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. Information Disclosure Statement The information disclosure statement (IDS) submitted on September 16, 2021, December 1, 2022, and September 13, 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Objections Claim 7 is objected to because of the following informalities: line 2 recites “a target”. It is not clear if this is the same element as “one or more targets” recited in line 2 of claim 1. For examination purposes, the examiner is considering the “targets” to be the same element. Claim 8 objected to because of the following informalities: line 2 and line 4 recite “a target”. It is not clear if this is the same element as “one or more targets” recited in line 2 of claim 1. For examination purposes, the examiner is considering the “targets” to be the same element. Claim 18 is objected to because of the following informalities: line 2 and line 4 recite “a target”, line 3 recites “the target”. It is not clear if this is the same element as “one or more targets” recited in line 7 of claim 13. For examination purposes, the examiner is considering the “targets” to be the same element. Claim Rejections - 35 USC § 103 Claim(s) 1 – 11, 13 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sakamaki et al. ( US 2023/0215277 A1 ) (herein after Sakamaki ) in view of CRANS et al. ( US 2021/0159965 A1 ) (herein after Crans ). Regarding Claim 1, Sakamaki teaches, a method comprising: receiving airspace surveillance data ( Fig. 1A ¶ 24 disclosed herein are methods, to identify and track objects in airspace ) from a plurality of sensors ( Fig. 1A, ¶ 100 multiple sensor devices 102-112 ), the airspace surveillance data comprising tracks ( Fig. 4, ¶ 98 sensor track data 402; Examiner interpretation: Fig 4 is part of Fig 1A, see ¶ 98 ) associated with one or more targets ( Fig. 1A, target object 130 ); aggregating the airspace surveillance data to obtain aggregated data ( Fig. 4, generate output tracks 404 ); — determining one or more candidate associations between the tracks ( Fig. 4, ¶ 100 instances 408-412 ) and the clusters ( Fig. 4, ¶ 100 track 1 association 406 ); associating each of the tracks with one of the one or more targets ( Fig. 5, ¶ 104 various tracks (e.g., 502-508); Examiner interpretation: Fig 5 is part of Fig 1A, see ¶ 102 ) based on the candidate associations ( Fig. 5, ¶ 104 instances 408, 410, 412 can be fused into a single fused track 510 ); and estimating a track for each of the one or more targets ( Fig. 4, ¶ 94 perform track fusion 310 ) based on the associations between the tracks and the one or more targets ( Fig. 5, ¶ 104 fused track 510 ). Sakamaki fails to teach, — performing density-based clustering of the aggregated data to obtain a plurality of clusters; — In analogous art, Crans teaches, — performing density-based clustering ( Fig. 1A, ¶ 19 density-based scan clustering with noise (DBSCAN) algorithm ) of the aggregated data to obtain a plurality of clusters; — It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki by combining the method performed by track fusion system 302 taught by Sakamaki with a method performed by aerial corridor management platform 101, for performing density-based clustering of the aggregated data to obtain a plurality of clusters; taught by Crans for the benefit of receiving airspace surveillance data by providing consistent communication without interfering with ground data [ Crans : ¶ 10 provide a consistent communication signal for the UAV, through focused beams that are not intended to interfere with any ground traffic. ]. Regarding Claim 2, Sakamaki in view of Crans teach the limitations of claim 1, which this claim depends on. Sakamaki further teaches, t he method of claim 1, wherein the tracks comprise a plurality of positions of the one or more targets ( Fig. 1A, ¶ 74 location parameters (e.g., range, angle, velocity, position, etc.), for the target object 130 ) recently measured by the plurality of sensors. Regarding Claim 3, Sakamaki in view of Crans teach the limitations of claim 1, which this claim depends on. Crans further teaches, the method of claim 1, wherein the aggregated data comprises positions of the one or more targets ( Fig. 1F, ¶ 54 waypoints associated with the flight path ) measured by the plurality of sensors within a predetermined threshold duration ( Fig. 1F, ¶ 54 threshold of cached content ) in the past. