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
This Office action is in response to the RCE filed on December 11, 2025 regarding the amendment filed on November 12, 2025. Claims 1, 11 and 22 have been amended. Claim 21 has been cancelled. New claim 23 has been added. Thus, claims 1-19 and 22-23 are pending. Claims 1, 11 and 22 are independent.
Response to Amendments
Applicant's amendments, regarding claim rejections under 35 U.S.C. § 103, filed November 12, 2025, have been fully considered but are not persuasive.
Examiner responded in the Advisory Action of December 3, 2025 that the proposed amendments to the independent claims of utilizing large language models (LLM) in aviation is well known and obvious and is sufficiently disclosed in the previously cited prior art of Bonnet (US 20180182250 A1) and rearticulated below with further supporting paragraphs from Bonnet.
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.
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.
Claims 1-5, 10-14, 19 and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Bonnet (US-20180182250-A1) in view of Waaler (US-20240144832-A1).
Regarding claim 1, Bonnet teaches a system (see Bonnet, Abstract, paragraphs 8-12, regarding “a method, implemented on a computer (of a system), for managing notice to airmen”), comprising:
a communication device configured to receive data from notification sources (see Bonnet, figures 2-3, paragraphs 72-73 and 90, regarding flight management system FMS 210, wherein in step 310 “(at least) one NOTAM message is received via the Internet in an EFB and/or by a SATCOM network in the FMS (communication device). The NOTAM messages are collected and are stored locally and/or remotely”); and
an artificial intelligence control unit coupled to the communication device, the artificial intelligence control unit (see Bonnet, figure 2, paragraphs 23, 72-73 and 101-103, regarding a method, implemented by a computer (control unit) to perform an analysis step comprising “one or more “deep learning” steps. Deep learning denotes data modelling methods in particular using neural networks (artificial intelligence)”, wherein the computer comprises the FMS 210 (communication device)) configured to:
receive data, via the communication device, from the notification sources, wherein the data relate to an aircraft being operated by a pilot (see Bonnet, paragraph 8, regarding “receiving at least one notice (notification, for example, a NOTAM) to airmen (pilots); receiving one or more elements of a navigation database associated (related) with a flight plan of an aircraft (operated by a pilot)”),
automatically determine, by using an aviation-based large language model (see Bonnet, paragraphs 32 (natural language), 101 (classification with supervised learning), and 104 (big data/high volume modeling), exemplary of natural language supervised (deep) learning using neural networks on "big data" (high volume) is data modeling using large language models (LLM), wherein well known and obvious since the release of OpenAI GPT-1 in June 2018, widely recognized as the first practical utilization of natural language processing with LLM's, they have become ubiquitous and being adopted in many industries, including aviation at the time of Applicant's effective filing date) and without human intervention relevant information for operating the aircraft from the data (see Bonnet, paragraph 44, regarding “the flight context also (automatically without human intervention) triggers some/certain (determination) comparisons. The flight context of the aircraft includes in particular the climbing, descent, cruising, take-off, landing etc. phases. The flight context may be used to filter/select (determine) the relevant NOTAM messages, i.e. those having to be taken into account for the flight plan of the aircraft (e.g. current, potential, revised),
Bonnet does not teach wherein the artificial intelligence control unit is configured to automatically ignore and discard one or more portions of the data below a relevancy threshold.
However, Waaler remedies this shortfall with a teaching of a system and method to improve notice to airman (NOTAM) readability by assigning a relevancy value to each NOTAM, generate an enhanced (reduced) set of NOTAM’s that fall within a threshold distance, are most relevant, or some other relevant-based metric such that “in this regard, the most relevant NOTAMs are enhanced and displayed, reducing the number of irrelevant NOTAMs shown to a user (for example, the pilot)”, wherein “NOTAMs deemed irrelevant may be filtered out (e.g., labeled as irrelevant, ignored, removed from a list, not enhanced, not displayed, and the like)” (see Waaler, Abstract, figure 2, paragraphs 46 and 53-58, regarding steps 206, 206b, 208 and 210).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s effective filing date to modify the system of Bonnet to further comprise the filtering of NOTAM’s bases on relevancy of Waaler, because this mitigates cockpit information overflow and improves flight operations for the pilot in command by discard irrelevant notifications, therefore, modified Bonnet enables automatically determine, without human intervention, relevant information for operating the aircraft from the data, wherein the artificial intelligence control unit is configured to automatically ignore and discard one or more portions of the data below a relevancy threshold.
