DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is in response to Application 19/284193 filed on July 29, 2025 in which Claims 1-20 are presented for examination.
Status of Claims
Claims 1-20 are pending, of which claims 1-20 are rejected under Double Patenting. Claims 1-20 do not have a prior art rejection.
Claim Rejections - 35 USC § 112
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 2, 4, 6 and 8 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation "the first task" in Line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the first task" in Line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the first task" in Line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation "the first task" in Line 2. There is insufficient antecedent basis for this limitation in the claim.
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 §§ 706.02(l)(1) - 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) 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-I.jsp.
Claims 1- 20 of the instant application are rejected on the ground of obviousness-type nonstatutory double patenting as being unpatentable over Claims 1-20 of U.S. Patent No. US 12,405,580. Although the claims at issue are not identical, they are not patentably distinct from each other because the aforementioned claims of the instant application are rejected based on obviousness-type double patenting with regards to the aforementioned parent patent.
The following table summarizes claim mappings associated with the obviousness-type double patenting rejections:
19/284193
18/655037 (12,405,580)
1. A non-transitory, computer-readable storage medium comprising instructions recorded thereon, wherein the instructions, when executed by at least one data processor of a system, cause the system to: use a large language model (LLM) to generate a first set of computer executable instructions; execute the first set of computer executable instructions; observe a result of the execution; and based on the observed result of the execution: use the LLM to generate a second set of computer executable instructions to perform a second task; or detect an error in the first set of computer executable instructions and, in response to detecting the error, use the LLM to modify the first set of computer executable instructions to correct the error.
1. A non-transitory, computer-readable storage medium comprising instructions recorded thereon, wherein the instructions, when executed by at least one data processor of a system, cause the system to: use a large language model (LLM) to generate a first set of extensible markup language (XML) instructions to perform a first task in an environment communicatively coupled to the system; execute the first set of XML instructions; observe a result of the execution; and based on the observed result of the execution: use the LLM to generate a second set of XML instructions to perform a second task in the environment; or detect an error in the first set of XML instructions and, in response to detecting the error, use the LLM to modify the first set of XML instructions to correct the error.
2. The non-transitory computer-readable storage medium of claim 1, wherein the instructions when executed further cause the system to: after observing the result of the execution and using the LLM to modify the first set of computer executable instructions to correct the error, executing the modified first set of computer executable instructions to output a task result for the first task.
2. The non-transitory computer-readable storage medium of claim 1, wherein the instructions when executed further cause the system to: after observing the result of the execution and using the LLM to modify the first set of XML instructions to correct the error, executing the modified first set of XML instructions to output a task result for the first task to the environment.
3. The non-transitory computer-readable storage medium of claim 1, wherein the instructions when executed cause the system to use the LLM to generate the second set of computer executable instructions in response to detecting no error in the first set of computer executable instructions.
3. The non-transitory computer-readable storage medium of claim 1, wherein the instructions when executed cause the system to use the LLM to generate the second set of XML instructions in response to detecting no error in the first set of XML instructions.
4. The non-transitory computer-readable storage medium of claim 1, wherein the first task includes writing content to a specified location within an environment, and wherein observing the result of the execution comprises: parsing the first set of computer executable instructions to determine an identification of the specified location; and observing the environment to locate the specified location.
4. The non-transitory computer-readable storage medium of claim 1, wherein the first task includes writing content to a specified location within the environment, and wherein observing the result of the execution comprises: parsing the first set of XML instructions to determine an identification of the specified location; and observing the environment to locate the specified location.
5. The non-transitory computer-readable storage medium of claim 4, wherein the error is detected when the specified location is not located in the environment.
5. The non-transitory computer-readable storage medium of claim 4, wherein the error is detected when the specified location is not located in the environment.
6. The non-transitory computer-readable storage medium of claim 1, wherein the first task includes writing content to an environment, and wherein observing the result of the execution comprises: parsing the first set of computer executable instructions to determine whether the content to be written can be identified.
6. The non-transitory computer-readable storage medium of claim 1, wherein the first task includes writing content to the environment, and wherein observing the result of the execution comprises: parsing the first set of XML instructions to determine whether the content to be written can be identified.
7. The non-transitory computer-readable storage medium of claim 6, wherein the error is detected when the first set of computer executable instructions cannot be executed to identify the content to be written.
7. The non-transitory computer-readable storage medium of claim 6, wherein the error is detected when the first set of XML instructions cannot be executed to identify the content to be written.
8. The non-transitory computer-readable storage medium of claim 1, wherein the first task includes reading content from an environment, and wherein executing the first set of computer executable instructions comprises performing a read operation to read the content from the environment.
