DETAILED ACTION
Claims 1-20 are pending in the current application.
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 .
Response to Arguments
Applicant’s arguments, see Remarks, filed 3/23/26, with respect to the rejection of claim 1 under 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Varma et al. (Pub. No. US 2025/0201142 A1) [0110] lines 6-19 and [0118] lines 3-13 which shows being able to use the output generated from the machine learning/ai model to keep training the machine learning model where specific output/results generated by the model if indicated as bad that output can be scrubbed/removed/deprecated from further training the machine learning model, where the determination that training data, including output should be removed can be based on a plurality of elements including determining that output data is more than a threshold number of standard deviations away from an expected value, viewed as a type of accuracy score of the output not meeting/falling outside a threshold value.
Claim Objections
Claims 1, 13 and 20 are objected to because of the following informalities: They recites in lines 16, 19 and 22 respectively “the generative artificial intelligence” it is viewed this is a typo from amendment and should recite “the generative artificial intelligence model”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
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 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (Pub. No. US 2023/0325164 A1)and further in view of Varma et al. (Pub. No. US 2025/0201142 A1).
As to claim 1, Singh discloses a method comprising: receiving a first code portion of a first programming language (Singh [0003] lines 1-4 and [0005] lines 1-16; which shows being able to process a source code snippet in a first/base programming language for translation into a second/another programming language and thus viewed that the first source code portion/snippet has been received);
converting the first code portion to a second code portion of a second programming language using a generative artificial intelligence model (Singh [0003] lines 1-4 and [0005] lines 1-16; which shows being able to translate/convert source code snippet from a first/base programming language to a second/target programming language where translation/conversion is performed using a machine learning model/generative artificial intelligence model);
converting the second code portion to a third code portion of the first programming language using the generative artificial intelligence model (Singh [0030] lines 11-21 and [0032] lines 1-6; which shows that a machine learning translation model for translating source code between programming languages, viewed as a type of generative artificial intelligence model, is able to not only translate source code from a native/first programming languages to a second/targeted programming language but can also translated back into the native/first programming language); and
calculating a translation accuracy score of converting of the first code portion to the second code portion (Singh [0047] lines 1-21, [0052] lines 1-10 and [0058] lines 1-6; which shows being able to perform plurality analysis of translated source code portion and determine a score/error value on the translated segment where the score can be based on how many warning/error raised when parsing/compiling the code based on lexical, syntax, semantical analysis and so forth or comparison to determined ground truth values to determine a score/value, viewed as a type of quality/translation accuracy score for the translated code segment and thus a type of score of the converting of the first source code to the second code portion); and
training the generative artificial intelligence model to improve a translation accuracy of the generative artificial intelligence model (Singh [0022] lines 1-4 and [0058] lines 1-7; which shows a plurality of techniques being used to train/improve translation machine learning/generative ai model for generating an accurate translation model)
Singh does not specifically disclose the specifics of wherein the training the generative artificial intelligence model comprises: when the translation accuracy score meets a value, training the generative artificial intelligence model using results of converting of the first code portion to the second code portion, and when the translation accuracy score does not meet the value, deprecating the results when training the generative artificial intelligence.
However, Varma discloses the specifics of wherein the training the generative artificial intelligence model comprises: when the translation accuracy score meets a value, training the generative artificial intelligence model using results of converting of the first code portion to the second code portion, and when the translation accuracy score does not meet the value, deprecating the results when training the generative artificial intelligence (Varma [0110] lines 6-19 and [0118] lines 3-13; which shows being able to use the output generated from the machine learning/ai model to keep training the machine learning model where specific output/results generated by the model if indicated as bad that output can be scrubbed/removed/deprecated from further training the machine learning model, where the determination that training data, including output should be removed can be based on a plurality of elements including determining that output data is more than a threshold number of standard deviations away from an expected value, viewed as a type of accuracy score of the output not meeting/falling outside a threshold value being able to remove/scrub/deprecating the output results from training and thus viewed that when the output does meet/fall within the threshold value those output results are used in training the machine learning model, where the specifics of the generating artificial intelligence model for converting of the first code portion to the second code portion and the specifics of calculating a translation accuracy score are seen in the teachings of Singh above and together are viewed as showing wherein the training the generative artificial intelligence model comprises: when the translation accuracy score meets a value, training the generative artificial intelligence model using results of converting of the first code portion to the second code portion, and when the translation accuracy score does not meet the value, deprecating the results when training the generative artificial intelligence)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Varma showing the specifics of conditionally using output from a machine learning model to further train the machine learning model into the training of a translation machine learning model of Singh for the purpose of improving the quality of the machine learning model by being able to determine a remove poor quality data from use in the machine learning model training, as taught by Varma [0118] lines 3-17.