communicate, via the communication device, with the aircraft (see Bonnet, figure 2, paragraph 77, regarding FMS system 210 (communication device) communicating with the aircraft 200 via autopilot 211),
provide an information presentation including the relevant information on a display of a user interface of the aircraft, (see Bonnet, figure 3, paragraphs 122-123, regarding step 341 of Notify, “to (provide) the pilot using different methods (visual and/or acoustic and/or haptic, etc.)”, such as, for example a display user interface for visual presentation of notifications/alerts/warnings) wherein the information presentation is devoid of the one or more portions of the data below the relevancy threshold (see Waaler, figure 2, paragraphs 46 and 53-58, regarding steps 206b of “NOTAMs deemed irrelevant (not meeting relevancy threshold) may be filtered out (e.g., labeled as irrelevant, ignored, removed from a list (devoided), not enhanced, not displayed, and the like)”), and
use feedback from one or more pilots to refine the aviation-based large language model in order to further refine selection of the relevant information in the future (see Bonnet, paragraphs 101-104, regarding “analysis step comprises one or more “deep learning” steps. Deep learning denotes data modelling methods in particular using neural networks”, wherein well known and obvious in the art is that a Large Language Model (LLM) relies on feedback loops for continuous improvement, for example, RLHF (Reinforcement Learning from Human Feedback), therefore it would be obvious to deploy a LLM using feedback from one or more pilots to improve (refine) the LLM)),
wherein the aircraft is operated based on the relevant information (see Bonnet, figure 3, paragraphs 122-123, regarding “For example, if there is ongoing maintenance work at the selected airport, the pilot is informed or warned thereof. Such warnings (relevant information) enable the pilot to acknowledge his mistake and to correct it quickly”, exemplary of the aircraft operation to correct a potential hazard ahead based on the relevant information/notification).
Regarding claim 2, modified Bonnet teaches the system of claim 1, including wherein the artificial intelligence control unit is remote from the aircraft (see Bonnet paragraphs 79, 90 and 112, regarding the “analysis steps carried out by computers (control units), … these steps may be carried out remotely”, exemplary of an artificial intelligence control unit remote from the aircraft (off-board)).
Regarding claim 3, modified Bonnet teaches the system of claim 1, including wherein the artificial intelligence control unit is onboard the aircraft (see Bonnet, figure 3, paragraphs 122-123, regarding step 341 of Notify, “to (provide) the pilot using different methods (visual and/or acoustic and/or haptic, etc.)”, such as, for example a display user interface for visual presentation of notifications/alerts/warnings, therefore the computer (control unit with its display component) is co-located and onboard the aircraft).
Regarding claim 4, modified Bonnet teaches the system of claim 1, including wherein the notification sources comprise one or more of: a tracking sub-system configured to track the aircraft; a weather sub-system; aviation data sources that provide information regarding aviation flight operations; aircraft data sources that provide information about the aircraft; or airport data sources that provide information regarding one or more airports (see Bonnet, paragraphs 21-22, regarding “NOTAMs contain highly diverse information regarding air navigation (one-off restriction or hazard, unavailability of a radio navigation item of equipment, of a runway, development of airport infrastructures (airport data source), modification of a ground facility, presence of obstacles, e.g. a crane, or zones undergoing maintenance in the vicinity of an airport). More generally, a NOTAM may provide information relating to any hazard to air navigation. It may also define no-fly zones” and “NOTAM messages may deal with various subjects, in particular: organization of airspace, radar communications and installations, facilities and services, instrument landing devices, lighting installations, movement and landing areas, en-route and terminal zone navigation equipment, air traffic procedures (aviation flight operations), navigation alerts (e.g. airspace restrictions), in-flight monitoring (encompasses the real-time tracking and analysis of an aircraft's flight path, systems, and operational data during flight) and in-flight weather services (weather sub-system), navigation alerts, diverse information regarding availability, condition changes and hazardous conditions, and restrictions”).
