# US 11,994,409 B2 · Enriched aviation information notice > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,994,409 B2 - Document ID: US11994409B2 - Status: Granted - Kind: B2 - Application number: 17/653,815 - Filed: 2022-03-07 - Published: 2023-09-07 - Granted: 2024-05-28 - Earliest claimed: 2022-03-07 - Assignees: The Boeing Company - Applicants: The Boeing Company - Inventors: Dirk Thierbach; Thomas Winston Scott; Aaron E. Wood; Jeffrey N. Ross; Randolph F. Porter - Web page: https://patentgrep.com/patent/US11994409B2 ## CPC - G01C 21/387: {Organisation of map data, e.g. version management or database structures} - G01C 23/00: Combined instruments indicating more than one navigational value, e.g. for aircraft; Combined measuring devices for measuring two or more variables of movement, e.g. distance, speed or acceleration - G06F 16/243: {Natural language query formulation} - G06F 16/24573: {using data annotations, e.g. user-defined metadata} - G06F 16/29: Geographical information databases - G08G 5/20: Arrangements for acquiring, generating, sharing or displaying traffic information (arrangements for monitoring traffic G08G5/72) - G08G 5/21: located onboard the aircraft - G08G 5/22: located on the ground - G08G 5/26: Transmission of traffic-related information between aircraft and ground stations - G08G 5/55: for a single aircraft - G08G 5/76: for monitoring atmospheric conditions - G08G 5/59: in accordance with predefined flight zones, e.g. to avoid prohibited zones - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - 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}) ## Abstract A computing device is provided comprising a processor and a memory storing instructions executable by the processor. The instructions are executable to receive a raw aviation information notice that comprises text. A text language analysis model is used to extract metadata from the raw aviation information notice. The metadata comprises aviation operation information that describes a status of a component of an airspace system. Geospatial aviation data is received that corresponds to the component of the airspace system. The aviation operation information is mapped to the geospatial aviation data to thereby generate geometry data for the aviation operation information. The geometry data and the metadata are used to generate an enriched aviation information notice. The instructions are further executable to output the enriched aviation information notice. ## Claims ### Claim 1 A computing device, comprising: a processor; and a memory storing instructions executable by the processor to: receive a raw aviation information notice that comprises text, wherein the text describes a status of a component of an airspace system; use a text language analysis model to extract metadata from the raw aviation information notice, the metadata comprising a summary of the status of the component of the airspace system; receive a map that corresponds to a geographic area described in the raw aviation information notice; generate a graphical representation of the status of the component of the airspace system; map the graphical representation to a region of the map affected by the raw aviation information notice; generate an enriched aviation information notice, wherein the enriched aviation information notice comprises the map, the graphical representation, the summary of the status of the component of the airspace system extracted from the raw aviation information notice, and the raw aviation information notice; and output the enriched aviation information notice. ### Claim 2 (depends on 1) The computing device of claim 1, wherein the text describes the status of a facility, service, procedure, or hazard, and wherein the airspace system comprises an airspace region, a navigation facility, or an airport. ### Claim 3 (depends on 1) The computing device of claim 1, wherein the text language analysis model comprises a machine learning model trained on a training data set that comprises a plurality of aviation information notices. ### Claim 4 (depends on 1) The computing device of claim 1, wherein the text language analysis model comprises an explicitly programmed algorithm. ### Claim 5 (depends on 1) The computing device of claim 1, wherein the instructions are further executable to: classify the component of the airspace system described in the raw aviation information notice; based at least on a determination that the component is a first component type, use a first language analysis model to extract first metadata from the raw aviation information notice; and based at least on a determination that the component is a second component type, use a second language analysis model to extract second metadata from the raw aviation information notice. ### Claim 6 (depends on 5) The computing device of claim 5, wherein the enriched aviation information notice comprises the first metadata and the second metadata. ### Claim 7 (depends on 1) The computing device of claim 1, wherein the instructions executable to map the graphical representation to the region of the map affected by the raw aviation information notice comprise instructions executable to: generate a geographical representation of the status of the component over the map; extract boundary information from the metadata, wherein the boundary information defines a region of the map affected by the aviation operation information; and