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki in view of Crans by combining the method performed by track fusion system 302 taught by Sakamaki in view of Crans with a method performed by aerial corridor management platform 101 wherein, the aggregated data comprises positions of the one or more targets measured by the plurality of sensors within a predetermined threshold duration in the past; taught by Crans for the benefit of receiving airspace surveillance data by providing consistent communication without interfering with ground data [ Crans : ¶ 10 provide a consistent communication signal for the UAV, through focused beams that are not intended to interfere with any ground traffic. ]. Regarding Claim 4, Sakamaki in view of Crans teach the limitations of claim 1, which this claim depends on. Crans further teaches, the method of claim 1, further comprising performing the density-based clustering of the aggregated data using DBSCAN clustering algorithm ( Fig. 1A, ¶ 19 density-based scan clustering with noise (DBSCAN) algorithm ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki in view of Crans by combining the method performed by track fusion system 302 taught by Sakamaki in view of Crans with a method performed by aerial corridor management platform 101 further comprising, performing the density-based clustering of the aggregated data using DBSCAN clustering algorithm; taught by Crans for the benefit of receiving airspace surveillance data by providing consistent communication without interfering with ground data [ Crans : ¶ 10 provide a consistent communication signal for the UAV, through focused beams that are not intended to interfere with any ground traffic. ]. Regarding Claim 5, Sakamaki in view of Crans teach the limitations of claim 1, which this claim depends on. Crans further teaches, the method of claim 1, further comprising determining a ranking ( Fig. 1A, ¶ 20 classifying ) of the candidate associations between the tracks and the clusters ( Fig. 1A, ¶ 20 classifying the one or more conditions may affect a determination of waypoints for the flight path ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki in view of Crans by combining the method performed by track fusion system 302 taught by Sakamaki in view of Crans with a method performed by aerial corridor management platform 101 further comprising, determining a ranking of the candidate associations between the tracks and the clusters; taught by Crans for the benefit of receiving airspace surveillance data by providing consistent communication without interfering with ground data [ Crans : ¶ 10 provide a consistent communication signal for the UAV, through focused beams that are not intended to interfere with any ground traffic. ]. Regarding Claim 6, Sakamaki in view of Crans teach the limitations of claim 5, which this claim depends on. Crans further teaches, the method of claim 5, wherein the ranking of the candidate associations between the tracks and the clusters is determined based on a number of elements ( Fig. 1C, ¶ 36 applicable waypoints ) from a track in a cluster ( Fig. 1C, ¶ 36 applicable waypoints that fulfill the one or more updated parameters and the one or more conditions ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki in view of Crans by combining the method performed by track fusion system 302 taught by Sakamaki in view of Crans with a method performed by aerial corridor management platform 101 wherein, the ranking of the candidate associations between the tracks and the clusters is determined based on a number of elements from a track in a cluster; taught by Crans for the benefit of receiving airspace surveillance data by providing consistent communication without interfering with ground data [ Crans : ¶ 10 provide a consistent communication signal for the UAV, through focused beams that are not intended to interfere with any ground traffic. ]. Regarding Claim 7, Sakamaki in view of Crans teach the limitations of claim 5, which this claim depends on. Crans further teaches, the method of claim 5, further comprising associating each of the tracks with a target ( Fig. 1A, ¶ 20 flight path of the UAV 105 ) based on the ranking of the candidate associations between the tracks and the clusters ( Fig. 1A, ¶ 20 one or more conditions may affect a determination of waypoints for the flight path ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki in view of Crans by combining the method performed by track fusion system 302 taught by Sakamaki in view of Crans with a method performed by aerial corridor management platform 101 further comprising, associating each of the tracks with a target based on the ranking of the candidate associations between the tracks and the clusters; taught by Crans for the benefit of receiving airspace