Regarding claim 5, modified Bonnet teaches the system of claim 1, including wherein the notification sources comprise: a tracking sub-system configured to track the aircraft; a weather sub-system; aviation data sources that provide information regarding aviation flight operations; aircraft data sources that provide information about the aircraft; and airport data sources that provide information regarding one or more airports (see Bonnet, paragraphs 21-22, regarding “NOTAMs contain highly diverse information regarding air navigation (one-off restriction or hazard, unavailability of a radio navigation item of equipment, of a runway, development of airport infrastructures (airport data source), modification of a ground facility, presence of obstacles, e.g. a crane, or zones undergoing maintenance in the vicinity of an airport). More generally, a NOTAM may provide information relating to any hazard to air navigation. It may also define no-fly zones” and “NOTAM messages may deal with various subjects, in particular: organization of airspace, radar communications and installations, facilities and services, instrument landing devices, lighting installations, movement and landing areas, en-route and terminal zone navigation equipment, air traffic procedures (aviation flight operations), navigation alerts (e.g. airspace restrictions), in-flight monitoring (encompasses the real-time tracking and analysis of an aircraft's flight path, systems, and operational data during flight) and in-flight weather services (weather sub-system), navigation alerts, diverse information regarding availability, condition changes and hazardous conditions, and restrictions”).
Regarding claim 10, modified Bonnet teaches the system of claim 1, including wherein the artificial intelligence control unit is further configured to automatically operate the aircraft based on the relevant information (see Bonnet, figure 2, paragraph 77, regarding autopilot device 210 that automatically performing maneuvers based on relevant information).
Regarding claim 23, modified Bonnet teaches the system of claim 1, including wherein the aviation-based large language model is aviation specific, wherein the artificial intelligence control unit is further configured to use the aviation-based large language model to provide machine learning pattern recognition in supervised algorithms to perform classifications, regressions, and decision-tree operations specific to inputs from aeronautical datasets and usage analytics (see Bonnet, paragraphs 32 (natural language), 101 (classification with supervised learning), exemplary of natural language processing (semantic analysis/pattern recognition) performing supervised learning for classification analytics).
Regarding claims 11-14 and 19, independent claim 11 is a method that performs the identical function of the system of independent claim 1, and similarly, dependent claims 12-14 and 19 of independent claim 11 also performs the identical functions corresponding to dependent claims 2-3, 5 and 10 of independent claim 1, respectively, therefore, claims 11-14 and 19 are also rejected under 35 USC § 103 for the same respective rationale as claims 1-3, 5 and 10.
Regarding claim 22, independent claim 22 is a system comprising: an aircraft;
a communication device configured to receive data from notification sources; and an artificial intelligence control unit coupled to the communication device, the artificial intelligence control unit configured to perform the identical function of the system of independent claim 1, therefore, claim 22 is also rejected under 35 USC § 103 for the same rationale as claim 1.
Claims 6-9 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Bonnet (US-20180182250-A1) in view of Waaler (US-20240144832-A1) and further in view of Ramaiah (US-20160047674-A1).
Regarding claim 6, modified Bonnet teaches the system of claim 1, excluding wherein the information presentation comprises a map of a current location of the aircraft.
However, Ramaiah remedies this shortfall with a teaching of “a system and method for providing integrated time-based notification and aircraft status data on a (single) display is provided” wherein the display includes a navigation map showing current location of the aircraft (see Ramaiah, Abstract, figure 3, paragraph 44, regarding display device 102).
