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 response to the application filed on 11/04/2025.
Claims 1-20 are pending in this Office Action. Claims 1, 10 and 19 are independent claims.
Priority
Acknowledged is this Application claims the benefit of priority from parent Application 19219090, filed 5/27/2025, to which the instant application is a divisional.
Information Disclosure Statement
The information disclosure statements filed 11/04/2025 are in compliance with 37 CFR 1.97(c) and therein have been considered. Its corresponding PTO-1449 have been electronically signed as attached.
Claim Interpretation
A “patent is invalid for indefiniteness if its claims, read in light of the specification delineating the patent, and the prosecution history, fail to inform, with reasonable certainty, those skilled in the art about the scope of the invention.” Nautilus, Inc. v. Biosig Instruments, Inc., 134 S.Ct. 2120, 2124, 110 USPQ2d 1688 (2014). The Office does not interpret claims when examining patent applications in the same manner as the courts. The Office construes claims by giving them their broadest reasonable interpretation during prosecution in an effort to establish a clear record of what the applicant intends to claim. See, MPEP 2173.02 (Determining Whether Claim Language is Definite). Such claim construction during prosecution may effectively result in a lower threshold for ambiguity than a court's determination. Id. However, Applicant has the ability to amend the claims during prosecution to ensure that the meaning of the language is clear and definite prior to issuance or provide a persuasive explanation (with evidence as necessary) that a person of ordinary skill in the art would not consider the claim language unclear. Id. (citing In re Buszard, 504 F.3d 1364, 1366 (Fed. Cir. 2007) (claims are given their broadest reasonable interpretation during prosecution “to facilitate sharpening and clarifying the claims at the application.
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 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.
In the event the determination of the status of the application as subject to 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
For application naming 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 1-3, 10-12 and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over
LINDER et al.: "GENERATIVE AI FOR CONTROL AND MANAGEMENT OF MANUFACTURING PROCESSES", (W.I.P.O. Patent Application Document WO 2024220444 A2, DATE PUBLISHED 2024-10-24 and DATE FILED 2024-04-16, hereafter "LINDER"), in view of
GHOLAMI et al.: "METHODS AND SYSTEMS FOR TRANSFERRING KNOWLEDGE FROM LARGE LANGUAGE MODELS TO SMALL LANGUAGE MODELS USING OUT-OF-DISTRIBUTION FEEDBACKS", (U.S. Patent Application Publication US-20250190712-A1, DATE PUBLISHED 2025-06-12and DATE FILED 2023-12-12, hereafter "GHOLAMI").
As per claim 1, LINDER teaches a computer-implemented method, comprising:
iteratively querying a very large language model (VLLM) with an instruction code including public entities with associated public documents to generate public solutions (See [0090] and [0106], large language model may be used to advantageously support inferences based on a knowledge domain derived from a large corpus of text-based documents upon which it was trained, the use of near natural language as a medium for exchanging data permits the use of very large language models to provide natural language to database query and in data query named ‘best operators’ there are 10 rows and 3 columns. The columns are ‘Name’, ‘Time’, and ‘Process’. The first row is ‘Johnny’, '90’ (seconds), ‘Process Widget.’ The second row is . . ..);
extracting rationale features from the public solutions with the VLLM (See [0080], a system that can receive video data, text, and program code, this may include a convolutional neural network or other structure to extract spatial features, objects, activities, keyframes, segments, and timing from a video stream).
LINDER does not explicitly teach updating the instruction code by combining an input query about public entities, the public solutions with the rationale features, text from reference chunks, and an input query about a single private entity, following a pre-determined template, to yield a private instruction code about a single private entity.
However, GHOLAMI teaches updating the instruction code by combining an input query about public entities, the public solutions with the rationale features, text from reference chunks, and an input query about a single private entity, following a pre-determined template, to yield a private instruction code about a single private entity (See [0051], [0054], [0060], natural language input given to the model to guide its generation of output, training digital objects in a given multidimensional space defined by features of these training examples and the model processes a batch of data and updates its parameters during an iteration).
It would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to combine GHOLAMI’s teaching with LINDER because LINDER is dedicated to use of generative artificial intelligence (Al) available for commercially available, preexisting, very large language models that encode correspondingly large knowledge to manage and control manufacturing processes and GHOLAMI is dedicated to knowledge distillation from large language models (LLMs) to small language models (SLMs) using out-of-distribution feedbacks, and a combined teaching would have enabled the embodiment of LINDER to deploy smaller large language models to enhance very large language model for knowledge distillation.
