DETAILED ACTION
This office action is responsive to amendment filed on September 14, 2026 in this application Merchan et al., U.S. Patent Application No. 18/646,219 (Filed April 25, 2024) (“Merchan”). Claims 1 – 21 were pending. Claims 1 and 10 – 21 are amended. Claims 1 - 21 are pending.
Applicants' arguments have been carefully and respectfully considered and found not persuasive. Accordingly, this action has been made FINAL.
Response to Arguments
1. With respect to Applicant’s argument on pg. 8 of the Applicant’s Remarks (“Remarks”) that the amendment overcome the rejections made under 35 USC 112(b), examiner respectfully disagrees. See infra § Claim Rejections - 35 USC §112(b). The amendment did not alter or clarify the interpretation of whether “at least one of the code of the first software program and the code of the second software program” means the first or the second codes, or if it is
intended to mean at least one of the first software program codes. Therefore, the claims remain rejected under 35 USC 112(b).
2. With respect to Applicant’s argument on pgs. 8 - 10 of the Remarks stating that Chen fails to teach the code of the first software program and the newly added limitations, examiner respectfully disagrees in part, however in the interests of compact prosecution prior art reference G Rao is added which teaches these limitations and therefore this argument is moot. See infra § Claim Rejections - 35 USC §103 § Claim 1. G Rao teaches programs in a first programming language may be translated by a machine learning model into a second programming language as well as having the code optimized as part of the translation. G Rao at Abstract; id at col. 2 ll. 6 – 9; id. at col. 4 ll. 22 – 43 (program algorithms may be optimized).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 – 21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites various instances of “at least one of the code of the first software program and the code of the second software program.” It is unclear if these are intended to mean the first or the second codes, or if they are intended to mean at least one of the first software program codes.
Claims 16 and 21 are rejected for substantially similar reasons. Claim 2 – 15 and 17 – 20 are rejected as depending on claim 1 and 16 respectively.
Claim Rejections 35 U.S.C. §103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 9 and 11 - 21 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al., United States Patent Application Publication No. 20200042298 (Published February 6, 2020, filed August 2, 2018) (“Jain”) in view of Chen et al., United States Patent Application Publication No. 2024/0020116 (Published January 18, 2024, filed May 23, 2023) (“Chen”) and G Rao et al., United States Patent No. 11,693,641 (Patented July 4, 2023, filed March 22, 2022) (“G Rao”).
Claims 1, 16, and 21
With respect to claims 1, 16, and 21, Jain teaches the invention as claimed including a method for generating redundant code, the method comprising / an electronic control unit of....:
receiving a first software program, the first software program comprising code that, when executed, causes at least one function to be performed; receiving a test suite configured to verify functionality of the at least one function; …the second software program comprising code that, when executed, causes the at least one function to be performed, wherein the code of the second software program is different from the code of the first software program; using the test suite to verify functionality of the at least one function of the second software program; in response to verifying the at least one function of the second software program, storing, in memory associated with at least one controller, the first software program and the second software program, wherein the at least one controller executes at least one of the code of the first software program and the code of the second software program to cause the at least one function to be performed; and in response to detecting a / cyber security / fault in the either the code of the first software program or the code of the second software program: disabling, using the at least one controller, execution of the at least one of the code of the first software program and the code of the second software program; and executing, by the at least one controller, the other of the at least one of the code of the first software program and the code of the second software program to cause the at least one function to be performed. {First and updated versions of a software code may be tested, one version may be deployed and executing, and when a fault is detected the executing version may be rolled-back to the other version. Jain at ¶ 0103 (testing); id. at ¶¶ 0118, 0122, 0125 (receiving updated version, testing the updated version, and executing the updated version); id. at ¶¶ 0102 & 0089 - 0090 (develop second version in response to testing and errors in first version and test second version prior to enabling the new version); id. at ¶ 0086 (fault in executing version of code may trigger rollback to other version [may be first or second version]).}
However, Jain doesn’t explicitly teach the limitation:
a vehicle / providing, to an artificial intelligence engine, …receiving, from the artificial intelligence engine, a second software program, {Chen does teach this limitation. Chen teaches that rolling back to a previous software version and shifting responsibility for call response back to that previously version, as taught in Jain, may include where a trained machine learning model receives a first software program such as a natural language code docstring and uses it to generate a second software program code, which may be verified via testing. Chen at ¶¶ 0080 – 0082; ¶ 0046 (unit test); id. at ¶ 0053 (integration testing); id. at ¶ 0045 (vehicle).
