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
1. This action is responsive to the application filed on September 10, 2024.
2. Claims 1-20 have been examined.
Specification
3. Please add US patent number of the parent application 18/096,466.
Double Patenting Rejection
4. 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. See 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); and 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) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.131 (c). A registered attorney or agent of record may sign a terminal disclaimer.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/ file/efs/guidance/eTD-info-l.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 12,135,958. Although the claims at issue are not identical, they are not patentably distinct from each other because claims of the present application are just broader versions of the patent claims.
US Patent 12,135,958
Present Application
1. A method for facilitating voice based dictation of programming code within a context of an integrated development environment (IDE) such that vocabulary specific to the programming code is recognizable, said method comprising: feeding programming code to a text-to-speech (TTS) model, wherein the TTS model generates at least one audio file that is associated with the programming code; feeding the at least one audio file to a speech-to-text (STT) model, wherein the STT model generates at least one transcription file that is associated with the at least one audio file; mapping each respective line of code included in the programming code to a corresponding line of code included in the at least one transcription file, resulting in generation of a list of phrase pairings, where the phrase pairings represent relationships between actual code and how that actual code sounds if read out loud; and causing a large language model (LLM) to ingest the list of phrase pairings, wherein the LLM identifies correlations between programming vocabulary that has specific meaning within the context of the IDE and how that programming vocabulary sounds if read out loud.
2. The method of claim 1, wherein the programming code is stored in a repository, and wherein the repository includes multiple different programs in multiple different programming languages.
3. The method of claim 2, wherein the method further includes performing language recognition on the programming code to determine in what language the programming code is written.
4. The method of claim 1, wherein the phrase pairings include a first phrase and a second phrase, the first phrase represents the actual code and the second phrase represents how that actual code sounds if read out loud, and wherein the second phrase is different than the first phrase.
5. The method of claim 1, wherein the method further includes: receiving an utterance, where the utterance includes vocabulary that has particular meaning within the context of the IDE; and imputing the particular meaning to the vocabulary.
6. The method of claim 5, wherein the particular meaning is one of an IDE command, variable, or comment.
7. The method of claim 5, wherein the particular meaning is a programming language executable meaning.
8. A computer system that facilitates voice based dictation of programming code within a context of an integrated development environment (IDE) such that vocabulary specific to the programming code is recognizable, said computer system comprising: at least one processor; and at least one hardware storage device that stores instructions that are executable by the at least one processor to cause the computer system to: feed programming code to a text-to-speech (TTS) model, wherein the TTS model generates at least one audio file that is associated with the programming code; feed the at least one audio file to a speech-to-text (STT) model, wherein the STT model generates at least one transcription file that is associated with the at least one audio file; map each respective line of code included in the programming code to a corresponding line of code included in the at least one transcription file, resulting in generation of a list of phrase pairings, where the phrase pairings represent relationships between actual code and how that actual code sounds if read out loud; and cause a large language model (LLM) to ingest the list of phrase pairings, wherein the LLM identifies correlations between programming vocabulary that has specific meaning within the context of the IDE and how that programming vocabulary sounds if read out loud.
9. The computer system of claim 8, wherein the programming code includes multiple different programs that are written in a same programming language.
10. The computer system of claim 9, wherein a different audio file is generated for each one of the multiple different programs.
11. The computer system of claim 10, wherein a different transcription file is generated for each one of the different audio files.
12. The computer system of claim 9, wherein a single audio file is generated for an entirety of the multiple different programs.
13. The computer system of claim 8, wherein the identified correlations made by the LLM are included in a prompt.
14. The computer system of claim 8, wherein execution of the instructions further causes the computer system to: receive an utterance that includes a set of programming vocabulary whose meaning is determined based on the context of the IDE; and impute the meaning to the programming vocabulary in the received utterance.
15. The computer system of claim 8, wherein the programming code is stored in a repository, and wherein the repository includes multiple different programs in multiple different programming languages.
16. The computer system of claim 15, wherein the computer system is further caused to perform language recognition on the programming code to determine in what language the programming code is written.
17. At least one hardware storage device comprising instructions that are executable by at least one processor of a computer system to cause the computer system to: feed programming code to a text-to-speech (TTS) model, wherein the TTS model generates at least one audio file that is associated with the programming code; feed the at least one audio file to a speech-to-text (STT) model, wherein the STT model generates at least one transcription file that is associated with the at least one audio file; map each respective line of code included in the programming code to a corresponding line of code included in the at least one transcription file, resulting in generation of a list of phrase pairings, where the phrase pairings represent relationships between actual code and how that actual code sounds if read out loud; and cause a large language model (LLM) to ingest the list of phrase pairings, wherein the LLM identifies correlations between programming vocabulary that has specific meaning within a context of an integrated development environment (IDE) and how that programming vocabulary sounds if read out loud.
18. The at least one hardware storage device of claim 17, wherein the phrase pairings include a first phrase and a second phrase, the first phrase represents the actual code and the second phrase represents how that actual code sounds if read out loud, and wherein the second phrase is different than the first phrase.
19. The at least one hardware storage device of claim 17, wherein the programming code includes multiple different programs that are written in a same programming language.
20. The at least one hardware storage device of claim 17, wherein the identified correlations made by the LLM are included in a prompt.
