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 .
DETAILED ACTION
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 12/3/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed 1/14/2026 with respect to 35 USC 101 have been fully considered but they are not persuasive. Applicant Asserts:
First, the Office Action asserts that, prior to the present amendment, Clause [1] could be reasonably interpreted as "evaluating the data collected to determine objects within the data," i.e. a mental evaluation (See Office Action, page 6). However, Clause [1], as amended, cannot reasonably be so interpreted. Specifically, Clause [1], as amended, recites that locating aircraft and non-aircraft objects in the priority data and in self-reported data, the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time, and querying a map service to determine where to focus detection based on the map of the airport, a GPS/IMU orientation of the aircraft, and locations of aircraft and non-aircraft objects within the priority data and the self-reported data. Neither locating aircraft and non-aircraft objects for every frame of data, nor querying a map service to determine GPS/IMU orientation of the aircraft, and locations of aircraft and non-aircraft objects for every frame of data can reasonably read on a manual process performed by a human as a mental process. Accordingly, Clause [1], as amended, does not recite an abstract idea.
Examiner respectfully disagrees. In summation, the above steps recite collecting data, including a map, GPS/IMU orientation, and object data, and determining locations/predictions for each object, which the Examiner respectfully asserts is a step that could reasonably be performed in the human mind [i.e. an abstract idea]. The limitation(s) recite evaluating data and forming judgements based on said data, said judgements including the presence and location of objects in the surroundings, which may be readily performed by a human evaluating data collected. Thus, Applicant Arguments are not persuasive.
Second, the Office Action asserts that, prior to the present amendment, Clause [2] can be reasonably interpreted as "evaluating the collected data to predict future movements of the objects identified" and "a mental evaluation in the context of the claims. Accordingly, the claim recites at least one abstract idea." See Office Action, page 6. However, there is no evidence that a person could mentally perform "predicting, in real time by the computer system, tracks of the aircraft and non-aircraft objects and computing probabilities that the predicted tracks are correct," as claimed. For example, it is not apparent how a person would mentally track aircraft and non-aircraft objects in real time. Further, there is no evidence that a person could predict the tracks of the objects and compute probabilities mentally and while also locating aircraft and non- aircraft objects in real time, and querying a map service to determine the orientation of the aircraft and locations of the aircraft and non-aircraft objects in Clause [1]. Applicant respectfully asserts that in the absence of any evidence of record that the claimed locating, tracking, and predicting could be performed mentally, a conclusion that Clause [2] is directed to a mental comparison is improper.
Examiner respectfully disagrees. Similar to the above, the Examiner respectfully asserts that the tracking of objects in the environment is quintessentially a mental process of making judgements based on data collected. For example, based on data received, a user may be capable of plotting the course of objects represented by said data, and predicting future movements based on other data, such as velocity and possible paths each object could take. These, under the broadest reasonable interpretation of the claim(s), encompass forming simple mathematical judgments based on data collected, which is a mathematical or mental, and therefore abstract process. Thus, Applicant arguments are not persuasive.
Further, Clause [2] is entirely unlike any of the examples of evaluation and judgement set forth in the MPEP: - a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); - claims to "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014); - a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011); and - a claim to identifying head shape and applying hair designs, which is a process that can be practically performed in the human mind, In re Brown, 645 Fed. App'x 1014, 1016-17 (Fed. Cir. 2016) (non-precedential). MPEP § 2106.04(a)(2)(III)(A). None of these examples are in any way similar to Clause [2], "predicting, in real time using a computer system, tracks of the aircraft and non-aircraft objects and computing probabilities that the predicted tracks are correct." Accordingly, at least Clauses [1]-[2] are not abstract under Step 2A, Prong 1.
Examiner respectfully disagrees, noting that while provided examples do not appear to clearly address the claim(s) at issue, as set forth above by the Applicant, activity recognized as “well-understood, routine, and conventional” appears to map onto the claims, given their broadest reasonable interpretation. Specifically, “receiving or transmitting data over a network” and “performing repetitive calculations” have both been recognized as such activity, which the present claimed invention appears to exemplify under the broadest reasonable interpretation of the claim, being directed to receiving data, and locating/tracking object movements, as well as computing probabilitie(s) regarding if the tracks are correct, which are a repetitive calculation. Thus, Applicant arguments set forth above are not persuasive.