Jain and Chen are analogous art because they are from the “same field of endeavor” and are both from the same “problem-solving area.” Specifically, they are both from the field of software deployment, and both are trying to solve the problem of how to deploy a new version of a software code.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine version testing, deployment, and roll-back, as taught in Jain, with using AI to generate the new version, as taught in Chen. Chen teaches that using natural language processing increases “efficiency and accuracy.” Id. at ¶ 0003. Therefore, one having ordinary skill in the art would have been motivated to use the known technique of generating a new software version using AI, as taught in Chen, with a method of creating and deploying a new software version, as taught in Jain, for the purpose of improving the efficiency and accuracy of the deployment process.}
However, Jain and Chen doesn’t explicitly teach the limitation:
the code of the first software program; …generated by the artificial intelligence engine …in that at least one function using a different algorithmic approach from the code of the second software program; {G Rao does teach this limitation. G Rao teaches that using a machine learning model to translate program code, as taught in Jain and Chen, may include where programs in a first programming language may be translated by a machine learning model into a second programming language as well as having the code optimized as part of the translation. G Rao at Abstract; id at col. 2 ll. 6 – 9; id. at col. 4 ll. 22 – 43 (program algorithms may be optimized).
Jain, Chen, and G Rao are analogous art because they are from the “same field of endeavor” and are both from the same “problem-solving area.” Specifically, they are both from the field of software deployment, and both are trying to solve the problem of how to deploy a new version of a software code.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine using a machine learning model to translate program code, as taught in Jain and Chen, with code language translation and optimization, as taught in G Rao. G Rao teaches that alterations are required to perform the code translation. Id. at col. 1 ll. 7 - 17. Therefore, one having ordinary skill in the art would have been motivated to combine using a machine learning model to translate program code, as taught in Jain and Chen, with code language translation and optimization, as taught in G Rao, for the purpose of performing optimization required to perform the code translation.}
Claims 2 and 17
With respect to claims 2 and 17, Jain, Chen, and G Rao teach the invention as claimed including:
wherein the test suite includes at least one of at least one function test, at least one integration test, and at least one safety test. {A trained machine learning model receives a first software program such as a natural language code docstring and uses it to generate a second software program code, which may be verified via testing. Chen at ¶¶ 0080 – 0082; ¶ 0046 (unit test); id. at ¶ 0053 (integration testing); id. at ¶ 0045 (vehicle).}
Claim 3
With respect to claim 3, Jain, Chen, and G Rao teach the invention as claimed including:
wherein the artificial intelligence engine is configured to use at least one machine learning model trained to generate software programs. {A trained machine learning model receives a first software program such as a natural language code docstring and uses it to generate a second software program code, which may be verified via testing. Chen at ¶¶ 0080 – 0082; ¶ 0046 (unit test); id. at ¶ 0053 (integration testing); id. at ¶ 0045 (vehicle).}
Claim 4
With respect to claim 4, Jain, Chen, and G Rao teach the invention as claimed including:
wherein the at least one machine learning model is trained using a training set comprising data associated with known snippets of vulnerable code and fixed code derived from vulnerability databases. {A machine learning model trained using code samples receives a first software program such as a natural language code docstring and uses it to generate a second software program code, which may be verified via testing. Chen at ¶¶ 0080 – 0082; ¶ 0046 (unit test); id. at ¶ 0053 (integration testing); id. at ¶ 0045 (vehicle); ¶¶ 0053 & 0086; id. at ¶ 0069 (vulnerability databases).}
Claim 5
With respect to claim 5, Jain, Chen, and G Rao teach the invention as claimed including:
