the existing training data set to the new training data set, (Wang, paragraph 0038, teaches a selecting rule that determines which data is incorporated into the new training data this selection rule can be set so that only data with the same attribute as the original training data set is included in the new dataset. ) and the creating the new model further comprises creating the new model by additionally learning new training data to which training data having a same attribute as that of the existing training data set is added for the existing model. ( Wang, Paragraph 0035-0036, teaches the training of a new model that was trained off an existing model that then gets additional data from a new data set and is retrained to create a new model.) Regarding Claim 6 Wang teaches A method for learning a new model the method comprising: generating a new training data set on a basis of attribute information of an existing training data set; (Wang, Paragraph 0035-0036, teaches the generating of a new training data set that comprised the attribute data, the training data sets are used to train a sequence of model continuously so the first model will have training data then the next model will be trained with that training data in addition to the new training data to create a new model, so the training attribute data from the existing training data set is used to help create and gather data for the next data set. ) and creating the new model by additionally learning the new training data set for an existing model. ( Wang, Paragraph 0035-0036, teaches the training of a new model that was trained off an existing model that then gets additional data from a new data set and is retrained to create a new model.) Regarding Claim 9 Wang teaches The method according to The method according to wherein the generating the new training data set further comprises adding training data having a same attribute as that of the existing training data set to the new training data set, (Wang, paragraph 0038, teaches a selecting rule that determines which data is incorporated into the new training data this selection rule can be set so that only data with the same attribute as the original training data set is included in the new dataset. ) and the creating the new model further comprises creating the new model by additionally learning new training data to which training data having a same attribute as that of the existing training data set is added for the existing model. ( Wang, Paragraph 0035-0036, teaches the training of a new model that was trained off an existing model that then gets additional data from a new data set and is retrained to create a new model.) 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) 2 , 4-5, . 8 , and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. Pub. No.: US 20210241177 A1 in view of Xiang et al. “Incremental Few-Shot Learning for Pedestrian Attribute Recognition” 2019 . Regarding Claim 2 Wang teaches The learning device according to claim 1, wherein the generating the new training data set further comprises dividing the new training data set ( Wang, Paragraph 0119, teaches the ability of the model training system to divide the data into different groups of training data for training and validation ) Wang does not teach into a plurality of divided data sets on a basis of the attribute information of the existing training data set, However, Xiang in analogous art teaches this limitation (Xiang, page 5, section 5.1, teaches the splitting of a training data set based on the attribute information of the dataset ) Further Wang does not teach and the creating the new model further comprises additionally learning one divided data set among the plurality of divided data sets for a learning target model using the existing model as the learning target model, and repeating the learning of the learning target model 1 until all divided data sets of the plurality of divided datasets are learned (Xiang, page 3-5, section 4, teaches a method of training in which a trained model is used as a base model (i.e. learning target model) and is trained episodically on a series of divided data sets one by one until all the divided data sets are learned by the new model.) It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Xiang’s teaching of splitting up datasets and training on them iteratively with Wang’s teaching of splitting datasets based on the attribute data. The motivation to do so would be to improve the accuracy of the model by slowly learning the different attributes one by one rather than learning them all at once. Regarding Claim 4 the combination of Wang and Xiang teaches The learning device according to claim 2, wherein generating the new training data set further comprises adding training data having a same attribute as that of the existing training data set to each of the plurality of divided data sets, ( Wang, Paragraph 0119, teaches the ability of the model training system to divide the data into different groups of training data for training and validation. Further Wang, Paragraph 0035, teaches the adding of new training data with similar attributes to the data that is in the previous training dataset. ) the creating the new model further comprises additionally learning one divided data set among the plurality of divided data sets to which the training data has been added for the learning target model using the existing