DETAILED ACTION
This communication is responsive to the application # 19/219,090 filed on May 27, 2025. Claims 1-20 are pending and are directed toward GUIDING MULTIPLE MODELS WITH A LARGE LANGUAGE MODEL.
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 . 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 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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5-7, 10, 14-16, and 19 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over GHOLAMI et al. (US 2025/0190712, Filed: Dec. 12, 2023), hereinafter referred to as GHOLAMI.
As per claim 1, GHOLAMI teaches a computer-implemented method, comprising:
generating an instruction code for a very large language model (VLLM) to generate a general guidance to guide AI models that answer reasoning questions for query documents (In the context of the present technology, the term "prompt" refers to an initial natural language input or instruction given to the model to guide its generation of output. It can be a few words, a sentence, or a paragraph, specifying the topic or format required for the generated text. The model then responds based on the provided prompt to produce relevant and coherent output, GHOLAMI, [0051]);
updating the instruction code with domain-specific information from reference materials to generate, with the VLLM, a reasoned answer for reasoning questions about the query documents generated based on the general guidance (In the context of the present technology, the term "teacher model" refers to a larger, more complex model that has been trained on a large dataset. The teacher model is typically used to label data for a smaller model, GHOLAMI, [0053]);
processing the reasoned answers into the general guidance with the VLLM (The student model, on the other hand, is a smaller, less complex model that is trained to mimic the behavior of the teacher model. The student model learns from the teacher model by leveraging its knowledge to achieve similar accuracy. GHOLAMI, [0053]); and
answering, with the AI models, the reasoning question iteratively applied to the query documents using the general guidance to perform downstream tasks (In the context of the present specification, the term "training data distribution" refers to a distribution of training samples (or otherwise, training digital objects) in a given multidimensional space defined by features of these training examples. For example, for training samples representative of movie reviews, the features defining the respective multidimensional space can be, without limitation, a genre of a given movie, a style of writing of a given reviewer, countries where the given movie has been produced, a release year of the given movie, etc. GHOLAMI, [0054]).
As per claim 5, GHOLAMI teaches the computer-implemented method of claim 1, wherein updating the instruction code further comprises extracting reference chunks from reference materials based on the general guidance (GHOLAMI, [0058]).
As per claim 6, GHOLAMI teaches the computer-implemented method of claim 5, wherein updating the instruction code further comprises appending the reference chunks to the instruction code to generate reasoning questions (GHOLAMI, [0098]).
As per claim 7, GHOLAMI teaches the computer-implemented method of claim 6, wherein updating the instruction code further comprises determining reasoned answers based on the reasoning questions by utilizing the VLLM (GHOLAMI, [0099]).
Claims 10, 14-16, and 19 have limitations similar to those treated in the above rejection, and are met by the references as discussed above, and are rejected for the same reasons of anticipation as used above.
Claim Rejections - 35 USC § 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, 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 2-4, and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over GHOLAMI et al. (US 2025/0190712, Filed: Dec. 12, 2023), in view of Leary et al. (US 2024/0095463, Mar. 21, 2024), hereinafter referred to as GHOLAMI and Leary.
As per claim 2, GHOLAMI teaches the computer-implemented method of claim 1, but does not teach privacy, Leary however teaches wherein generating the instruction code further comprises providing filtered query documents to the VLLM to ensure privacy of the query documents (In at least one embodiment, to protect privacy and rights of patients, subjects, or clients of different facilities, pre-trained models 1506 may have been trained, on-premise, using customer or patient data generated on-premise. Leary, [0147]).
GHOLAMI in view of Leary are analogous art to the claimed invention, because they are from a similar field of endeavor of systems, components and methodologies for using AI. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify GHOLAMI in view of Leary. This would have been desirable because pre-trained models 1606 may have been trained, at least in part, at one or more facilities other than a facility executing process 1600. In at least one embodiment, to protect privacy and rights of patients, subjects, or clients of different facilities (Leary, [0158]).
As per claim 3, GHOLAMI in view of Leary teaches the computer-implemented method of claim 2, wherein generating the instruction code further comprises extracting guidance examples from filtered query documents to instruct the VLLM (GHOLAMI, [0054]).