use the boundary information to refine the geographical representation to the region of the map affected by the raw aviation information notice. ### Claim 8 (depends on 1) The computing device of claim 1, wherein the instructions are executable to output the enriched aviation information notice to a database. ### Claim 9 (depends on 1) The computing device of claim 1, wherein the text language analysis model comprises a neural network or a generative model. ### Claim 10 (depends on 1) The computing device of claim 1, wherein the text language analysis model is configured to extract and label words and/or symbols in the text to thereby generate the metadata. ### Claim 11 At a computing device, a method for generating an enriched aviation information notice, the method comprising: receiving a raw aviation information notice comprising text, wherein the text describes a status of a component of an airspace system; using a text language analysis model to extract metadata from the raw aviation information notice, the metadata comprising a summary of the status of the component of the airspace system; receiving a map that corresponds to a geographic area described in the raw aviation information notice; generate a graphical representation of the status of the component of the airspace system; mapping the graphical representation to a region of the map affected by the raw aviation information notice; generating an enriched aviation information notice, wherein the enriched aviation information notice comprises the map, the graphical representation, the summary of the status of the component of the airspace system extracted from the raw aviation information notice, and the raw aviation information notice; and outputting the enriched aviation information notice. ### Claim 12 (depends on 11) The method of claim 11, wherein using the text language analysis model to extract the metadata from the raw aviation information notice comprises using a machine learning model trained on a training data set comprising a plurality of aviation information notices. ### Claim 13 (depends on 11) The method of claim 11, wherein using the text language analysis model to extract the metadata from the raw aviation information notice comprises using an explicitly programmed algorithm. ### Claim 14 (depends on 11) The method of claim 11, wherein mapping the graphical representation to the region of the map affected by the raw aviation information notice comprises: generating a geographical representation of the aviation operation information over the map; extracting boundary information from the metadata, the boundary information defining a region of the map affected by the aviation operation information; and using the boundary information to refine the geographical representation to the region of the map affected by the raw aviation information notice. ### Claim 15 A computing device, comprising: a processor; and a memory comprising instructions executable by the processor to: receive a raw aviation information notice that comprises text, wherein the text describes a status of a component of an airspace system; use a text language analysis model to extract metadata from the raw aviation information notice, wherein the metadata comprises a subset of the text, and wherein the subset of the text comprises a summary of the status of the component of the airspace system; receive a map that corresponds to a geographic area described in the raw aviation information notice; generate a graphical representation of the status of the component of the airspace system; map the graphical representation to a region of the map affected by the raw aviation information notice; generate an enriched aviation information notice, wherein the enriched aviation information notice comprises the map, the graphical representation, the summary of the status of the component of the airspace system extracted from the raw aviation information notice, and the raw aviation information notice; and output the enriched aviation information notice. ### Claim 16 (depends on 15) The computing device of claim 15, wherein the text language analysis model comprises a machine learning model trained on a training data set that comprises a plurality of aviation information notices. ### Claim 17 (depends on 15) The computing device of claim 15, wherein the text language analysis model comprises an explicitly programmed algorithm. ### Claim 18 (depends on 15) The computing device of claim 15, wherein the instructions executable to map the graphical representation to the region of the map affected by the raw aviation information notice comprise instructions executable to: generate a geographical representation of the status of the component of the airspace system over the map; extract boundary information from the metadata, wherein the boundary information defines a region of the map affected by the aviation operation information; and use the boundary information to refine the geographical representation to the region of the map affected by the raw aviation information notice. ### Claim 19 (depends on 15) The computing device of claim 15, wherein the text language analysis model comprises a neural network or a generative model. ### Claim 20 (depends on 15) The computing device of claim 15, wherein the text language analysis model is configured to extract and label words and/or symbols in the text to thereby generate the metadata.