As to claim 11, Singh discloses wherein the generative artificial intelligence model comprises a large language model (Singh [0032] lines 1-9 and [0033] lines 1-3; which shows that the translation model can include large language model).
As to claim 12, Singh discloses further comprising determining that the first code portion and the second code portion have a same functionality (Singh [0047] lines 1-21, [0052] lines 1-10 and [0058] lines 1-6; which shows that the analysis of the translated code portion to determine accuracy/error includes a plurality of analysis that include comparison of translated portion to ground truth and semantic analysis of code, viewed as analysis that shows function/meaning/behavior of code thus viewed as being able to determine if the first and second code portion have the same functionality/are the same semantically).
As to claim 13, Singh discloses an apparatus comprising: a processing device (Singh [0059] lines 3-6); and
memory operatively coupled to the processing device, wherein the memory stores computer program instructions that, when executed, cause the processing device to (Singh [0063] lines 1-7):
The remaining claim limitations are comparable to claim 1 above and rejected under the same reasoning.
Claims 2-4, 9, 14-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Singh and Varma as applied to claims 1 and 13 above, and further in view of Kashiwagi et al. (Pub. No. US 2016/0378445 A1).
As to claims 2 and 14, Singh as modified by Varma does not specifically disclose further comprising: calculating a first complexity score for the first code portion; calculating a second complexity score for the third code portion; and calculating the translation accuracy score of the converting of the first code portion to the second code portion based on a comparison of the first complexity score and the second complexity score.
However, Kashiwagi discloses further comprising: calculating a first complexity score for the first code portion (Kashiwagi [0076] lines 1-7; which shows as part of source code analysis being able to extract metrics from the portion/function of source code where the metrics extracted/determined from source code include complexity values/score, where the specifics of the first source code is seen disclosed above in the teachings of Singh);
calculating a second complexity score for the third code portion (Kashiwagi [0076] lines 1-7; which shows as part of source code analysis being able to extract metrics from the portion/function of source code where the metrics extracted/determined from source code include complexity values/score, where the specifics of the third source code portion, source code translated from a first to a second language and then translated back to the first source code programming language, are seen disclosed above in the teachings of Singh); and
calculating the translation accuracy score of the converting of the first code portion to the second code portion based on a comparison of the first complexity score and the second complexity score (Kashiwagi [0076] lines 1-7 and [0139] lines 1-8; which shows being able to compare source code functions and their determined complexity metric/score to determining how similar the two source code functions are based on a comparison of their complexity values, that in light of the teachings of Singh above showing calculating of translation accuracy value/score by code analysis and comparison of code can together be viewed as being able to determine complexity metric/score of first source code and third source code as part of code analysis code and comparing translated code to ground truth/first code portion to determine a type of similarity/translation accuracy score/value/error/metric for those code portions and thus together viewed as showing calculating the translation accuracy score of the converting of the first code portion to the second code portion based on a comparison of the first complexity score and the second complexity score).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kashiwagi showing the specifics of determining and comparing complexity values of source code to determine similarity between code portions into the determination of similarity of translated code portions of Singh as modified by Varma for the purpose of improving code analysis to determine similarity of code elements for additional characteristics and thus having a more accurate determination of code similarity, as seen in Kashiwagi [0018] lines 1-5 and [0139] lines 1-8.
As to claims 3 and 15, Singh as modified by Varma does not specifically disclose, however, Kashiwagi discloses wherein calculating the first complexity score for the first code portion further comprises calculating the first complexity score for the first code portion based on one or more complexity metrics (Kashiwagi [0076] lines 1-7 and [0139] lines 1-8; which shows being able to extract metrics from the source code including complexity metric where the metrics values themselves are viewed as the complexity score/value as compared to determine similarity based on one complexity metric where the specifics of the first source code is seen disclosed above in the teachings of Singh above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kashiwagi showing the specifics of determining and comparing complexity values of source code to determine similarity between code portions into the determination of similarity of translated code portions of Singh as modified by Varma for the purpose of improving code analysis to determine similarity of code elements for additional characteristics and thus having a more accurate determination of code similarity, as seen in Kashiwagi [0018] lines 1-5 and [0139] lines 1-8.
