DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections – 35 USC § 101
2. 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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1 recites a method comprising:
receiving a vocal input message from a trainee;
converting the vocal input message into a text input message;
identifying a content of the text input message;
accessing and searching a database to determine an educational advice;
generating a response message containing the educational advice;
converting the response message into a vocal response message; and
providing the vocal response message to the trainee
The limitations of receiving a vocal input, converting it into text, identifying a content, searching a database, generating a response and providing the response, as drafted, constitutes a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, aside from the “large language model” language, which amounts to a computer programmed with a LLM algorithm to perform the processing, these steps could be practically being performed by a human for example by listening to a vocal message, identifying the content of the message, searching a database, and providing a response to the message. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites using a large language model to perform the processing and generating steps. The model in both steps is recited at a high-level of generality (i.e., as a generic large language model algorithm performing generic computer functions of converting text and generating a response) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this 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 further recites the method is used for pilot training on a simulator, extracting the request related to the simulator, and the data is about an aircraft on which the training is performed. This amounts to no more than generally linking the use of the judicial exception to a particular technological environment (pilot training simulators). See MPEP 2106.05(h). The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a LLM to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, as noted above the use of the method on a pilot training simulator only generally links the use of the exception to a particular technological environment. The claim is not patent eligible.
Dependent claims 2-15 recite the same abstract idea as in claim 1, only recite further abstract limitations (e.g. monitoring and evaluating trainee based on relevant metrics) being performed in the generic simulator using generic neural network elements, and generic computer elements configured to perform the method of claim 1. Therefore, these claims do not recite additional limitations sufficient to direct the claimed invention to significantly more.
Claim Rejections - 35 USC § 103
3. 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.
4. The factual inquiries 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.
5. Claims 1-10 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hagelin et al. (US 2008/0189092 A1) in view of Sheth (US 2024/0146563 A1).
Regarding claims 1, Hagelin discloses a method for assisting a trainee during a pilot training on a pilot training simulator (Par’s. 6-7), comprising:
receiving a vocal input message from a trainee (input question at step s307); and converting the vocal input message into a text input message (see Par. 64 – communication input unit may be a voice-controlled display unit);
processing the text input message to identify a content of the text input message and extract at least a request related to the pilot training simulator; and
accessing and searching a database (memory 15) based on the request to determine an educational advice, wherein the database includes data about an aircraft on which the pilot training is performed (Par. 65 – communication unit allows inputting of questions to simulation device which contains information about the simulation; and evaluation of answer during simulation – Par. 314 – Par’s. 103-104);
generating a response message (answer) containing the educational advice (at step s320 – Par. 109); and
converting the response message into a vocal response message; and providing the vocal response message to the trainee (s325 – answer information outputted for example using a loud speaker).
Hagelin does not appear to disclose processing the text input by a neural network configured as a large language model, LLM, to identify the content, and generating the response using the neural network. However, Sheth discloses utilizing an LLM engine 250 to process an input prompt during a simulation and provide a response (providing dynamically generated content utilizing LLM in response to a user input during a simulation – see Par’s. 39, 43, Fig. 3).
Accordingly, it would have been obvious to one skilled in the art before the effective filing date of the invention to modify the teachings of Hagelin by utilizing an LLM, as taught by Sheth. Such a modification would involve applying a known technique to a known method ready for improvement to yield predictable results.
Regarding claims 2-6, 8-10 and 13-15, Hagelin in view of Sheth further discloses monitoring the trainee during the pilot training to identify a current action; accessing and searching the database to determine a best action based on the identified current action of the trainee and relevant information from the database; accessing and searching the database to determine a predicted action based on the identified current action of the trainee and past trainee data, wherein the database includes the past trainee data using the neural network; and evaluating whether to perform a corrective action based on a comparison of the best action and the predicted action (Hagelin, Par. 70 – scenario modification unit 165 communicates with simulation preparation unit to identify actions and requests from user and determining whether to perform corrective action, i.e. change scenario) (as per claim 2)
determining the predicted action includes predicting a token based on the vocal input message using the neural network (Hagelin, Par. 76 - “question requiring prediction simulation”) (as per claim 3),
monitoring the trainee is performed in real-time, or wherein the method further comprises a step of comparing the current action with a command history recorded by the pilot training simulator, or both (Hagelin - Par’s. 49-50) (as per claim 4),
the request is related to at least one of an aircraft control element, a flight operation of the aircraft, an activation or a deactivation of a component of the simulated aircraft on the pilot training simulator (Hagelin, Par. 56) (as per claim 5),
the neural network is trained at least with information about the simulated aircraft on the pilot training simulator (this modification of Hagelin using the teachings of Sheth would be obvious for the reasons stated above with respect to claim 1) (as per claim 6),
receiving a current system status of the pilot training simulator, wherein searching the database is based on the current system status (Par. 79) (as per claim 8),
applying reinforcement learning to the neural network comprising a policy, which is based on the current system status (Sheth, Par. 39) (as per claim 9),
extracting an implicit request from the text input message using the neural network; and supplying the implicit request to the request of the text input message before accessing and searching the database to determine a response to the input message (Par. 76) (as per claim 10),
an input terminal, a speaker, a data storage comprising a database, and a processor configured to perform the method according to claim 1 (as per claim 13), a pilot training simulator comprising: the data processing apparatus according to claim 13 (as per claim 14), and a non-transitory computer readable medium storing a computer program comprising instructions, which, when the program is executed by a processor of a computer, causes the computer to carry out the method of claim 1 (as per claim 15) (Hagelin, Par’s. 58, 116).
Regarding claim 7, to the extent that Sheth does not explicitly disclose the neural network is configured to a GPT or a generative artificial intelligence, the examiner takes OFFICIAL NOTICE that the use of LLM’s and neural networks configured as GPT or generative AI were well known to those of ordinary skill in the art before the effective filing date of the invention. Accordingly, it would have been obvious to one skilled in the art before the effective filing date of the invention to utilize one of these models. Such a modification would involve a simple substitution of one known element for another to obtain predictable results.
6. Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Hagelin et al. (US 2008/0189092 A1) in view of Sheth (US 2024/0146563 A1), and further in view of Lechner et al. (US 2018/0286269 A1).
Regarding claims 11 and 12, the combination of Hagelin and Sheth does not appear to explicitly disclose determining a stress level of the trainee based on the vocal input message using the neural network, wherein generating the response message includes generating the response message based on the stress level of the trainee (as per claim 11), and determining the stress level is based on the implicit request extracted from the text input message (as per claim 12). However, Lecnher discloses the concept and advantages of detecting stress levels of a pilot in a simulator based on vocal inputs and generating responses according were well known to those of ordinary skill in the art before the effective filing date of the invention (see Par. 22, last 5 lines). Accordingly, it would have been obvious to one skilled in the art before the effective filing date of the invention to modify the combination of Hagelin and Sheth by determining stress level and generating the responses based on the users’s vocal input messages, as taught by Lechner, in order to enhance the realism of the simulation.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER EGLOFF whose telephone number is (571)270-3548. The examiner can normally be reached on Monday - Friday 9:00 am - 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Peter R Egloff/
Primary Examiner, Art Unit 3715