wherein the code of the first software program is written in a first high-level language and the code of the second software program may be generated in a second high-level language, and wherein the first high-level language is different from the second high-level language. {Programs in a first programming language may be translated by a machine learning model into a second programming language as well as having the code optimized as part of the translation. G Rao at Abstract; id at col. 2 ll. 6 – 9; id. at col. 4 ll. 22 – 43 (program algorithms may be optimized); id. at col 4 ll. 61 – 65 (high level languages).}
Claim 6
With respect to claim 6, Jain, Chen, and G Rao teach the invention as claimed including:
wherein the second high-level language is compiled to a binary segment. {Generated application updates may be deployed as compiled binaries. Jain at ¶ 0201.}
Claim 7
With respect to claim 7, Jain, Chen, and G Rao teach the invention as claimed including:
providing, to the artificial intelligence engine, at least one additional input that includes auxiliary data. {Prompts may be tuned using samples. Chen at ¶¶ 0053 & 0086.}
Claim 8
With respect to claim 8, Jain, Chen, and G Rao teach the invention as claimed including:
wherein the auxiliary data includes security priority information associated with the code of the first software program. {Prompts may be tuned using security samples from databases and fine-turned using parameter adjustment to improve the model’s performance. Chen at ¶¶ 0053 & 0086; id. at ¶ 0065; id. at ¶ 0069.}
Claim 9
With respect to claim 9, Jain, Chen, and G Rao teach the invention as claimed including:
wherein the auxiliary data includes at least one design architecture assumption, and wherein artificial intelligence engine generates the code for the second software program by modifying the at least one design architecture assumption. {Prompts may be tuned using security and design samples from databases and fine-turned using parameter adjustment to improve the model’s performance. Chen at ¶¶ 0053 & 0086; id. at ¶ 0065; id. at ¶ 0069.}
Claim 11
With respect to claim 11, Jain, Chen, and G Rao teach the invention as claimed including:
wherein the code of the first software program includes networking infrastructure code, wherein a configuration for a network associated with the networking infrastructure code is defined in a file using defined syntax, and wherein the artificial intelligence engine generates the code for the second software program using the first software program and the file. {Prompts may be tuned using security, networking, and design samples from databases and fine-turned using parameter adjustment to improve the model’s performance. Chen at ¶¶ 0053 & 0086; id. at ¶ 0065; id. at ¶ 0069; id. at ¶ 0054.}
Claims 12 and 18
With respect to claims 12 and 18, Jain, Chen, and G Rao teach the invention as claimed including:
wherein the artificial intelligence engine may be tuned using prompt-based tuning. {Prompts may be tuned using samples. Chen at ¶¶ 0053 & 0086.}
Claim 13
With respect to claim 13, Jain and Chen teach the invention as claimed including:
wherein the at least one function is associated with at least one aspect of vehicle operation. {A machine learning model trained using code samples receives a first software program, such as a natural language code docstring, and uses it to generate a second software program code in a high level object oriented language, which may be verified via testing. Chen at ¶¶ 0080 – 0082; ¶ 0046 (unit test); id. at ¶ 0053 (integration testing); id. at ¶ 0045 (vehicle); ¶¶ 0053 & 0086 (code samples); id. at ¶ 0043(object oriented language).}
Claim 14
With respect to claim 14, Jain, Chen, and G Rao teach the invention as claimed including:
wherein sub-segments of the first software program are combined using the artificial intelligence engine. {A machine learning model trained using code samples receives a first software program, such as a natural language code docstring, and uses it to generate a second software program code in a high level object oriented language, which may be verified via testing. Chen at ¶¶ 0080 – 0082; ¶ 0046 (unit test); id. at ¶ 0053 (integration testing); id. at ¶ 0045 (vehicle); ¶¶ 0053 & 0086 (code samples); id. at ¶ 0043(object oriented language); id. at ¶ 0054 (segments).}
Claim 15
With respect to claim 15, Jain, Chen, and G Rao teach the invention as claimed including:
