DETAILED ACTION
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 .
Status of Claims
Claims 1-20 are pending in this application.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “components” in claims 1-6, 8 and 17-20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong One: The independent claim 9 recites “receiving a prompt related to maintenance of one or more components of a powered system, the prompt received by a generative large language model (LLM); identifying one or more function tools to be used in searching for information using the generative LLM, the one or more function tools identified based on the prompt; assigning one or more discriminative LLMs to search maintenance logbooks and technical manuals associated with the one or more components of the powered system based on the one or more function tools that are identified; and creating a response to the prompt according to a pattern associated with the one or more function tools that are identified and using the responsive information”.
[Abstract idea indicators]
receiving a prompt— a task that human routinely performs mentally or with conventional tools.
identifying one or more function tools to search, i.e., a cognitive process.
Assigning data source (model) step and creating an answer step that are mental processes.
Accordingly, the claims are directed to the judicial exception of a mental process.
Step 2A, Prong Two: This judicial exception is not integrated into a practical application. Claims 1 and 9, recite additional element of “powered system”. The powered system recited at a high-level of generality (i.e., as a generic computer performing functions and being used as an applying) such that it amounts no more than mere instructions to apply the exception using a generic computer component as well. Accordingly, there additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B — Claims Do Not Recite an Inventive Concept That Transforms the Mental Process into Patent-Eligible Subject Matter.
The claims add generic, well-understood computer components (memory, processor, and presenting to a workspace device) and broadly recite use of “a large language model (LLM)” without describing any specific, unconventional structure, algorithmic detail, data structure, or system architecture that provides a concrete technical improvement in computer functionality.
Applying Alice step two and relevant Federal Circuit precedent:
The recitation of conventional computer components (memory and processor) performing routine functions does not supply an inventive concept.
The mere invocation of “LLMs trained” without particularity does not demonstrate an unconventional machine or technique or a specific improvement in computer technology.
The claims recite high-level, result-oriented steps (e.g., “receive”, “select”, “identify”, “assign”, “create”) that describe mental processes rather than specific technical means for performing those processes.
Because the claims lack limitations that tie the mental-process steps to a particular way of achieving a technological improvement (for example, a novel model architecture, specialized data representation, unique training regimen that yields demonstrable technical performance gains, a specialized streaming/decoding pipeline that reduces latency by a quantifiable amount, or hardware/software co-design), the additional elements do not transform the mental processes into significantly more.
Therefore, claims 1, 9 and 17 fail to recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
With respect to dependent claim 2, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 3, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 4, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 5, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 6, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 7, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 8, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 10, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 11, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 12, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 13, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 14, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 15, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 16, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 18, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 19, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
With respect to dependent claim 20, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and further does not remedy the judicial exception being integrated into a practical application.
Conclusion — Rejection
Claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to a judicial exception (mental processes) and failing to recite additional elements that amount to significantly more than the judicial exception.
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 and 9 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Gopalan et al., (US Pub. 2025/0139140 A1).
Regarding claim 1, Gopalan discloses an artificial intelligence system comprising:
a generative large language model (LLM) configured to receive prompts related to maintenance of one or more components of a powered system, the generative LLM configured to identify one or more function tools to be used in searching for information responsive to the prompts (Figs. 1 and 6, [0050][0058][0094] receiving a user query about the status of “network handovers” in a 5G network by a LLM 104 and a LLM 102 agent which may monitor the Real Network or Emulator 112); and
one or more discriminative LLMs trained on maintenance logbooks and technical manuals associated with the one or more components of the powered system, the generative LLM configured to select from among the one or more discriminative LLMs to search for the information responsive to the prompts based on which of the one or more function tools are identified, the one or more discriminative LLMs that are selected configured to obtain the information responsive to the prompts and to provide the information to the generative LLM, the generative LLM configured to create and present responses to the prompts according to a pattern associated with the one or more function tools that are identified and using the responsive information (Figs. 1, 3, and 6, [0072] For monitoring the Radio Access Network (RAN), an English-described dashboard design is easily translated by the LLM to generate listener code that is ready-to-run; [0045][0098][0099] the planner 640 can efficiently generate coding instructions to fulfill the user's request and interact with the database in a context-aware and action-oriented manner and may be implemented using tools like OpenAI GPT, Codex, or Google PaLM, which can be used to translate natural language queries into step-by-step coding instructions; [0049][0063][0064][0099][0107] generating a response which may include action-based outputs, such as confirmation of a network control command execution).