As to claims 4 and 16, Singh as modified by Varma does not specifically disclose, however, Kashiwagi discloses wherein calculating the second complexity score for the third code portion further comprises calculating the second complexity score for the third code portion based on the one or more complexity metrics (Kashiwagi [0076] lines 1-7 and [0139] lines 1-8; which shows being able to extract metrics from the source code including complexity metric where the metrics values themselves are viewed as the complexity score/value as compared to determine similarity based on one complexity metric, where the specifics of the third source code portion, source code translated from a first to a second language and then translated back to the first source code programming language, are seen disclosed above in the teachings of Singh).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kashiwagi showing the specifics of determining and comparing complexity values of source code to determine similarity between code portions into the determination of similarity of translated code portions of Singh as modified by Varma for the purpose of improving code analysis to determine similarity of code elements for additional characteristics and thus having a more accurate determination of code similarity, as seen in Kashiwagi [0018] lines 1-5 and [0139] lines 1-8.
As to claim 9, Singh discloses wherein calculating the translation accuracy score of the converting of the first code portion to the second code portion based on the comparison of the first complexity score and the second complexity score further comprises: calculating the translation accuracy score based on the difference between the first complexity score and the second complexity score (Singh [0047] lines 1-21, [0052] lines 1-10 and [0058] lines 1-6; which shows being able to perform plurality analysis/comparison of translated source code portion and determine a score/error value on the translated segment where the score/value can be based on the comparison or translated source code to know truth values viewed as a type of similarity/translation accuracy score, thus in light of the teachings of Kashiwagi below showing the specifics of calculating similarity/difference between complexity metrics for individual code portions to determine the similarity between the code portions can together be viewed as showing calculating the translation accuracy score based on the difference between the first complexity score and the second complexity score).
Singh as modified by Varma does not specifically disclose calculating a difference between the first complexity score and the second complexity score.
However, Kashiwagi discloses calculating a difference between the first complexity score and the second complexity score (Kashiwagi [0076] lines 1-7 and [0139] lines 1-8; which shows being able to calculate a complexity metric/score for source code portion and being able to compare the calculated complexity values for source code to determine similarity/difference between those complexity metric values).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kashiwagi showing the specifics of determining and comparing complexity values of source code to determine similarity between code portions into the determination of similarity of translated code portions of Singh as modified by Varma for the purpose of improving code analysis to determine similarity of code elements for additional characteristics and thus having a more accurate determination of code similarity, as seen in Kashiwagi [0018] lines 1-5 and [0139] lines 1-8.
As to claim 20, Singh discloses a computer program product comprising a computer readable storage medium, wherein the computer readable storage medium comprises computer program instructions that, when executed: receive a first code portion of a first programming language (Singh [0003] lines 1-4, [0005] lines 1-16 and [0011] lines 6-10; which shows being able to process a source code snippet in a first/base programming language for translation into a second/another programming language and thus viewed that the first source code portion/snippet has been received);
convert the first code portion to a second code portion of a second programming language by a generative artificial intelligence model (Singh [0003] lines 1-4 and [0005] lines 1-16; which shows being able to translate/convert source code snippet from a first/base programming language to a second/target programming language where translation/conversion is performed using a machine learning model/generative artificial intelligence model);
convert the second code portion to a third code portion of the first programming language by the generative artificial intelligence model (Singh [0030] lines 11-21 and [0032] lines 1-6; which shows that a machine learning translation model for translating source code between programming languages, viewed as a type of generative artificial intelligence model, is able to not only translate source code from a native/first programming languages to a second/targeted programming language but can also translated back into the native/first programming language);
calculate a translation accuracy score of the converting of the first code portion to the second code portion based on a comparison of values. (Singh [0047] lines 1-21, [0052] lines 1-10 and [0058] lines 1-6; which shows being able to perform plurality analysis of translated source code portion and determine a score/error value on the translated segment where the score can be based on how many warning/error raised when parsing/compiling the code based on lexical, syntax, semantical analysis and so forth or comparison to determined ground truth values to determine a score/value, viewed as a type of quality/translation accuracy score for the translated code segment and thus a type of translation accuracy score of the converting of the first source code to the second code portion, where the specifics of the similarity value/difference/errors between the first and second code portion being the specifics of compared complexity values of a first and second code segment are seen specifically disclosed in the teachings of Kashiwagi below and together would disclose calculate a translation accuracy score of the converting of the first code portion to the second code portion based on a comparison of the first complexity score and the second complexity score);
train the generative artificial intelligence model to improve a translation accuracy of the generative artificial intelligence model (Singh [0022] lines 1-4 and [0058] lines 1-7; which shows a plurality of techniques being used to train/improve translation machine learning/generative ai model for generating an accurate translation model
Singh does not specifically disclose the specifics of wherein to train the generative artificial intelligence model, the computer program instructions are executed to: when the translation accuracy score meets a value, train the generative artificial intelligence model using results of converting of the first code portion to the second code portion, and when the translation accuracy score does not meet the value, deprecating the results when training the generative artificial intelligence.