surveillance data by providing consistent communication without interfering with ground data [ Crans : ¶ 10 provide a consistent communication signal for the UAV, through focused beams that are not intended to interfere with any ground traffic. ]. Regarding Claim 8, Sakamaki in view of Crans teach the limitations of claim 1, which this claim depends on. Sakamaki further teaches, the method of claim 1, wherein the airspace surveillance data from at least one of the plurality of sensors comprises cooperative sensor data ( Fig. 1A, ¶ 24 ADS-B data ) that identifies a target associated with the cooperative sensor data ( Fig. 1A, ¶ 24 first and second signal can include ADS-B data ); and the method further comprises associating each of the tracks with a target ( Fig. 1A, target object 130 ) based at least in part on the identification of the target associated with the cooperative sensor data ( Fig. 1A, ¶ 24 signal can include measurements and/or sensed data representing a motion of the target object. ). Regarding Claim 9, Sakamaki in view of Crans teach the limitations of claim 1, which this claim depends on. Sakamaki further teaches, the method of claim 1, further comprising estimating the track for each of the one or more targets ( Fig. 1A, ¶ 65 estimate or predict the target object's states ) based on the airspace surveillance data received from a preferred sensor ( Fig. 1A, ¶ 63 radar sensor ( e.g., 102) ) of the plurality of sensors. Regarding Claim 10, Sakamaki in view of Crans teach the limitations of claim 1, which this claim depends on. Sakamaki further teaches, the method of claim 1, further comprising estimating the track for each of the one or more targets ( Fig. 1A, ¶ 65 estimate or predict the target object's states ) based on a Kalman filter ( Fig. 1A, ¶ 64 multiple filters 116, such as Kalman filters ). Regarding Claim 11, Sakamaki in view of Crans teach the limitations of claim 1, which this claim depends on. Sakamaki further teaches, the method of claim 1, further comprising estimating ( Fig. 1A, ¶ 65 estimate or predict the target object's states ) the track for each of the one or more targets based on covariance intersection of the tracks ( Fig. 1A, ¶ 64 generate respective state estimates and error covariances for the target object (e.g., 130) ) associated with each target. Regarding Claim 13, Sakamaki teaches, an apparatus ( Fig. 12, system architecture 1200 ) comprising: one or more processors; one or more memory modules ( Fig. 12, ¶ 165 system memory 1215, such as read only memory (ROM) 1220 and random access memory (RAM) 1225 ); and machine-readable instructions stored in the one or more memory modules ( Fig. 12, ¶ 165 software instructions are incorporated into the actual processor design; Examiner interpretation: Fig 12 is part of Fig 1A, see ¶ 164 ) that, when executed by the one or more processors, cause the apparatus to: receive airspace surveillance data ( Fig. 1A ¶ 24 disclosed herein are methods, to identify and track objects in airspace ) from a plurality of sensors ( Fig. 1A, ¶ 100 multiple sensor devices 102-112 ), the airspace surveillance data comprising tracks ( Fig. 4, ¶ 98 sensor track data 402; Examiner interpretation: Fig 4 is part of Fig 1A, see ¶ 98 ) associated with one or more targets ( Fig. 1A, target object 130 ); aggregate the airspace surveillance data to obtain aggregated data ( Fig. 4, generate output tracks 404 ); — determine one or more candidate associations between the tracks ( Fig. 4, ¶ 100 instances 408-412 ) and the clusters ( Fig. 4, ¶ 100 track 1 association 406 ); associate each of the tracks with one of the one or more targets ( Fig. 5, ¶ 104 various tracks (e.g., 502-508); Examiner interpretation: Fig 5 is part of Fig 1A, see ¶ 102 ) based on the candidate associations ( Fig. 5, ¶ 104 instances 408, 410, 412 can be fused into a single fused track 510 ); and estimate a track for each of the one or more targets ( Fig. 4, ¶ 94 perform track fusion 310 ) based on the associations between the tracks and the one or more targets ( Fig. 5, ¶ 104 fused track 510 ). Sakamaki fails to teach, — perform density-based clustering of the aggregated data to obtain a plurality of clusters; — In analogous art, Crans teaches, — perform density-based clustering ( Fig. 1A, ¶ 19 density-based scan clustering with noise (DBSCAN) algorithm ) of the aggregated data to obtain a plurality of clusters; — It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki by combining the track fusion system 302 taught by Sakamaki with aerial corridor management platform 101 to causing it to, perform