It would have been obvious to one of ordinary skill in the art at the time of Applicants effective filing date to modify the system of Bonnet to further comprise the integrated/single display of Ramaiah because this improves a pilot’s situational awareness by limiting flight information in real-time to only relevant information needed for flight operations; therefore, modified Bonnet enables wherein the information presentation comprises a map of a current location of the aircraft.
Regarding claim 7, modified Bonnet teaches the system of claim 1, excluding wherein the information presentation comprises information prompts that show the relevant information on the display.
However, Ramaiah remedies this shortfall with a teaching of “a system and method for providing integrated time-based notification and aircraft status data on a (single) display is provided” wherein the display includes a navigation map showing current location of the aircraft (see Ramaiah, Abstract, figure 3, paragraph 44, regarding display device 102).
It would have been obvious to one of ordinary skill in the art at the time of Applicants effective filing date to modify the system of Bonnet to further comprise the integrated/single display of Ramaiah because this improves a pilot’s situational awareness by limiting flight information in real-time to only relevant information needed for flight operations; therefore, modified Bonnet enables wherein the information presentation comprises information prompts that show the relevant information on the display (see Ramaiah, paragraphs 10 and 66, regarding “Symbology associated with the notification (information presentation) is selected and detailed information associated with the selected notification (relevant information) is displayed”, wherein “user notifications may require additional steps of prompting the user to provide text content for an associated message and position/location information for the associated notification”).
Regarding claim 8, further modified Bonnet teaches the system of claim 7, including wherein the information prompts comprise one or more feedback input indicators that allow the pilot to provide feedback to the artificial intelligence control unit (see Ramaiah, figure 15, paragraphs 51 and 66, regarding “Pilot input data regarding a predetermined origin or time zero, zoom, and other time scale preferences (feedback inputs) is reflected in the time scale region displayed at STEP 1504).
Regarding claim 9, modified Bonnet teaches the system of claim 1, excluding wherein the information presentation comprises: a map of a current location of the aircraft.
However, Ramaiah remedies this shortfall with a teaching of “a system and method for providing integrated time-based notification and aircraft status data on a (single) display is provided” wherein the display includes a navigation map showing current location of the aircraft (see Ramaiah, Abstract, figure 3, paragraph 44, regarding display device 102).
It would have been obvious to one of ordinary skill in the art at the time of Applicants effective filing date to modify the system of Bonnet to further comprise the integrated/single display of Ramaiah because this improves a pilot’s situational awareness by limiting flight information in real-time to only relevant information needed for flight operations; therefore, modified Bonnet enables wherein the information presentation comprises: a map of a current location of the aircraft (see Ramaiah, Abstract, figure 3, paragraph 44, regarding display device 102 showing current location of aircraft on a navigation map); and information prompts that show the relevant information on the display, wherein the information prompts comprise one or more feedback input indicators that allow the pilot to provide feedback to the artificial intelligence control unit (see Ramaiah, figure 15, paragraphs 51 and 66, regarding “Pilot input data regarding a predetermined origin or time zero, zoom, and other time scale preferences (feedback inputs) is reflected in the time scale region displayed at STEP 1504).
Regarding claims 15-18, independent claim 11 is a method that performs the identical function of the system of independent claim 1, and similarly, dependent claims 15-18 of independent claim 11 also performs the identical functions corresponding to dependent claims 6-9 of independent claim 1, respectively, therefore, claims 15-18 are also rejected under 35 USC § 103 for the same respective rationale as claims 6-9.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached form PTO-892.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER NING whose telephone number is (408) 918-7664. The examiner can normally be reached Monday - Thursday and alternate Fridays, 7:30-4:30 PT.
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, Peter D. Nolan can be reached at 571-270-7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/P.Y.N./Examiner, Art Unit 3661
December 27, 2025
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661