However, Varma discloses the specifics of wherein to train the generative artificial intelligence model, the computer program instructions are executed to: when the translation accuracy score meets a value, train the generative artificial intelligence model using results of converting of the first code portion to the second code portion, and when the translation accuracy score does not meet the value, deprecating the results when training the generative artificial intelligence (Varma [0110] lines 6-19 and [0118] lines 3-13; which shows being able to use the output generated from the machine learning/ai model to keep training the machine learning model where specific output/results generated by the model if indicated as bad that output can be scrubbed/removed/deprecated from further training the machine learning model, where the determination that training data, including output should be removed can be based on a plurality of elements including determining that output data is more than a threshold number of standard deviations away from an expected value, viewed as a type of accuracy score of the output not meeting/falling outside a threshold value being able to remove/scrub/deprecating the output results from training and thus viewed that when the output does meet/fall within the threshold value those output results are used in training the machine learning model, where the specifics of the generating artificial intelligence model for converting of the first code portion to the second code portion and the specifics of calculating a translation accuracy score are seen in the teachings of Singh above and together are viewed as showing wherein the training the generative artificial intelligence model comprises: when the translation accuracy score meets a value, training the generative artificial intelligence model using results of converting of the first code portion to the second code portion, and when the translation accuracy score does not meet the value, deprecating the results when training the generative artificial intelligence)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Varma showing the specifics of conditionally using output from a machine learning model to further train the machine learning model into the training of a translation machine learning model of Singh for the purpose of improving the quality of the machine learning model by being able to determine a remove poor quality data from use in the machine learning model training, as taught by Varma [0118] lines 3-17.
Singh as modified by Varma does not specifically disclose calculate a first complexity score for the first code portion; calculate a second complexity score for the third code portion; and calculating the translation accuracy score of the converting of the first code portion to the second code portion based on a comparison of the first complexity score and the second complexity score.
However, Kashiwagi discloses calculate a first complexity score for the first code portion (Kashiwagi [0076] lines 1-7; which shows as part of source code analysis being able to extract metrics from the portion/function of source code where the metrics extracted/determined from source code include complexity values/score, where the specifics of the first source code is seen disclosed above in the teachings of Singh);
calculate a second complexity score for the third code portion (Kashiwagi [0076] lines 1-7; which shows as part of source code analysis being able to extract metrics from the portion/function of source code where the metrics extracted/determined from source code include complexity values/score, where the specifics of the third source code portion, source code translated from a first to a second language and then translated back to the first source code programming language, are seen disclosed above in the teachings of Singh); and
calculating the translation accuracy score of the converting of the first code portion to the second code portion based on a comparison of the first complexity score and the second complexity score (Kashiwagi [0076] lines 1-7 and [0139] lines 1-8; which shows being able to compare source code functions and their determined complexity metrics/scores to determining how similar the two source code functions are based on a comparison of their complexity values, viewed as a type of similarity score from code analysis that in light of the teachings of Singh above showing calculating of translation accuracy/similarity/difference/error value/score by code analysis associated with comparison of translated code and ground truth code/first portion can together be viewed as being able to determine complexity metric/score of first source code and third source code as part of code analysis code and comparing translated code to ground truth/first code portion to determine a type of similarity/translation accuracy score/value/error/metric for those code portions and thus together viewed as showing calculating the translation accuracy score of the converting of the first code portion to the second code portion based on a comparison of the first complexity score and the second complexity score).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kashiwagi showing the specifics of determining and comparing complexity values of source code to determine similarity between code portions into the determination of similarity of translated code portions of Singh as modified by Varma for the purpose of improving code analysis to determine similarity of code elements for additional characteristics and thus having a more accurate determination of code similarity, as seen in Kashiwagi [0018] lines 1-5 and [0139] lines 1-8.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Singh, Varma and Kashiwagi as applied to claims 3 above, and further in view of Kadam (Pub. No. US 2019/0079759 A1).
As to claim 5, Singh as modified by Varma and Kashiwagi do not specifically disclose wherein the one or more complexity metrics include one or more of a cyclomatic complexity metric, a Halstead metric, a live variable metric, a knot count metric, an ultrametric topology metric, and an abstract syntax tree metric.