LINDER in view of GHOLAMI further teaches:
answering, with private large language models (PLLM), the private instruction code to obtain private answers for performing downstream tasks (See GHOLAMI: abstract, iteratively executing: acquiring a client input including: (i) an task for the SLM, (ii) a plurality of data samples responsive to the task, and (iii) an indication of the SLM; generating, using the LLM, a plurality of training samples for the SLM based on client input; training the SLM based on the plurality of training samples to execute the task, thereby generating a first trained SLM; generating, using the LLM, a plurality of validation samples for the first trained SLM; generating an augmented plurality of data samples including the plurality of data samples and at least one target validation sample from the plurality of validation samples; and using the augmented plurality of data samples for training the SLM during a following training iteration. Here the generating of validation samples, data samples and target validation samples teaches the private answers for performing the iteration of executing the serial of tasks, the downstream tasks).
As per claim 2, LINDER in view of GHOLAMI teaches the computer-implemented method of claim 1, wherein extracting the rationale features further comprises dividing the public solutions previously generated by the VLLM for an input query into solution paragraphs (See LINDER: [0091], text-based natural language processing, similar generative Al models may be created for other modes of input and/or output such as audio, speech, video, images, and so forth.).
As per claim 3, LINDER in view of GHOLAMI teaches the computer-implemented method of claim 2, wherein extracting the rationale features further comprises retrieving reference chunks from private documents by utilizing the solution paragraphs and an input private entity as key (See LINDER: [0044], all documents mentioned are incorporated by reference in their entirety. References to items in the singular should be understood to include items in the plural, and vice versa, unless explicitly stated otherwise or clear from the text. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations of conjoined clauses, sentences, words, and the like, unless otherwise stated or clear from the context.).
As per claims 10-12, the claims recite a system, comprising:
a memory device (See LINDER: [0054], a first memory may include memory on the processor,);
one or more processor devices operatively coupled with the memory device to perform (See LINDER: [0054], a second memory that provides additional working memory for instructions and data used by the processor to perform) operations recited as the steps of the claims 1-3 above, respectively and rejected under 35 U.S.C. § 103 as being unpatentable over LINDER in view of GHOLAMI.
Accordingly, claims 10-12 are rejected along the same rationale that rejected claims 1-3, respectively.
As per claim 19, the claim recites a non-transitory computer program product comprising a computer readable storage medium including a program code, wherein the program code when executed on a computer causes the computer to perform (See LINDER: [0005], computer executable code embodied in a non-transitory computer readable medium that, when executing on one or more computing devices, performs) operations recited as the steps of the claim 1 and rejected under 35 U.S.C. § 103 as being unpatentable over LINDER in view of GHOLAMI.
Accordingly, claim 19 is rejected along the same rationale that rejected claim 1.
Claims 4-6 and 13-15 are rejected under 35 U.S.C. § 103 as being unpatentable over
LINDER in view of
GHOLAMI, as applied to claims 1-3, 10-12 and 19 above, and further in view of
CARBUNE et al.: " GENERATIVE MODEL TUNING AND INFERENCE UTILIZING QUALITY SIGNALS", (W.I.P.O. Patent Application Document WO 2025170579 A1, DATE PUBLISHED 2025-08-14 and DATE FILED 2024-02-07, hereafter "CARBUNE").
As per claim 4, LINDER in view of GHOLAMI does not explicitly teach the computer-implemented method of claim 3, wherein extracting the rationale features further comprises obtaining highest-ranked reference chunks based on relevance scores of the reference chunks.
On the other hand, in the same endeavor on large or very large language models, CARBUNE teaches the computer-implemented method of claim 3, wherein extracting the rationale features further comprises obtaining highest-ranked reference chunks based on relevance scores of the reference chunks (See [0085]-[0086], the plurality of candidate predicted responses can be generated based on learned features for token prediction, the candidate model generated outputs 318 can include response fragments.).
It would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to combine GHOLAMI’s teaching with LINDER in view of GHOLAMI because LINDER is dedicated to use of generative artificial intelligence (Al) available for commercially available, preexisting, very large language models that encode correspondingly large knowledge to manage and control manufacturing processes, GHOLAMI is dedicated to knowledge distillation from large language models (LLMs) to small language models (SLMs) using out-of-distribution feedbacks and CARBUNE is dedicated to leveraging quality signals associated with training resources to prioritize particular resources during training and model inferences, which may include training and leveraging a machine- learned reward model, and a combined teaching would have enabled LINDER in view of GHOLAMI to the reinforcement learning from human feedback to increase perceived quality.
As per claim 5, LINDER in view of GHOLAMI and further in view of CARBUNE teaches the computer-implemented method of claim 4, wherein updating the instruction code further comprises inserting the highest-ranked reference chunks into the instruction code (See CARBUNE: [0087], the candidate model generated outputs 318 can include response fragments the generated output 326 can then be fed back to the generative model 314 to generate a full model generated response).
