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 .
DETAILED ACTION
This action is in response to the claimed listing filed on 09/24/2024.
Claims 1-20 are pending.
Double Patenting
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).
A timely filed terminal disclaimer in compliance with 37 CFR 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 CFR 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 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US12141560B2.
Although the claims at issue are not identical, they are not patentably distinct from each other because:
Current Application
US12141560B2 (App. 18/055558)
1. A transpilation platform, comprising:
at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor,
cause the transpilation platform to:
receive, from a source computing system via a network, a source file, wherein the source computing system comprises a data repository storing the source file associated with a source programming language;
parse the source file to generate a first intermediate file corresponding to a structure of the source programming language;
transpile, by a machine-learning (ML) transpilation model, terms from the source programming language to corresponding terms in a target programming language, wherein a target computing system processes operations in a target file associated with the target programming language, wherein the source programming language and the target programming language are different programming languages;
generate, based on transpiled terms received from the ML transpilation model, a second intermediate file corresponding to a structure of the target programming language;
generate, based on the second intermediate file, a target file comprising instructions in the target programming language, wherein the target file causes the target computing system to perform actions performed by the source computing system based on the source file; and
train, by a language learning engine, the ML transpilation model, based on feedback received from the target computing system after performance of operations based on execution of the target file.
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2. The transpilation platform of claim 1, wherein the target programming language is a dialect of the source programming language.
3. The transpilation platform of claim 1, wherein the source programming language is unrelated to the target programming language.
4. The transpilation platform of claim 1, wherein the first intermediate file is an abstract syntax tree (AST) file.
5. The transpilation platform of claim 1, wherein the instructions cause the transpilation platform to automatically train the ML transpilation model based on documentation describing operations of the source programming language and the target programming language.
6. The transpilation platform of claim 5, wherein the instructions cause the transpilation platform to:
process, via a natural language processor, the documentation describing operations of the source programming language:
generate a first configuration file based on output from the natural language processor, wherein the first configuration file identifies a plurality of characteristics of operations of the source programming language; and
train the ML model based on the first configuration file.
7. The transpilation platform of claim 6, wherein the instructions cause the transpilation platform to:
process, via a natural language processor, the documentation describing operations of the target programming language:
generate a second configuration file based on output from the natural language processor, wherein the second configuration file identifies a plurality of characteristics of operations of the source programming language; and
train the ML model based on the second configuration file.
8. The transpilation platform of claim 7, wherein the instructions cause the transpilation platform to:
generate, based on the first configuration file and the second configuration file, a first mapping between operations of the source programming language and the target programming language.
9. The transpilation platform of claim 7, wherein the instructions cause the transpilation platform to:
process, via a natural language processor, the documentation describing operations of a third programming language:
generate a third configuration file based on output from the natural language processor, wherein the third configuration file identifies a plurality of characteristics of operations of the source programming language;
train the ML model based on the third configuration file; and
generate, by the ML model, a second mapping of operations between the target programming language and the third programming language.
10. The transpilation platform of claim 9, wherein the instructions cause the transpilation platform to:
generate, by the ML model and based on first mapping of operations and the second mapping of operations, a third mapping of operations between the source programming language and the third programming language.
1. A system comprising:
a source computing system comprising a data repository storing a source file associated with a source programming language;
a target computing system comprising configured to process operations in a target file associated with a target programming language, wherein the source programming language and the target programming language are different programming languages;
a transpilation platform, comprising:
at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the transpilation platform to:
receive, from the source computing system via a network, the source file;
parse the source file to generate a first intermediate file corresponding to a structure of the source programming language;
transpile, by a machine-learning (ML) transpilation model, terms from the source programming language to corresponding terms in the target programming language;
generate, based on transpiled terms received from the ML transpilation model, a second intermediate file corresponding to a structure of the target programming language;
generate, based on the second intermediate file, a target file comprising instructions in the target programming language, wherein the target file causes the target computing system to perform actions performed by the source computing system based on the source file; and
train, by a language learning engine, the ML transpilation model, based on feedback received from the target computing system after performance of operations based on execution of the target file.
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2. The system of claim 1, wherein the target programming language is a dialect of the source programming language.
3. The system of claim 1, wherein the source programming language is unrelated to the target programming language.
4. The system of claim 1, wherein the first intermediate file is an abstract syntax tree (AST) file.
5. The system of claim 1, wherein the instructions cause the transpilation platform to automatically train the ML transpilation model based on documentation describing operations of the source programming language and the target programming language.
6. The system of claim 5, wherein the instructions cause the transpilation platform to:
process, via a natural language processor, the documentation describing operations of the source programming language:
generate a first configuration file based on output from the natural language processor, wherein the first configuration file identifies a plurality of characteristics of operations of the source programming language; and
train the ML model based on the first configuration file.
7. The system of claim 6, wherein the instructions cause the transpilation platform to:
process, via a natural language processor, the documentation describing operations of the target programming language:
generate a second configuration file based on output from the natural language processor, wherein the second configuration file identifies a plurality of characteristics of operations of the source programming language; and
train the ML model based on the second configuration file.
8. The system of claim 7, wherein the instructions cause the transpilation platform to:
generate, based on the first configuration file and the second configuration file, a first mapping between operations of the source programming language and the target programming language.
9. The system of claim 7, wherein the instructions cause the transpilation platform to:
process, via a natural language processor, the documentation describing operations of a third programming language:
generate a third configuration file based on output from the natural language processor, wherein the third configuration file identifies a plurality of characteristics of operations of the source programming language;
train the ML model based on the third configuration file; and
generate, by the ML model, a second mapping of operations between the target programming language and the third programming language.
10. The system of claim 9, wherein the instructions cause the transpilation platform to:
generate, by the ML model and based on first mapping of operations and the second mapping of operations, a third mapping of operations between the source programming language and the third programming language.
- Current Claims 1-10 are directed to a transpilation platform.
-Current Claims 11-20 of the Application are directed to Non-transitory computer-readable media, and recite the limitations corresponding to the limitations in the current claims 1-10 in the table above. Therefore, they would be compared to the claims in the US Patent, provided in the above table or with the method and the system claims of the above US Patent.
The Claims in the current Application recite to extend its claimed functionality on a platform and on non-transitory computer-readable media. From the table above, the features in the current claims are rearranged in comparison to the limitations in the claims of the US patent. Although the claims at issue are not identical, they are not patentable distinct from each other.
Therefore, it would be obvious to one of the ordinary skills in the art before the effective filing of the invention to modify current claims (as seen in the compared table above) from the US patent claims for extending coverage.
Allowable Subject Matter
Claims 1-20 are subjected under the Double Patenting issue given above; the Claims would be allowable, provided with Double Patenting resolved.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ted T Vo whose telephone number is (571)272-3706. The examiner can normally be reached 8am-4:30pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wei Y Mui can be reached at (571) 272-3708. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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TTV
July 9, 2026
/Ted T. Vo/
Primary Examiner, Art Unit 2191
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