However, Kadam discloses wherein the one or more complexity metrics include one or more of a cyclomatic complexity metric, a Halstead metric, a live variable metric, a knot count metric, an ultrametric topology metric, and an abstract syntax tree metric (Kadam [0042] lines 11-21; which shows the evaluation of source code can be based on a plurality of metrics including metric for evaluation of source code base on cyclomatic complexity metric and thus showing that complexity metric can include at least cyclomatic complexity metric as claimed).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kadam showing the specifics of being able to evaluate source code according to a cyclomatic complexity metric into the evaluation of source code to determine complexity of Singh as modified by Varma and Kashiwagi for the purpose of increasing the adaptability of the evaluation of source code so that it can take into account additional metrics in its evaluation and thus have a more accurate analysis of the source code as taught by Kadam [0002] lines 38-45 and [0042] lines 11-21
Claims 6-7 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Singh, Varma and Kashiwagi as applied to claims 3 and 15 above, and further in view of Sharma (Pub. No. US 2013/0311968 A1)
As to claims 6 and 17 Sing as modified by Varma and Kashiwagi do not specifically disclose wherein calculating the first complexity score for the first code portion further comprises calculating the first complexity score for the first code portion based on a combination of a plurality of the one or more complexity metrics.
However, Sharma discloses wherein calculating the first complexity score for the first code portion further comprises calculating the first complexity score for the first code portion based on a combination of a plurality of the one or more complexity metrics (Sharma [0053] lines 1-9 and [0055] lines 1-7; which shows that the determine complexity metrics for the source code of the set/plurality of code complexity metrics can be combined together for a larger view, viewed as a type of combined complexity score).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Sharma showing the specifics of combining complexity metrics of source code to create a combined complexity view/score of the source code, into the source code complexity determination of source code in Singh as modified by Varma and Kashiwagi for the purpose of being able to generate a larger more detailed view of the source code project as taught by Sharm [0055] lines 1-7.
As to claims 7 and 18 Singh as modified by Varma and Kashiwagi do not specifically disclose, however, Sharma discloses wherein calculating the first complexity score for the first code portion further comprises calculating the first complexity score for the first code portion based on a weighted combination of a plurality of the one or more complexity metrics (Sharma [0053] lines 1-9, [0055] lines 1-7, [0094] lines 1-6 and [0095] lines 1-8; which shows being able to determine complexity metrics of source code and combine them together to create an overall view, viewed as a type of complexity score based on the plurality of complexity metric where the collected metrics can also be further processed and fed back into the modeling system and can be weighted by age thus viewed that complexity metrics can be fed back and weighted by age when combined to generate the larger view including the weighted collected complexity metrics).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Sharma showing the specifics of combining complexity metrics of source code to create a combined complexity view/score of the source code, into the source code complexity determination of source code in Singh as modified by Varma and Kashiwagi for the purpose of being able to generate a larger more detailed view of the source code project as taught by Sharm [0055] lines 1-7.
Claims 8, 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Singh and Varma as applied to claims 1 and 13 above, and further in view of Singh2 et al. (Patent No. US 11,693,637 B1).
As to claims 8 and 19 Singh as modified by Varma does not specifically disclose further comprising updating the generative artificial intelligence model based on the translation accuracy score.
However, Singh2 discloses further comprising updating the generative artificial intelligence model based on the translation accuracy score (Singh2 Col. 2 lines 5-8, Col. 21 lines 11-26 and Col. 22 lines 10-17; which shows the machine learning/ai models uses in the translation of source code from one programming language to another include base/programming language to natural language models and natural language to target/programming language where these models are updated based on comparison between predicted/output code snippet and ground truth snippet to determine loss, viewed as accuracy of translation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Singh2 showing the ability to train and update the machine learning model used in translation of source code, into the source code translation using machine learning of Singh as modified by Varma for the purpose of improving the translation model until it meets specific performance conditions and thus meeting desired performance, as taught by Singh2 Col. 22 lines 24-30.
As to claim 10, Singh as modified by Varma does not specifically disclose, however, Singh2 discloses further comprising indicating that the translation accuracy score is outside of an acceptable tolerance (Singh2 Col. 15 lines 15-26 and Col. 23 lines 40-53; which shows being able to determine if the generated/translated source code does not meet/satisfy evaluation conditions/outside acceptable tolerance will generated further/additional source code translations, viewed as type of translation accuracy seen specifically disclosed in Singh above, where by generating additionally source code translation snippets acts as the type of indication).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Singh2 showing the ability to train and update the machine learning model used in translation of source code, into the source code translation using machine learning of Singh as modified by Varma for the purpose of improving the translation model until it meets specific performance conditions and thus meeting desired performance, as taught by Singh2 Col. 22 lines 24-30.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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