8. The non-transitory computer-readable storage medium of claim 1, wherein the first task includes reading content from the environment, and wherein executing the first set of XML instructions comprises performing a read operation to read the content from the environment.
9. The non-transitory computer-readable storage medium of claim 8, wherein detecting the error comprises detecting the read operation returns a null value.
9. The non-transitory computer-readable storage medium of claim 8, wherein detecting the error comprises detecting the read operation returns a null value.
10. The non-transitory computer-readable storage medium of claim 1, wherein observing the result of the execution comprises detecting a syntax error in the first set of computer executable instructions.
10. The non-transitory computer-readable storage medium of claim 1, wherein observing the result of the execution comprises detecting a syntax error in the first set of XML instructions.
11. The non-transitory computer-readable storage medium of claim 1, wherein the instructions when executed cause the system to use the LLM to generate the first set of computer executable instructions in response to a natural language input received from a user in association with an environment.
11. The non-transitory computer-readable storage medium of claim 1, wherein the instructions when executed cause the system to use the LLM to generate the first set of XML instructions in response to a natural language input received from a user in association with the environment.
12. The non-transitory computer-readable storage medium of claim 1, wherein the instructions when executed cause the system to use the LLM to generate the first set of computer executable instructions in response to execution of a third set of computer executable instructions to perform a third task.
12. The non-transitory computer-readable storage medium of claim 1, wherein the instructions when executed cause the system to use the LLM to generate the first set of XML instructions in response to execution of a third set of XML instructions to perform a third task in the environment.
13. A system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to: use a large language model (LLM) to generate a first set of computer executable instructions to perform a first task; execute the first set of computer executable instructions; observe a result of the execution; and based on the observed result of the execution: use the LLM to generate a second set of computer executable instructions to perform a second task; or detect an error in the first set of computer executable instructions and, in response to detecting the error, use the LLM to modify the first set of computer executable instructions to correct the error.
13. A system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to: use a large language model (LLM) to generate a first set of extensible markup language (XML) instructions to perform a first task in an environment communicatively coupled to the system; execute the first set of XML instructions; observe a result of the execution; and based on the observed result of the execution: use the LLM to generate a second set of XML instructions to perform a second task in the environment; or detect an error in the first set of XML instructions and, in response to detecting the error, use the LLM to modify the first set of XML instructions to correct the error.
14. The system of claim 13, wherein the instructions when executed further cause the system to: after observing the result of the execution and using the LLM to modify the first set of computer executable instructions to correct the error, executing the modified first set of computer executable instructions to output a task result for the first task.
14. The system of claim 13, wherein the instructions when executed further cause the system to: after observing the result of the execution and using the LLM to modify the first set of XML instructions to correct the error, executing the modified first set of XML instructions to output a task result for the first task to the environment.
15. The system of claim 13, wherein the first task includes writing content to a specified location within an environment, and wherein observing the result of the execution comprises: parsing the first set of computer executable instructions to determine an identification of the specified location; and observing the environment to locate the specified location.
15. The system of claim 13, wherein the first task includes writing content to a specified location within the environment, and wherein observing the result of the execution comprises: parsing the first set of XML instructions to determine an identification of the specified location; and observing the environment to locate the specified location.
16. The system of claim 13, wherein observing the result of the execution comprises detecting a syntax error in the first set of computer executable instructions.
16. The system of claim 13, wherein observing the result of the execution comprises detecting a syntax error in the first set of XML instructions.
17. A method comprising: causing a computer system to use a large language model (LLM) to generate a first set of computer executable instructions to perform a first task in an environment communicatively coupled to the computer system; executing, by the computer system, the first set of computer executable instructions; observing, by the computer system, a result of the execution; and based on the observed result of the execution: causing the computer system to use the LLM to generate a second set of computer executable instructions to perform a second task in the environment; or detecting, by the computer system, an error in the first set of computer executable instructions and, in response to detecting the error, using the LLM to modify the first set of computer executable instructions to correct the error.
17. A method comprising: causing a computer system to use a large language model (LLM) to generate a first set of extensible markup language (XML) instructions to perform a first task in an environment communicatively coupled to the computer system; executing, by the computer system, the first set of XML instructions; observing, by the computer system, a result of the execution; and based on the observed result of the execution: causing the computer system to use the LLM to generate a second set of XML instructions to perform a second task in the environment; or detecting, by the computer system, an error in the first set of XML instructions and, in response to detecting the error, use the LLM to modify the first set of XML instructions to correct the error.