model as the learning target model, (Xiang, page 5, section 5.1, teaches the splitting of a training data set based on the attribute information of the dataset ) and repeating the learning of the learning target model until all the divided data sets are learned and the generating the new training data comprises adding training data having a same attribute as that of a divided data set learned before the divided data set to the corresponding divided data set. (Xiang, page 3-5, section 4, teaches a method of training in which a trained model is used as a base model (i.e. learning target model) and is trained episodically on a series of divided data sets one by one until all the divided data sets are learned by the new model.) Regarding Claim 5 Wang teaches A learning device comprising a processor configured to execute operations comprising: evaluating: a first model created by collectively performing additional learning of a new training data set for an existing model according to attribution information of an existing training data set, (Wang, Paragraph 0035-0036, teaches the generating of a new training data set that comprised the attribute data, the training data sets are used to train a sequence of model continuously so the first model will have training data then the next model will be trained with that training data in addition to the new training data to create a new model, so the training attribute data from the existing training data set is used to help create and gather data for the next data set. Wang, Paragraph 0035-0036, teaches the training of a new model that was trained off an existing model that then gets additional data from a new data set and is retrained to create a new model.) ) a second model created by; dividing the new training data set into a plurality of divided data sets based on the attribute information of an existing training data set, ( Wang, Paragraph 0119, teaches the ability of the model training system to divide the data into different groups of training data for training and validation ) 1 … a third model created by; adding the training data having a same attribute as that of the existing training data set to each of the plurality of divided data sets, (Wang, paragraph 0038, teaches a selecting rule that determines which data is incorporated into the new training data this selection rule can be set so that only data with the same attribute as the original training data set is included in the new dataset. ) and additionally learning the new training data to which the training data having the same attribute as that of the existing training data set is added for the existing model, ( Wang, Paragraph 0035-0036, teaches the training of a new model that was trained off an existing model that then gets additional data from a new data set and is retrained to create a new model.) a fourth model created by; adding the training data having the same attribute as that of the existing training data set to each of the plurality of divided data sets, ( Wang, Paragraph 0119, teaches the ability of the model training system to divide the data into different groups of training data for training and validation. Further Wang, Paragraph 0035, teaches the adding of new training data with similar attributes to the data that is in the previous training dataset) 2 … Wang does not teach 1 … additionally learning one divided data set among the plurality of divided data sets for a learning target model, However, Xiang in analogous art teaches this limitation (Xiang, page 5, section 5.1, teaches the splitting of a training data set based on the attribute information of the dataset ) Further Wang does not teach and repeating the learning of the learning target model until all divided data sets of the plurality of divided data sets are learned, However, Xiang in analogous art teaches this limitation (Xiang, page 3-5, section 4, teaches a method of training in which a trained model is used as a base model (i.e. learning target model) and is trained episodically on a series of divided data sets one by one until all the divided data sets are learned by the new model.)… Further Wang does not teach 2 … additionally learning the one divided data set among the plurality of divided data sets to which the training data has been added for the learning target model using the existing model as the learning target model, However, Xiang in analogous art teaches this limitation (Xiang, page 5, section 5.1, teaches the splitting of a training data set based on the attribute information of the dataset ) Further Wang does not teach and repeating the learning of the learning target model until all the divided data sets are learned; and the learning device according repeating the learning of the learning target model until all the divided data sets are learned; and However, Xiang in analogous art teaches this limitation (Xiang, page 3-5, section 4, teaches a method of training in which a trained model is used as a base model (i.e. learning target model) and is trained episodically on a series of divided data sets one by one until all the divided data sets are learned by the new model.) Further Wang does not teach determining, based on at least one of the first model, the second model, the third model, or the fourth model, a new model according to a result from the evaluating. However, Xiang in analogous art teaches this limitation (Xiang, page 