As per claim 6, LINDER in view of GHOLAMI and further in view of CARBUNE teaches the computer-implemented method of claim 5, wherein answering the private instruction code iteratively further comprises prompting the PLLM with the instruction code to iteratively answer the private instruction code (See CARBUNE : [0051], [0054], [0060], natural language input given to the model to guide its generation of output, training digital objects in a given multidimensional space defined by features of these training examples and the model processes a batch of data and updates its parameters during an iteration).
As per claims 13-15, the claims recite a system, comprising:
a memory device (See LINDER: [0054], a first memory may include memory on the processor,);
one or more processor devices operatively coupled with the memory device to perform (See LINDER: [0054], a second memory that provides additional working memory for instructions and data used by the processor to perform) operations recited as the steps of the claims 4-6 above, respectively and rejected under 35 U.S.C. § 103 as being unpatentable over LINDER in view of GHOLAMI and further in view of CARBUNE.
Accordingly, claims 13-15 are rejected along the same rationale that rejected claims 4-6, respectively.
Claims 7-9, 16-18 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over
LINDER in view of
GHOLAMI, as applied to claims 1-3, 10-12 and 19 above, and further in view of
Kumar; Navneet: "CROSS-PLATFORM QUERY AND CONTENT CREATION SERVICE AND INTERFACE FOR COLLABORATION PLATFORMS", (U.S. Patent Application Document US 20250110618 A1, DATE PUBLISHED 2025-04-03and DATE FILED 2023-09-28, hereafter "Kumar").
As per claim 7, LINDER in view of GHOLAMI does not explicitly each the computer-implemented method of claim 1, further comprising truncating a private instruction code to avoid overflowing the context limit of the PLLM.
On the other hand, in the same endeavor on large or very large language models, KUMAR teaches the teaches the computer-implemented method of claim 1, further comprising truncating a private instruction code to avoid overflowing the context limit of the PLLM (See [0222], the plugin may also truncate the generative response or generate a subsequent prompt including the generative response and a command to shorten the summary or output.).
It would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to combine GHOLAMI’s teaching with LINDER in view of GHOLAMI because LINDER is dedicated to use of generative artificial intelligence (Al) available for commercially available, preexisting, very large language models that encode correspondingly large knowledge to manage and control manufacturing processes, GHOLAMI is dedicated to knowledge distillation from large language models (LLMs) to small language models (SLMs) using out-of-distribution feedbacks and KUMAR is dedicated to automated content creation and organization in collaborative work environments, and a combined teaching would have enabled users of LINDER in view of GHOLAMI to share the same platforms or services to facilitate cooperation and completion of work.
As per claim 8, LINDER in view of GHOLAMI and further in view of Kumar teaches the computer-implemented method of claim 1, wherein the downstream tasks further comprises selecting a polymer to be manufactured using candidate materials determined to have desired properties (See Kumar: [0264], in some embodiments, device may include electroactive polymers suggested polymer a candidate material selected for manufacturing).
As per claim 9, LINDER in view of GHOLAMI and further in view of Kumar teaches the computer-implemented method of claim 8, wherein selecting the polymer further comprises visualizing clusters of candidate materials based on determined similarity of properties (See Kumar: [0264]-[0266], [0264], in some embodiments, device may include electroactive polymers suggested polymer a candidate material selected for manufacturing; and the output devices 1310 may include any suitable components for providing outputs. Examples of output devices 1310 include light emitters, audio output devices (e.g., speakers), visual output devices).
As per claims 16-18, the claims recite a system, comprising:
a memory device (See LINDER: [0054], a first memory may include memory on the processor,);
one or more processor devices operatively coupled with the memory device to perform (See LINDER: [0054], a second memory that provides additional working memory for instructions and data used by the processor to perform) operations recited as the steps of the claims 7-9 above, respectively and rejected under 35 U.S.C. § 103 as being unpatentable over LINDER in view of GHOLAMI and further in view of Kumar.
Accordingly, claims 16-18 are rejected along the same rationale that rejected claims 7-9, respectively.
As per claim 20, the claim recites a non-transitory computer program product comprising a computer readable storage medium including a program code, wherein the program code when executed on a computer causes the computer to perform (See LINDER: [0005], computer executable code embodied in a non-transitory computer readable medium that, when executing on one or more computing devices, performs) operations recited as the steps of the claim 8 and rejected under 35 U.S.C. § 103 as being unpatentable over LINDER in view of GHOLAM I and further in view of Kumar.
Accordingly, claim 20 is rejected along the same rationale that rejected claim 8.
Related Prior Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of Reference Cited.
Conclusion
Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KUEN S LU whose telephone number is (571)272-4114. The examiner can normally be reached on M-F, 8-19, Mid-Flex 2 hours.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mr. Aleksandr Kerzhner can be reached on 571-270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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KUEN S LU /Kuen S Lu/
Art Unit 2165
Primary Patent Examiner
September 29, 2026
Prosecution Insights