18. The method of claim 17, further comprising: after observing the result of the execution and using the LLM to modify the first set of computer executable instructions to correct the error, executing, by the computer system, the modified first set of computer executable instructions to output a task result for the first task to the environment.
18. The method of claim 17, further comprising: after observing the result of the execution and using the LLM to modify the first set of XML instructions to correct the error, executing, by the computer system, the modified first set of XML instructions to output a task result for the first task to the environment.
19. The method of claim 17, wherein the first task includes writing content to a specified location within the environment, and wherein observing the result of the execution comprises: parsing the first set of computer executable instructions to determine an identification of the specified location; and observing the environment to locate the specified location.
19. The method of claim 17, wherein the first task includes writing content to a specified location within the environment, and wherein observing the result of the execution comprises: parsing the first set of XML instructions to determine an identification of the specified location; and observing the environment to locate the specified location.
20. The method of claim 17, wherein observing the result of the execution comprises detecting a syntax error in the first set of computer executable instructions.
20. The method of claim 17, wherein observing the result of the execution comprises detecting a syntax error in the first set of XML instructions.
The claims of US Patent No. 12,405,580 do not explicitly teach computer executable instructions.
However, Bowden et al. (US Patent Application 2025/0013435) teaches provide the list to a large language model (LLM) configured to summarize actions for each role based on the desired features; generate a set of instructions for each action for each role; and generate content for each set of instruction, in the Abstract.
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to combine the claims of US Patent No. 12,405,580 with computer executable instructions for the purpose of modifying the computer executable instructions to correct an error.
Prior Art Made of Record
Martin et al. (US Patent 7,512,840) teaches instead of using XML as a mere transport of data parameters between two object-oriented end points that are parts of a Web Service exchange (RPC), the systems and methods described herein consist in a procedural-based language that executes on top of XML schemas and that is subjected to all the constraints expressed in these schemas.
Tsang et al. (US Patent Application 2009/0055686) teaches receiving test results from the test command and the test data produced via the command simulator and the bean simulator, including catching an exception on an error condition, and outputting the test results to an XML output repository.
Gelfenbeyn et al. (US Patent 12,118,320) teaches prior to sending a request to the LLM, the platform may classify and filter the user questions and messages to change words based on the personalities of AI characters, emotional states of AI characters, emotional states of users, context of a conversation, scene and environment of the conversation, and so forth. Similarly, the platform may adjust the response formed by the LLM by changing words and adding fillers based on the personality, role, and emotional state of the AI character. The fillers can include words like “ah”, “hm”, “like,” “you know,” “alright”, and so forth. The fillers can be used to provide the AI character with time to think, express uncertainty or make something awkward feel less awkward, or as a verbal tick. The AI character model may change emotions based on the role of the AI character and in response to emotions of the user.
Wang et al. (US Patent Application 2019/0332680) teaches a multi-lingual device can be configured to receive verbal input. The verbal input can provided in a first language, which is a natural language spoken by humans. The multi-lingual device can further be configured to determine original text from the verbal input. The text can be determined using an automatic speech recognition engine of the multi-lingual device. The original text can be output in the first language. The multi-lingual device can further be configured to determine a confidence value for the original text. The confidence value for the original text can use a statistical association between the original text and the verbal input. The automatic speech recognition engine can output the original text according to the confidence value for the original text. The multi-lingual device can further be configured to determine translated text corresponding to the original text. The translated text can be determined using a machine translation engine of the multi-lingual device. The machine translation engine can translate the original text to a second language, which is also a natural language.
Koiek Akino et al. (US Patent Application 2025/0045492) teaches C/C++ code parsing with Clang format can generate unified extensible markup language (XML) formats to be analyzed by LLM.
Jones et al. (US Patent Application No. 2025/0111220 A1), teaches generative pre trained large language models (LLMs) can create domain-specific text answers in various formats like JSON, XML, HTML, SQL, or programming languages. However, LLMs may “hallucinate,” generating incorrect or nonsensical answers that diverge from reality, thus eroding trust in their outputs or worse. Disclosed techniques use a sampling-based approach and an equivalence checker. Multiple answers (samples) to a prompt are generated by the LLM; if they are equivalent, the LLM is likely answering correctly. If the samples disagree or contradict, it's more likely that the LLM is hallucinating, or the prompt is ambiguous. An automated reasoning equivalence checker is utilized to verify the samples' functional equivalency, providing a method to detect and possibly rectify hallucination issues in LLM-generated answers.
Conclusion
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/SARAI E BUTLER/Primary Examiner, Art Unit 2114
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