B. Step 2A, Prong 2: Clauses [1]-[4] are Linked to Improvements Over the Prior Art
Clauses [1]-[2] are directly linked to improvements over the prior art. This linkage satisfies Step 2A, Prong 2, thus establishing the eligibility of the claims under 35 U.S.C. § 101.
"The consideration of whether the claim as a whole includes an improvement to a computer or to a technological field requires an evaluation of the specification and the claim to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement." Example 47 (emphasis added); see also Example 48, claim 2 ("[T]he claim reflects the improvement discussed in the disclosure by reciting details of certain features.] ... The claimed invention reflects this technical improvement by including these features.").
Specifically, Clauses [1]-[2] are collectively and directly responsible for detecting and tracking a larger array of objects than were detected and tracked using any single prior art technique. Cf Specification, paragraph 3. Specifically, locating aircraft and non-aircraft objects according to Clause [1], and predicting tracks of the objects according to Clause [2], allow for the claimed invention to detect a wider array of objects, including land-based non-aircraft objects and objects that are relatively much smaller than the aircraft, than are detectable by any of the prior art disclosures of record, and therefore improve incursion prevention and aircraft/human safety. See Sharma and Gariel. Thus, Step 2A, Prong 2 is satisfied, and the claims are fully eligible under 35 U.S.C. § 101.
Examiner respectfully disagrees that the claimed invention, under the broadest reasonable interpretation of the claim, encompasses an improvement to a specific technological field, as asserted by the Applicant in arguments above. Given the broadest reasonable interpretation of the claim(s), the claim(s) are directed to evaluating data to determine the locations and tracks of objects in the environment, and the evaluation of a focus area based on said data. While the implementation of the detection focus area in some way to the technology may reflect a technological improvement, the mere determination of such a zone does not appear to specifically integrate the asserted abstract idea into a practical application. Alone, as set forth above, the claimed invention appears to encompass the mere processing of received data to make simple judgments as to object paths, and the output of said data to a receiver, which as noted above and below encompass steps that the Examiner asserts to be “well-understood, routine, and conventional” under the broadest reasonable interpretation of the claim.
Applicant therefore respectfully requests that the rejection under 35 U.S.C. § 101 be withdrawn and the claims indicated as allowable.
For at least the reasons set forth above, as well as those set forth below with respect to specific limitation(s), The rejection(s) of the claim(s) under 35 USC 101 are respectfully maintained.
Claim Objections
Claim 1 objected to because of the following informalities: The word "computng" appears to be misspelled in the line "and non-aircraft objects and computng probabilities...". Appropriate correction is required.
Claim Rejections - 35 USC § 101
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to a method of determining possible runway incursions (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories. Similarly, Claim 10 is directed to a computer system for determining possible runway incursions (i.e., a machine) and is also within at least one of the four statutory categories. As set forth above, Claim 19 is directed to a computer program product, and is not within at least one of the four statutory categories, however is also directed to an abstract idea.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 10 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 10 recites:
A computer system for determining possible incursions onto a runway that could affect an aircraft landing on the runway comprising:
a hardware processor; and a non-volatile storage medium storing instructions that when executed by the hardware processor perform operations comprising:
determining characteristics of the runway, [mental process/step] wherein the characteristics are based on data collected from the aircraft and environmental data about the runway;
determining a focus of incursion detection based on the characteristics and a position of the aircraft; [mental process/step]
selecting priority data from the collected data based on a pre-defined priority scheme; [mental process/step]
locating aircraft and non-aircraft objects in the priority data and in self-reported data; the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time, and [mental process/step]
querying a map service to determine where to focus detection based on the map of the airport, a GPS/IMU orientation of the aircraft, and locations of aircraft and non-aircraft objects within the priority data and the self-reported data;
predicting in real time using a computer system tracks of the aircraft and non-aircraft objects and computng probabilities that the predicted tracks are correct; and [mental process/step]