Regarding claim 9, Gopalan discloses a method comprising:
receiving a prompt related to maintenance of one or more components of a powered system, the prompt received by a generative large language model (LLM) (Figs. 1 and 6, [0050][0058][0094] receiving a user query about the status of “network handovers” in a 5G network by a LLM 104 and a LLM 102 agent which may monitor the Real Network or Emulator 112);
identifying one or more function tools to be used in searching for information using the generative LLM, the one or more function tools identified based on the prompt (Figs. 1, 3, and 6, [0072] For monitoring the Radio Access Network (RAN), an English-described dashboard design is easily translated by the LLM to generate listener code that is ready-to-run);
assigning one or more discriminative LLMs to search maintenance logbooks and technical manuals associated with the one or more components of the powered system based on the one or more function tools that are identified (Figs. 1 and 6, [0045][0098][0099] the planner 640 can efficiently generate coding instructions to fulfill the user's request and interact with the database in a context-aware and action-oriented manner and may be implemented using tools like OpenAI GPT, Codex, or Google PaLM, which can be used to translate natural language queries into step-by-step coding instructions); and
creating a response to the prompt according to a pattern associated with the one or more function tools that are identified and using the responsive information (Figs. 1, 3, and 6, [0049][0063][0064][0099][0107] generating a response which may include action-based outputs, such as confirmation of a network control command execution).
Claim Rejections - 35 USC § 103
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.
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.
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 nonobviousness.
This application currently names 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 2-8 and 10-20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Gopalan et al., (US Pub. 2025/0139140 A1) in view of Wang et al., (“Large Language Model Empowered by Domain Specific Knowledge Base for Industrial Equipment Operation and Maintenance”, IEEE, 2023).
Regarding claim 2, Gopalan discloses the artificial intelligence system of claim 1, wherein the generative LLM is configured to identify a component function tool as the one or more function tools responsive to the prompts [0049][0063][0064][0099][0107] identifying a network control command execution).
Gopalan does not explicitly teach however Wang does explicitly teach:
the one or more discriminative LLMs configured to one or more of obtain maintenance records of the one or more components, provide recent actions performed on the one or more components, or provide conditions of the one or more components (pp. 476, Fig. 1, section III, pp.478, TABLE 1, receiving prompts/query related to maintenance of a powered system).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate the method for AI/ML assisted management as taught by Gopalan with the method of apply LLM to industrial equipment operation and maintenance as taught by Wang to improve industrial operational efficiency, equipment lifespan, and personnel safety (Wang, [Abstract]).
Regarding claim 3, Gopalan in view of Wang discloses the artificial intelligence system of claim 1, and Wang further discloses:
wherein the generative LLM is configured to identify a condition function tool as the one or more function tools responsive to the prompts, the one or more discriminative LLMs configured to one or more of obtain the information on conditions of the one or more components during recent operation (Wang, pp. 476, Fig. 1, section III, pp.478, TABLE 1, identifying information related to maintenance records of a powered system).
The previous motivation statement as in claim 2 is still applied.
Regarding claim 4, Gopalan in view of Wang discloses the artificial intelligence system of claim 1, and Wang further discloses:
wherein the generative LLM is configured to identify an action function tool as the one or more function tools responsive to the prompts, the one or more discriminative LLMs configured to obtain actions or events performed during recent operation of the one or more components (Wang, pp. 476, Fig. 1, section III, pp.478, TABLE 1, identifying information related to technical document and manuals of a powered system).
The previous motivation statement as in claim 2 is still applied.
Regarding claim 5, Gopalan in view of Wang discloses the artificial intelligence system of claim 1, and Gopalan further discloses:
wherein the generative LLM is configured to identify a recurrent summary tool as the one or more function tools responsive to the prompts, the one or more discriminative LLMs configured to provide a frequency at which some event occurred involving the one or more components ([0049] for example with frequently-asked questions, the stored answers can be re-used, and no interaction with the LLM is needed).
Regarding claim 6, Gopalan in view of Wang discloses the artificial intelligence system of claim 1, and Wang further discloses:
wherein the generative LLM is configured to identify a minimum equipment list summary tool as the one or more function tools responsive to the prompts, the one or more discriminative LLMs configured to provide one or more maintenance logbooks of the one or more components that are included in a minimum equipment list for an aircraft responsive to the minimum equipment list summary tool being identified (pp. 478, LLM's responses are general and lack specific technical details. In contrast, LLM-DSKB's responses are more professional, providing clear and concise technical details that offer practical guidance”).
The previous motivation statement as in claim 2 is still applied.
Regarding claim 7, Gopalan in view of Wang discloses the artificial intelligence system of claim 1, and Wang further discloses:
wherein the generative LLM is configured to identify a sensor warning tool as the one or more function tools responsive to the prompts, the one or more discriminative LLMs configured to one or more of provide a characteristic measured by a sensor giving rise to a warning or alarm, historical values of sensor outputs, a history of the warning or alarm occurring, or one or more limits used by the sensor to determine when to output the warning or alarm (Wang, pp. 476, Fig. 1, section III, pp.478, TABLE 1, identifying and providing response for fault code occurring; pp. 475, Left Column, LLMs can be integrated with sensor networks and monitoring systems to monitor device status in real-time and generate corresponding alerts).
