DETAILED ACTION
1. This communication is in response to the Application No. 18/436,419 filed on February 8, 2024 in which Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
3. The information disclosure statement submitted on 02/08/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner’s Remarks
4. Examiner notes that Independent Claims 1, 11, and 12 recite “submitting a hardware prompt of a language machine learning model […]” and “submitting a software prompt of a language machine learning model […]”, however, these limitations seem disjoint from the remaining claim language, as the results of these submissions are seemingly not used in any subsequent processing/training/tuning presented by the currently drafted claim language. Moreover, the “outputs” and “training/tuning” of the language model, original system, and the discriminant machine learning model are merely stated/recited without significantly more – there are no subsequent limitations which outline how exactly these outputs are obtained and how exactly the machine learning models are trained/tuned (e.g., training data, algorithms, performance metrics, thresholds, optimization strategy, etc.). Examiner advises Applicant to consider amending the claim language to address these gaps in the currently drafted claim limitations, which may also assist in overcoming the 35 U.S.C. 101 abstract idea rejection.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a method claim. Therefore, Claims 1-10 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
distinguish between outputs of the original system and outputs of the language machine learning model (mental process – other than reciting a “discriminant machine learning model”, distinguishing between outputs of the original system and outputs of the language machine learning model may be performed manually by a user observing/analyzing received hardware/software prompts, given outputs of the original system, and given outputs of the language machine learning model and correspondingly using judgement/evaluation to compare (with the aid of pen and paper) and accordingly distinguish between outputs of the original system and outputs of the language machine learning model, based on said analysis)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a computer-implemented method of training a digital twin (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more)
submitting a hardware prompt to a language machine learning model, the hardware prompt characterizing hardware of an original system (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
submitting a software prompt to the language machine learning model, the software prompt characterizing software of the original system (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
training a discriminant machine learning model […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more)
tuning the language machine learning model to act as a digital twin of the original system based on an output of the discriminant machine learning model, an output of the language machine learning model, and an output of the original system (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a computer-implemented method of training a digital twin (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
submitting a hardware prompt to a language machine learning model, the hardware prompt characterizing hardware of an original system (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
submitting a software prompt to the language machine learning model, the software prompt characterizing software of the original system (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
training a discriminant machine learning model […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
tuning the language machine learning model to act as a digital twin of the original system based on an output of the discriminant machine learning model, an output of the language machine learning model, and an output of the original system (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-10. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 2 depends on.
validating a hardware configuration […] comparing an output description to the hardware of the original system (mental process – validating a hardware configuration may be performed manually by a user observing/analyzing the received/generated description of the hardware configuration and the hardware of the original system, specified by the received hardware prompt, and correspondingly using judgement/evaluation to compare (with the aid of pen and paper) the generated description of the hardware configuration and the hardware of the original system, based on said analysis)
Step 2A Prong 2 & Step 2B:
[…] generated via the submitting of the hardware prompt to the language machine learning model, by prompting the language machine learning model to describe the hardware configuration […] (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim. Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 3:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 3 depends on.
Step 2A Prong 2 & Step 2B:
wherein the software prompt includes a description of software on the original system and dependencies for the software (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the software prompt includes a description of software on the original system and dependencies for the software does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 4:
Step 2A Prong 1: See the rejection of Claim 3 above, which Claim 4 depends on.
validating a software configuration […] comparing an output description to the software and dependencies of the original system (mental process – validating a software configuration may be performed manually by a user observing/analyzing the received/generated description of the software configuration and the software and dependencies of the original system, specified by the received software prompt, and correspondingly using judgement/evaluation to compare (with the aid of pen and paper) the generated description of the software configuration and the software and dependencies of the original system, based on said analysis)
Step 2A Prong 2 & Step 2B:
[…] generated via the submitting of the software prompt to the language machine learning model, by prompting the language machine learning model to describe the software configuration […] (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim. Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 5:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 5 depends on.
Step 2A Prong 2 & Step 2B:
wherein the discriminant machine learning model is a neural network classifier that accepts a system output and that generates a classification as to whether the system output was generated by the digital twin (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner's note: high level recitation of applying a trained machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 6:
Step 2A Prong 1:
See the rejection of Claim 5 above, which Claim 1 depends on.