density-based clustering of the aggregated data to obtain a plurality of clusters; taught by Crans for the benefit of receiving airspace surveillance data by providing consistent communication without interfering with ground data [ Crans : ¶ 10 provide a consistent communication signal for the UAV, through focused beams that are not intended to interfere with any ground traffic. ]. Regarding Claim 14, Sakamaki in view of Crans teach the limitations of claim 13, which this claim depends on. Sakamaki further teaches, wherein the tracks comprise a plurality of positions of the one or more targets ( Fig. 1A, ¶ 74 location parameters (e.g., range, angle, velocity, position, etc.), for the target object 130 ) recently measured by the plurality of sensors — Crans further teaches, — and the aggregated data comprises positions of the one or more targets ( Fig. 1F, ¶ 54 waypoints associated with the flight path ) measured by the plurality of sensors within a predetermined threshold duration ( Fig. 1F, ¶ 54 threshold of cached content ) in the past. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki in view of Crans by combining the track fusion system 302 taught by Sakamaki in view of Crans with aerial corridor management platform 101, wherein the tracks comprise the aggregated data comprises positions of the one or more targets measured by the plurality of sensors within a predetermined threshold duration in the past; taught by Crans for the benefit of receiving airspace surveillance data by providing consistent communication without interfering with ground data [ Crans: ¶ 10 provide a consistent communication signal for the UAV, through focused beams that are not intended to interfere with any ground traffic. ] . Regarding Claim 15, Sakamaki in view of Crans teach the limitations of claim 13, which this claim depends on. Crans further teaches, the apparatus of claim 13, wherein the instructions, when executed by the one or more processors, cause the apparatus to perform the density-based clustering of the aggregated data using DBSCAN clustering algorithm ( Fig. 1A, ¶ 19 density-based scan clustering with noise (DBSCAN) algorithm ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki in view of Crans by combining the track fusion system 302 taught by Sakamaki in view of Crans with aerial corridor management platform 101 wherein the instructions, when executed by the one or more processors, cause the apparatus to perform the density-based clustering of the aggregated data using DBSCAN clustering algorithm; taught by Crans for the benefit of receiving airspace surveillance data by providing consistent communication without interfering with ground data [ Crans: ¶ 10 provide a consistent communication signal for the UAV, through focused beams that are not intended to interfere with any ground traffic. ] . Regarding Claim 16, Sakamaki in view of Crans teach the limitations of claim 13, which this claim depends on. Crans further teaches, the apparatus of claim 13, wherein the instructions, when executed by the one or more processors, further cause the apparatus to determine a ranking ( Fig. 1A, ¶ 20 classifying ) of the candidate associations between the tracks and the clusters ( Fig. 1A, ¶ 20 classifying the one or more conditions may affect a determination of waypoints for the flight path ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki in view of Crans by combining the track fusion system 302 taught by Sakamaki in view of Crans with aerial corridor management platform 101 wherein, the instructions when executed by the one or more processors, further cause the apparatus to determine a ranking of the candidate associations between the tracks and the clusters; taught by Crans for the benefit of receiving airspace surveillance data by providing consistent communication without interfering with ground data [ Crans: ¶ 10 provide a consistent communication signal for the UAV, through focused beams that are not intended to interfere with any ground traffic. ]. Regarding Claim 17, Sakamaki in view of Crans teach the limitations of claim 16, which this claim depends on. Crans further teaches, the apparatus of claim 16, wherein the ranking of the candidate associations between the tracks and the clusters is based on a number of elements ( Fig. 1C, ¶ 36 applicable waypoints ) from a track in a cluster ( Fig. 1C, ¶ 36 applicable waypoints that fulfill the one or more updated parameters and the one or more conditions ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki in view of Crans by combining the track fusion system 302 taught by Sakamaki