GHOLAMI in view of Leary are analogous art to the claimed invention, because they are from a similar field of endeavor of systems, components and methodologies for using AI. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify GHOLAMI in view of Leary. This would have been desirable because pre-trained models 1606 may have been trained, at least in part, at one or more facilities other than a facility executing process 1600. In at least one embodiment, to protect privacy and rights of patients, subjects, or clients of different facilities (Leary, [0158]).
As per claim 4, GHOLAMI in view of Leary teaches the computer-implemented method of claim 3, wherein generating the instruction code further comprises concatenating extracted text from the guidance examples to the instruction code (GHOLAMI, [0056]).
GHOLAMI in view of Leary are analogous art to the claimed invention, because they are from a similar field of endeavor of systems, components and methodologies for using AI. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify GHOLAMI in view of Leary. This would have been desirable because pre-trained models 1606 may have been trained, at least in part, at one or more facilities other than a facility executing process 1600. In at least one embodiment, to protect privacy and rights of patients, subjects, or clients of different facilities (Leary, [0158]).
Claims 11-13 have limitations similar to those treated in the above rejection, and are met by the references as discussed above, and are rejected for the same reasons of obviousness as used above.
Claims 8, 9, 17, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over GHOLAMI et al. (US 2025/0190712, Filed: Dec. 12, 2023), in view of Gao et al. (Machine Learning-Assisted Design of Advanced Polymeric Materials, Acc. Mater. Res. (2024) 5 (5): 571–584. Published April 16, 2024), hereinafter referred to as GHOLAMI and Gao.
As per claim 8, GHOLAMI teaches the computer-implemented method of claim 1, but does not teach a polymer, Gao, however teaches wherein the downstream tasks further comprises manufacturing a polymer using candidate materials determined to have desired properties (Figure 2. ML-assisted design procedure to obtain the desired polymeric materials: (A) structure representation and database construction, (B) establishment of ML-based property prediction model, and (C) virtual design and high-throughput screening. Gao, page 572).
GHOLAMI in view of Gao are analogous art to the claimed invention, because they are from a similar field of endeavor of systems, components and methodologies for using AI. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify GHOLAMI in view of Gao. This would have been desirable because ML-assisted design has proven to be a successful approach for designing novel high-performance polymeric materials (GAO, page 571).
As per claim 9, GHOLAMI teaches the computer-implemented method of claim 8, wherein manufacturing the polymer further comprises visualizing clusters of candidate materials based on determined similarity of properties (During the ML-assisted screening process, by defining and combining polymer genes, virtual polymer candidates are generated, and subsequently, their properties are predicted and high-throughput screened using ML property prediction models. Finally, the promising polymers identified through this approach are verified by computer simulations and experiments. Gao, page 571).
GHOLAMI in view of Gao are analogous art to the claimed invention, because they are from a similar field of endeavor of systems, components and methodologies for using AI. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify GHOLAMI in view of Gao. This would have been desirable because ML-assisted design has proven to be a successful approach for designing novel high-performance polymeric materials (GAO, page 571).
Claims 17, 18, and 20 have limitations similar to those treated in the above rejection, and are met by the references as discussed above, and are rejected for the same reasons of obviousness as used above.
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 obviousness-type 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); 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) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of copending Application No. 19/379,133. Although the conflicting claims are not identical, they are not patentably distinct from each other because all elements of claims 1-20 of the instant application correspond to elements of claims 1-20 of the Application No. 19/379,133. The above claims of the present application would have been obvious over claims 1-20 of the Application No. 19/379,133 because each element of the claims of the present application is anticipated by the claims of the Application No. 19/379,133 and as such are unpatentable for obviousness-type double patenting (In re Goodman (CAFC) 29 USPQ2D 2010 (12/3/1993)).
This is a provisional nonstatutory double patenting rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLEG KORSAK whose telephone number is (571)270-1938. The examiner can normally be reached on Monday-Friday 7:30am - 5:00pm EST.
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/OLEG KORSAK/
Primary Examiner, Art Unit 2492
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