wherein the first software program is segmented into at least two segments and each segment of the at least two segments is provided to the artificial intelligence engine individually. {A machine learning model trained using code samples receives a first software program, such as a natural language code docstring, and uses it to generate a second software program code in a high level object oriented language, which may be verified via testing. Chen at ¶¶ 0080 – 0082; ¶ 0046 (unit test); id. at ¶ 0053 (integration testing); id. at ¶ 0045 (vehicle); ¶¶ 0053 & 0086 (code samples); id. at ¶ 0043(object oriented language); id. at ¶ 0054 (segments).}.}
Claim 19
With respect to claim 19, Jain, Chen, and G Rao teach the invention as claimed including:
wherein prompts associated with the prompt-based tuning are provided to the artificial intelligence engine as auxiliary data input. {Prompts may be tuned using security, networking, and design samples from databases and fine-turned using parameter adjustment to improve the model’s performance. Chen at ¶¶ 0053 & 0086; id. at ¶ 0065; id. at ¶ 0069; id. at ¶ 0054.}
Claim 20
With respect to claim 20, Jain, Chen, and G Rao teach the invention as claimed including:
wherein prompts associated with the prompt-based tuning include at least one of generating code with fewer variables than the code of the first software program, generating code with a faster runtime than the code of the first software program, generating code with lower memory footprint than the code of the first software program, generating code with a different ordering of independent code elements than the ordering of independent code elements of the code of the first software program, and generating code with a same functionality but following a different algorithmic approach as the functionality and algorithmic approach of the code of the first software program. {Prompts may be tuned using security, networking, and design samples from databases and fine-turned using parameter adjustment to improve the model’s performance. Chen at ¶¶ 0053 & 0086; id. at ¶ 0065; id. at ¶ 0069; id. at ¶ 0054; id. at ¶ 0047 (faster and more efficient code).}
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Jain in view of Chen, G Rao, and Mestchian et al., United States Patent Application Publication No. 2021/0200519 (Published July 1, 2021, filed December 27, 2019) (“Mestchian”).
Claim 10
With respect to claim 10, Jain, Chen, and G Rao teach the invention as claimed, However, Jain, Chen, and G Rao doesn’t explicitly teach the limitation:
wherein the code of the first software program and the code of the second software program includes hardware description language code representing functions to be implemented on reconfigurable hardware. {Mestchian does teach this limitation. Mestchian teaches that generating new versions of code to update previous code, as taught in Jain, Chen, and G Rao, may include where the new code is hardware description language code for reconfigurable field gate array processors. Mestchian at ¶¶ 0004, 0009, 0056, 0060.
Jain, Chen, G Rao, and Mestchian are analogous art because they are from the “same field of endeavor” and are both from the same “problem-solving area.” Specifically, they are both from the field of software deployment, and both are trying to solve the problem of how to deploy a new version of a software code.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine generating new versions of code to update previous code, as taught in Jain, Chen, and G Rao, with where the new code is hardware description language code, as taught in Mestchian. Chen teaches that using natural language processing increases “efficiency and accuracy.” Id. at ¶ 0003. Therefore, one having ordinary skill in the art would have been motivated to combine generating new versions of code to update previous code, as taught in Jain, Chen, and G Rao, with where the new code is hardware description language code, as taught in Mestchian, for the purpose of improving the efficiency and accuracy of generating a particular type of high level code.}
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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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//T.H./ September 27, 2026
Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199
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