5-6, section 5, teaches the evaluating of multiple models performing the same task to determine the best one based on the results) It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Xiang’s teaching of splitting up datasets and training on them iteratively with Wang’s teaching of splitting datasets based on the attribute data. The motivation to do so would be to improve the accuracy of the model by slowly learning the different attributes one by one rather than learning them all at once. Regarding Claim 8 Wang teaches The method according to The method according to wherein the generating the new training data set further comprises dividing the new training data set ( Wang, Paragraph 0119, teaches the ability of the model training system to divide the data into different groups of training data for training and validation ) Wang does not teach into a plurality of divided data sets on a basis of the attribute information, However, Xiang in analogous art teaches this limitation (Xiang, page 5, section 5.1, teaches the splitting of a training data set based on the attribute information of the dataset ) Further Wang does not teach and the creating the new model further comprises additionally learning one divided data set among the plurality of divided data sets for a learning target model using the existing model as the learning target model, and repeating the learning of the learning target model until all divided data sets of the plurality of divided data sets are learned However, Xiang in analogous art teaches this limitation (Xiang, page 3-5, section 4, teaches a method of training in which a trained model is used as a base model (i.e. learning target model) and is trained episodically on a series of divided data sets one by one until all the divided data sets are learned by the new model.) It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Xiang’s teaching of splitting up datasets and training on them iteratively with Wang’s teaching of splitting datasets based on the attribute data. The motivation to do so would be to improve the accuracy of the model by slowly learning the different attributes one by one rather than learning them all at once. Regarding Claim 10 Wang teaches The method according to The method according to wherein generating the new training data set further comprises adding the training data having a same attribute as that of the existing training data set to each of the plurality of divided data sets, ( Wang, Paragraph 0119, teaches the ability of the model training system to divide the data into different groups of training data for training and validation. Further Wang, Paragraph 0035, teaches the adding of new training data with similar attributes to the data that is in the previous training dataset. ) Wang does not teach the creating the new model further comprises additionally learning one divided data set among the plurality of divided data sets to which the training data has been added for a learning target model using the existing model as the learning target model, However, Xiang in analogous art teaches this limitation (Xiang, page 5, section 5.1, teaches the splitting of a training data set based on the attribute information of the dataset ) Further Wang does not teach and repeating the learning of the learning target model until all divided data sets of the plurality of divided data sets are learned, and the generating the new training data further comprises adding training data having a same attribute as that of a divided data set learned before the divided data set to the corresponding divided data set. However, Xiang in analogous art teaches this limitation (Xiang, page 3-5, section 4, teaches a method of training in which a trained model is used as a base model (i.e. learning target model) and is trained episodically on a series of divided data sets one by one until all the divided data sets are learned by the new model.) It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Xiang’s teaching of splitting up datasets and training on them iteratively with Wang’s teaching of splitting datasets based on the attribute data. The motivation to do so would be to improve the accuracy of the model by slowly learning the different attributes one by one rather than learning them all at once. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS B LANE whose telephone number is (571)272-1872. The examiner can normally be reached M-Th: 6:40am-4:40pm; F: Out of Office. 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, MARIELA REYES can be reached at (571) 270-1006. 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. /THOMAS BERNARD LANE/ Examiner, Art Unit 2142 /HAIMEI JIANG/ Primary Examiner, Art Unit 2142 Application/Control Number: 18/279,595 Page 2 Art Unit: 2142 Application/Control Number: 18/279,595 Page 3 Art Unit: 2142 Application/Control Number: 18/279,595 Page 4 Art Unit: 2142 Application/Control Number: 18/279,595 Page 5 Art Unit: 2142 Application/Control Number: 18/279,595 Page 6 Art Unit: 2142 Application/Control Number: 18/279,595 Page 7 Art Unit: 2142 Application/Control Number: 18/279,595 Page 8 Art Unit: 2142 Application/Control Number: 18/279,595 Page 9 Art Unit: 2142 Application/Control Number: 18/279,595 Page 10 Art Unit: 2142 Application/Control Number: 18/279,595 Page 11 Art Unit: 2142 Application/Control Number: 18/279,595 Page 12 Art Unit: 2142
Prosecution Insights