1. A method comprising: feeding code to a text-to-speech (TTS) model that generates an audio file based on the code; feeding the audio file to a speech-to-text (STT) model that generates a transcription file based on the audio file; mapping language in the code to corresponding language in the transcription file, resulting in generation of a list of phrase pairings that represent relationships between actual code and how that actual code sounds if read aloud; and causing a large language model (LLM) to ingest the list of phrase pairings, wherein the LLM identifies correlations between vocabulary that has specific meaning within a context of a development environment and how that vocabulary sounds if read aloud.
2. The method of claim 1, wherein the code is programming code that includes a command.
3. The method of claim 1, wherein the code is programming code that includes a variable.
4. The method of claim 1, wherein the code is programming code that includes a comment.
5. The method of claim 1, wherein the method further includes performing language recognition on the code to determine in what programming language the code is written.
6. The method of claim 1, wherein the LLM further generates a listing of available utterances that a user can speak to invoke a specific programming command that will be recognized.
7. The method of claim 6, wherein the listing of available utterances includes multiple different utterance variations that refer to the same specific programming command.
8. A computer system comprising: one or more processors; and one or more hardware storage devices that store instructions that are executable by the one or more processors to cause the computer system to: feed code to a text-to-speech (TTS) model that generates an audio file based on the code; feed the audio file to a speech-to-text (STT) model that generates a transcription file based on the audio file; map language in the code to corresponding language in the transcription file, resulting in generation of a list of phrase pairings that represent relationships between actual code and how that actual code sounds if read aloud; and cause a large language model (LLM) to ingest the list of phrase pairings, wherein the LLM identifies correlations between vocabulary that has specific meaning within a context of a development environment and how that vocabulary sounds if read aloud.
9. The computer system of claim 8, wherein the code is programming code that includes a command.
10. The computer system of claim 8, wherein the code is programming code that includes a variable.
11. The computer system of claim 8, wherein the code is programming code that includes a comment.
12. The computer system of claim 8, wherein the instructions are further executable to cause the computer system to perform language recognition on the code to determine in what programming language the code is written.
13. The computer system of claim 8, wherein the LLM further generates a listing of available utterances that a user can speak to invoke a specific programming command that will be recognized.
14. The computer system of claim 13, wherein the listing of available utterances includes multiple different utterance variations that refer to the same specific programming command.
15. One or more hardware storage devices that store instructions that are executable by one or more processors to cause the one or more processors to: feed code to a text-to-speech (TTS) model that generates an audio file based on the code; feed the audio file to a speech-to-text (STT) model that generates a transcription file based on the audio file; map language in the code to corresponding language in the transcription file, resulting in generation of a list of phrase pairings that represent relationships between actual code and how that actual code sounds if read aloud; and cause a large language model (LLM) to ingest the list of phrase pairings, wherein the LLM identifies correlations between vocabulary that has specific meaning within a context of a development environment and how that vocabulary sounds if read aloud.
16. The one or more hardware storage devices of claim 15, wherein the code is programming code that includes a command.
17. The one or more hardware storage devices of claim 15, wherein the code is programming code that includes a variable.
18. The one or more hardware storage devices of claim 15, wherein the code is programming code that includes a comment.
19. The one or more hardware storage devices of claim 15, wherein the instructions are further executable to cause the computer system to perform language recognition on the code to determine in what programming language the code is written.
20. The one or more hardware storage devices of claim 15, wherein the LLM further generates a listing of available utterances that a user can speak to invoke a specific programming command that will be recognized.
Allowable Subject Matter
4. The following is an examiner’s statement of reasons for allowance:
US 12,541,542 to Hays et al. discloses an LLM utterance rewriter module that uses a large language model (LLM), in conjunction with linguistic metadata, to generate precise questions (e.g., candidate precise utterances) from vague utterances.
US 12,511,489 to Andreas et al. discloses generating a formal meaning representation using a natural language utterance and a language model. A large language model including a semantic parser is used to parse the natural language utterance into the formal meaning representation.
NPL to Zhou et al. discloses detecting utterances using a large language model in a conversational setting.
However, neither Hays, Andreas, nor Zhou anticipates or renders obvious the combination set forth in the independent claims recited as "feeding the audio file to a speech-to-text (STT) model that generates a transcription file based on the audio file," "mapping language in the code to corresponding language in the transcription file, resulting in generation of a list of phrase pairings that represent relationships between actual code and how that actual code sounds if read aloud," and "causing a large language model (LLM) to ingest the list of phrase pairings, wherein the LLM identifies correlations between vocabulary that has specific meaning within a context of a development environment."
That is, Hays, Andreas, and Zhou disclose large language models and language utterances but do not disclose the specific limitations as required above in all independent claims.
Conclusion
5. Any inquiry concerning this communication should be directed to examiner Thuy (Twee) Dao, whose telephone/fax numbers are (571) 272 8570 and (571) 273 8570, respectively. Examiner can normally be reached from Monday to Friday, 5:30am - 2:00pm ET.
If attempts to reach Examiner by telephone are unsuccessful, Examiner’s supervisor, Hyung (Sam) Sough, can be reached at (571) 272 6799.
The fax phone number for the organization where this application or proceeding is assigned is (571) 273 8300.
Any inquiry of a general nature of relating to the status of this application or proceeding should be directed to the TC 2100 Group receptionist whose telephone number is (571) 272 2100.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/Thuy Dao/Primary Examiner, Art Unit 2192
Prosecution Insights