in view of Crans with aerial corridor management platform 101 wherein, the ranking of the candidate associations between the tracks and the clusters is based on a number of elements from a track in a cluster; taught by Crans for the benefit of receiving airspace surveillance data by providing consistent communication without interfering with ground data [ Crans: ¶ 10 provide a consistent communication signal for the UAV, through focused beams that are not intended to interfere with any ground traffic. ]. Regarding Claim 18, Sakamaki in view of Crans teach the limitations of claim 13, which this claim depends on. Sakamaki further teaches, the apparatus of claim 13, wherein: the airspace surveillance data from at least one of the plurality of sensors comprises cooperative sensor data ( Fig. 1A, ¶ 24 ADS-B data ) that identifies a target associated with the cooperative sensor data ( Fig. 1A, ¶ 24 first and second signal can include ADS-B data ); and the instructions, when executed by the one or more processors, further cause the apparatus to associate each track with a target ( Fig. 1A, target object 130 ) based at least in part on the identification of the target associated with the cooperative sensor data ( Fig. 1A, ¶ 24 signal can include measurements and/or sensed data representing a motion of the target object. ). Regarding Claim 19, Sakamaki in view of Crans teach the limitations of claim 13, which this claim depends on. Sakamaki further teaches, the apparatus of claim 13, wherein the instructions, when executed by the one or more processors, cause the apparatus to estimate the track for each of the one or more targets ( Fig. 1A, ¶ 65 estimate or predict the target object's states ) based on the airspace surveillance data received from a preferred sensor ( Fig. 1A, ¶ 63 radar sensor ( e.g., 102) ) of the plurality of sensors. Regarding Claim 20, Sakamaki in view of Crans teach the limitations of claim 13, which this claim depends on. Sakamaki further teaches, the apparatus of claim 13, wherein the instructions, when executed by the one or more processors, cause the apparatus to estimate the track for each of the one or more targets ( Fig. 1A, ¶ 65 estimate or predict the target object's states ) based on a Kalman filter ( Fig. 1A, ¶ 64 multiple filters 116, such as Kalman filters ). Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Sakamaki et al. ( US 2023/0215277 A1 ) (herein after Sakamaki ) in view of CRANS et al. ( US 2021/0159965 A1 ) (herein after Crans ), and further in view of Wilson, JR. et al. ( US 2006/0224318 A1 ) (herein after Wilson ). Regarding Claim 12, Sakamaki in view of Crans teach the limitations of claim 1, which this claim depends on. Sakamaki in view of Crans fail to teach, the method of claim 1, further comprising estimating the track for each of the one or more targets based on a particle filter. In analogous art, Wilson teaches, the method of claim 1, further comprising estimating the track for each of the one or more targets ( Fig. 2, ¶ 17 easy to extract track estimates ) based on a particle filter ( Fig. 2, ¶ 19 discrete inputs to the particle-filtering model ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamaki in view of Crans by combining the method performed by track fusion system 302 taught by Sakamaki in view of Crans with a method performed by a system 40 further comprising, estimating the track for each of the one or more targets based on a particle filter; taught by Wilson for the benefit of increasing accuracy of the air surveillance system to allow tight packing of aircraft thus allowing more aircraft [ Wilson: ¶ 49 the accuracy allowing very tight packing of trajectory vehicle; ¶ 8 greater accuracy, allowing more vehicles in the space. ]. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Esposito ( US 2018/0047294 A1 ) teaches, aggregating the airspace surveillance data to obtain aggregated data ( Fig. 1, ¶ 21 data aggregating system 150 configured to aggregate and de-conflict air traffic data ) . Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT JOSEPH O. NYAMOGO whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (469)295-9276 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 9:00 A to 5:00 P CT . 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, FILLIN "SPE Name?" \* MERGEFORMAT EMAN ALFAKAWI can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-4448 . 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. FILLIN "Examiner Stamp" \* MERGEFORMAT /JOSEPH O. NYAMOGO/ Examiner Art Unit 2858 /EMAN A ALKAFAWI/ Supervisory Patent Examiner, Art Unit 2858 10/17/2025