The previous motivation statement as in claim 2 is still applied.
Regarding claim 8, Gopalan in view of Wang discloses the artificial intelligence system of claim 1, and Wang further discloses:
wherein the generative LLM is configured to identify an estimated work time tool as the one or more function tools responsive to the prompts, the one or more discriminative LLMs configured to provide an estimated time to complete maintenance on the one or more components responsive to the estimated work time tool being identified (pp. 475, Left Column, “DSKBs can be updated in real-time to ensure that LLM-DSKB stays up-to-date with the latest technological developments. This enables LLM-DSKB to provide professional, specific, and practical results in various industrial tasks”).
The previous motivation statement as in claim 2 is still applied.
Regarding claims 10-16, claims 10-16 are the corresponding medium claims to system claims 2-8. Therefore, claims 10-16 are rejected using the same rationale as applied to claims 2-8 above.
Regarding claim 17, Gopalan discloses an artificial intelligence system comprising:
a generative large language model (LLM) configured to receive a prompt related to maintenance of a component of an [aircraft], the generative LLM configured to identify a function tool to be used in searching for information responsive to the prompt (Figs. 1 and 6, [0050][0058][0094] receiving a user query about the status of “network handovers” in a 5G network by a LLM 104 and a LLM 102 agent which may monitor the Real Network or Emulator 112); and
one or more discriminative LLMs trained on maintenance logbooks and technical manuals associated with the component of the [aircraft], the generative LLM configured to select from among the one or more discriminative LLMs to search for the information responsive to the prompt based on which of the one or more function tools are identified, the one or more discriminative LLMs configured to obtain the information responsive to the prompt using the function tool that is identified and to provide the information to the generative LLM (Figs. 1, 3, and 6, [0072] For monitoring the Radio Access Network (RAN), an English-described dashboard design is easily translated by the LLM to generate listener code that is ready-to-run; [0045][0098][0099] the planner 640 can efficiently generate coding instructions to fulfill the user's request and interact with the database in a context-aware and action-oriented manner and may be implemented using tools like OpenAI GPT, Codex, or Google PaLM, which can be used to translate natural language queries into step-by-step coding instructions; [0049][0063][0064][0099][0107] generating a response which may include action-based outputs, such as confirmation of a network control command execution).
Gopalan does not explicitly teach the bracketed limitation however Wang does explicitly teach including the bracketed limitation:
a generative large language model (LLM) configured to receive a prompt related to maintenance of a component of an [aircraft], and one or more discriminative LLMs trained on maintenance logbooks and technical manuals associated with the component of the [aircraft] (pp. 476, Fig. 1, section III, pp.478, TABLE 1, receiving prompts/query related to maintenance of industrial equipments such as Helicopter and etc.).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to incorporate the method for AI/ML assisted management as taught by Gopalan with the method of apply LLM to aircraft operation and maintenance as taught by Wang to improve industrial operational efficiency, equipment lifespan, and personnel safety (Wang, [Abstract]).
Regarding claim 18, Gopalan in view of Wang discloses the artificial intelligence system of claim 17, and Gopalan further discloses:
wherein the generative LLM is configured to create and present a response to the prompt according to a designated pattern associated with the function tool that is identified and using the responsive information (Figs. 1, 3, and 6, [0049][0063][0064][0099][0107] generating a response which may include action-based outputs, such as confirmation of a network control command execution).
Regarding claim 19, Gopalan in view of Wang discloses the artificial intelligence system of claim 17, and Gopalan further discloses:
wherein the generative LLM is configured to identify a component function tool as the one or more function tools responsive to the prompts [0049][0063][0064][0099][0107] identifying a network control command execution).
Gopalan does not explicitly teach however Wang does explicitly teach:
the one or more discriminative LLMs configured to one or more of obtain maintenance records of the one or more components, provide recent actions performed on the one or more components, or provide conditions of the one or more components (pp. 476, Fig. 1, section III, pp.478, TABLE 1, receiving prompts/query related to maintenance of a powered system).
The previous motivation statement as in claim 17 is still applied.
Regarding claim 20, Gopalan in view of Wang discloses the artificial intelligence system of claim 17, and Wang further discloses:
wherein the generative LLM is configured to identify a condition function tool as the one or more function tools responsive to the prompts, the one or more discriminative LLMs configured to one or more of obtain the information on conditions of the one or more components during recent operation (Wang, pp. 476, Fig. 1, section III, pp.478, TABLE 1, identifying information related to maintenance records of a powered system).
The previous motivation statement as in claim 17 is still applied.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEONG-AH A. SHIN whose telephone number is (571)272-5933. The examiner can normally be reached 9 AM-3PM.
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Seong-ah A. Shin
Primary Examiner
Art Unit 2659
/SEONG-AH A SHIN/Primary Examiner, Art Unit 2659
Prosecution Insights