# US 2025/0095650 A1 · System and/or method for semantic parsing of air traffic control audio > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095650 A1 - Document ID: US20250095650A1 - Status: Published application - Kind: A1 - Application number: 18/969,752 - Filed: 2024-12-05 - Published: 2025-03-20 - Earliest claimed: 2020-10-13 - Assignees: Merlin Labs, Inc. - Applicants: Merlin Labs, Inc. - Inventors: Michael Pust; Joseph Bondaryk; Matthew George - Web page: https://patentgrep.com/patent/US20250095650A1 ## CPC - G08G 5/26: Transmission of traffic-related information between aircraft and ground stations - G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue - G06F 40/20: Natural language analysis (semantic analysis of natural language G06F40/30) - G08G 5/21: located onboard the aircraft - G08G 5/265: for managing air traffic control [ATC] clearance - G08G 5/38: for identification of landing sites, e.g. of diversion airports - G08G 5/53: for cruising - G10L 15/16: using artificial neural networks - G10L 15/183: using context dependencies, e.g. language models - G10L 15/193: Formal grammars, e.g. finite state automata, context free grammars or word networks - G08G 5/34: for flight plan modification - G08G 5/55: for a single aircraft - G10L 15/063: {Training} - G10L 15/1822: {Parsing for meaning understanding} - G10L 2015/223: {Execution procedure of a spoken command} - G10L 2015/228: {of application context} - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract The method S200 can include: at an aircraft, receiving an audio utterance from air traffic control S210, converting the audio utterance to text, determining commands from the text using a question-and-answer model S240, and optionally controlling the aircraft based on the commands S250. The method functions to automatically interpret flight commands from the air traffic control (ATC) stream. ## Claims ### Claim 1 A method comprising: based on an Air Traffic Control (ATC) audio signal, determining a plurality of entity hypotheses which are phonetically-conflicted; based on a set of contextual information, determining a respective language score for each phonetically-conflicted entity hypothesis of the plurality using a language model; deconflicting the plurality of phonetically-conflicted entity hypotheses by selecting a first entity hypothesis of the plurality based on the respective language score; and performing an action based on the first entity hypothesis. ### Claim 2 (depends on 1) The method of claim 1, wherein the plurality of phonetically-conflicting entity hypotheses are selected from a predetermined lexicon based on an analysis the ATC audio signal. ### Claim 3 (depends on 2) The method of claim 2, wherein the analysis of the ATC audio signal comprises automatic speech recognition (ASR) with the language model. ### Claim 4 (depends on 1) The method of claim 1, wherein the language model is pretrained for entity pronunciations corresponding to the set of contextual information. ### Claim 5 (depends on 4) The method of claim 4, wherein plurality of phonetically-conflicting entity hypotheses comprise waypoints with distinct spellings. ### Claim 6 (depends on 4) The method of claim 4, wherein the action is further based on the set of contextual information. ### Claim 7 (depends on 6) The method of claim 6, wherein the action comprises requesting ATC clarification of a waypoint spelling via an ATC radio. ### Claim 8 (depends on 7) The method of claim 7, further comprising: based on analysis of an ATC response to the requested ATC clarification, updating at least one element selected from a set consisting of: the language model and the set of contextual information. ### Claim 9 (depends on 1) The method of claim 1, wherein performing the action comprises: commanding the aircraft based on the first entity hypothesis. ### Claim 10 (depends on 1) The method of claim 1, wherein the set of contextual information comprises: a geographic identifier and a waypoint pronunciation associated with the geographic identifier. ### Claim 11 (depends on 1) The method of claim 1, wherein the set of contextual information is associated with a regional pronunciation. ### Claim 12 (depends on 11) The method of claim 11, wherein the regional pronunciation comprises a colloquial speech pattern, a standardized dialectic speech pattern, English vocal sounds, colloquial jargon, or regional phrasing. ### Claim 13 (depends on 11) The method of claim 11, wherein the set of contextual information comprises a speaker tag for the audio signal. ### Claim 14 (depends on 1) The method of claim 1, wherein at least one entity hypothesis of the plurality is automatically determined by semantic parsing of ATC utterances based on a speaker tag and a regional dialectic associated with the speaker tag. ### Claim 15 (depends on 14) The method of claim 14, wherein the regional dialect comprises a subset of an entity lexicon specific to a geographic region. ### Claim 16 A method comprising: predicting a waypoint spelling for an audio signal, comprising: determining a plurality of waypoint spelling hypotheses for the audio signal; and determining a respective language score, using the language model, for each of the plurality of waypoint spelling hypotheses; and navigating based on the waypoint spelling prediction, wherein the waypoint spelling prediction is further based on contextual information. ### Claim 17 (depends on 16) The method of claim 16, wherein determining the plurality of waypoint spelling hypotheses for the audio signal comprises generating at least one waypoint spelling hypothesis by Automatic Speech Recognition (ASR). ### Claim 18 (depends on 17) The method of claim 17, wherein the at least one waypoint spelling hypothesis is generated using an ASR model with integrated Sentence Boundary Detection (SBD), which is pretrained for semantic parsing of Air Traffic Control (ATC) utterances from ATC radio. ### Claim 19 (depends on 16) The method of claim 16, wherein the contextual information comprises: a geographic identifier corresponding with a waypoint pronunciation. ### Claim 20 (depends on 16) The method of claim 16, wherein the contextual information comprises a colloquial speech pattern, wherein the language model is pretrained based on the colloquial speech pattern.