Step 2A Prong 2 & Step 2B:
tuning the discriminant machine learning model responsive to a determination that the discriminant machine learning model has incorrectly identified output from the digital twin as being output from the original system (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner's note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 7:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 7 depends on.
Step 2A Prong 2 & Step 2B:
wherein tuning the language machine learning model is performed responsive to a determination that the discriminant machine learning model has correctly identified output from the digital twin (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner's note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 8:
Step 2A Prong 1:
See the rejection of Claim 1 above, which claim 8 depends on.
Step 2A Prong 2 & Step 2B:
testing the digital twin independently from inputs to the original system (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner's note: high level recitation of testing/training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 9:
Step 2A Prong 1: See the rejection of Claim 8 above, which Claim 9 depends on.
altering the original system responsive to the testing (mental process – altering the original system responsive to the testing may be performed manually by a user observing/analyzing the original system and the results related to the testing and correspondingly using judgement/evaluation to alter (with the aid of pen and paper) the original system responsive to the testing)
Step 2A Prong 2 & Step 2B:
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 10:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 10 depends on.
Step 2A Prong 2 & Step 2B:
wherein the output of the language machine learning model and the output of the original system are generated responsive to a single input (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the outputs are generated responsive to a single input does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Independent Claim 11 recites substantially the same limitations as Claim 1, in the form of a computer program product, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
For the reasons above, Claim 11 is rejected as being directed to an abstract idea without significantly more.
Independent Claim 12 recites substantially the same limitations as Claim 1, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
For the reasons above, Claim 12 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 13-20. The additional limitations of the dependent claims are addressed below.
Claim 13 recites substantially the same limitations as Claim 2, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 14 recites substantially the same limitations as Claim 3, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 15 recites substantially the same limitations as Claim 4, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 16 recites substantially the same limitations as Claim 5, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 17 recites substantially the same limitations as Claim 6, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 18 recites substantially the same limitations as Claim 7, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 19 recites substantially the same limitations as Claim 8, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 20 recites substantially the same limitations as Claim 9, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Allowable Subject Matter
7. No prior art rejection is made for Claims 1-20. However, Claims 1-20 are rejected under 35 U.S.C. 101 – abstract idea.
8. Examiner has disclosed Li et al. (“ChatTwin: Toward Automated Digital Twin Generation for Data Center via Large Language Models”), Hong et al. (“LLM-Twin: Mini-Giant Model-driven Beyond 5G Digital Twin Networking Framework with Semantic Secure Communication and Computation”), and Marr et al. (US PG-PUB 20250068799), which are the closest prior art as compared to instant application Independent Claims 1, 11, and 12. Li discloses ChatTwin, a conversational system that leverages the power of large language models to automate the generation of scene description documents for data center digital twins. Hong discloses a large language model empowered digital twins networking framework, called LLM-Twin, which utilizes LLMs to process multimodal data and provide semantic-level communication within the digital twin network. Marr discloses digital twin generation using generative transformer networks and large language models, which may receive real-world conditions, determine an abstract class, and generate code to define and customize the digital twin of the physical system for electronic testing. However, while Li, Hong, and Marr disclose the use of language models in unison with a digital twin system, Li, Hong, and Marr seemingly do not explicitly disclose the specific limitations of Independent Claims 1, 11, and 12 (and Claims 2-10 and 13-20 by virtue of dependency), including “submitting a hardware prompt to a language machine learning model, the hardware prompt characterizing hardware of an original system; submitting a software prompt to the language machine learning model, the software prompt characterizing software of the original system; […] and tuning the language machine learning model to act as a digital twin of the original system based on an output of the discriminant machine learning model, an output of the language machine learning model, and an output of the original system” in combination with the remaining limitations of the Independent claims.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devika S Maharaj whose telephone number is (571)272-0829. The examiner can normally be reached Monday - Thursday 8:30am - 5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571)270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEVIKA S MAHARAJ/Examiner, Art Unit 2123