providing a state of the runway for landing to a receiver based on the predicted tracks and the probabilities.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determining characteristics…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement as to the state of a runway based on said data, which is a mental evaluation under its broadest reasonable interpretation. Further, “determining a focus of incursion…” in the context of the claim encompasses a person looking at data regarding the aircraft, and forming a simple judgement as to an area of evaluation based on such data, which is also a mental process under its broadest reasonable interpretation. The limitation “selecting priority data…” in the context of the claim encompasses a mental determination of data within the data collected that should be prioritized in the analysis. Further, “locating objects…” in the context of the claim encompasses evaluating the data collected to determine objects within the data, including in real-time and “predicting… tracks…” in the context of the claim encompasses evaluating the collected data to predict future movements of the objects identified, each of which is a mental evaluation in the context of the claim. Finally, “querying…” in the context of the claim encompasses using the collected data to make a judgement as to where detection should be focused, which, without use or integration into the specific technology, forms a simple judgement under the broadest reasonable interpretation of the claim and is therefore an abstract idea. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A computer system for determining possible incursions onto a runway that could affect an aircraft landing on the runway comprising: [generic linking to technical field, 2106.05(h)]
a hardware processor; and a non-volatile storage medium storing instructions that when executed by the hardware processor perform operations comprising: [applying the abstract idea using generic computing module, Apply it 2106.05(f)]
determining characteristics of the runway, wherein the characteristics are based on data collected from the aircraft and environmental data about the runway; [pre-solution activity (data gathering), 2106.05(g)]
determining a focus of incursion detection based on the characteristics and a position of the aircraft;
selecting priority data from the collected data based on a pre-defined priority scheme;
locating aircraft and non-aircraft objects in the priority data and in self-reported data; the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time, and
querying a map service to determine where to focus detection based on the map of the airport, a GPS/IMU orientation of the aircraft, and locations of aircraft and non-aircraft objects within the priority data and the self-reported data;
predicting in real time using a computer system [applying the abstract idea using generic computing module, Apply it 2106.05(f)] tracks of the aircraft and non-aircraft objects and computng probabilities that the predicted tracks are correct; and
providing a state of the runway for landing to a receiver based on the predicted tracks and the probabilities. [insignificant post-solution activity (displaying results of the mental process) 2106.05(g)]
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “a hardware processor; and a non-volatile storage medium…,” “using a computer system,” “wherein the characteristics are…,” and “providing a state of the runway…,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the “providing…” step is recited at a high level of generality (i.e. as a general means of outputting the state of the runway based on the determined data), and amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. Further, “a hardware processor; and a non-volatile storage medium…” and “using a computer system” are each recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that each limitation amounts to no more than mere instructions to apply the exception using a generic computer component. Finally, “wherein the characteristics are…” in the context of the claim encompasses mere data gathering recited generally, which is insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a vehicle controller to perform the evaluating… amounts to nothing 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. And as discussed above, the additional limitation of “providing a state of the runway…,” the examiner submits that this limitation is insignificant extra-solution activity.
Dependent claim(s) 2 – 9, 11 – 18, & 20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. Specifically:
Claim 2 recites wherein the collected data comprises images, which merely narrows the extra-solution activity of data gathering to a specific embodiment. Claim 11 recites substantially the same limitations as Claim 2, and is similarly rejected.
Claim 3 recites wherein the collected data comprises time-synchronizing the collected data, which merely narrows the extra-solution activity of data gathering to a specific embodiment, alternatively further comprising the mental process of organizing data based on time. Claim 12 recites substantially the same limitations as Claim 3, and is similarly rejected.
Claim 4 recites calibrating a data collection sensor against an IMU, which is a mental process of adjusting data based on a second data set under its broadest reasonable interpretation. Claim 13 recites substantially the same limitations as Claim 4, and is similarly rejected.
Claim 5 recites identifying runway zones from runway characteristics, which is a mental process of evaluating data under its broadest reasonable interpretation. Claim 14 recites substantially the same limitations as Claim 5, and is similarly rejected.
Claim 6 recites specific steps for processing collected data, which are a mental process of evaluating collected data under its broadest reasonable interpretation. Claim 15 recites substantially the same limitations as Claim 6, and is similarly rejected.
Claim 7 recites specific steps for determining position accuracy and uncertainty in such, which is a mental process under its broadest reasonable interpretation. Claim 16 recites substantially the same limitations as Claim 7, and is similarly rejected.
Claim 8 recites tracking objects, including propagating trajectories and smoothing predictions, which is a mental process of evaluating data to predict trajectories under its broadest reasonable interpretation. Claim 17 recites substantially the same limitations as Claim 8, and is similarly rejected.
Claim 9 recites determining a confidence for each of the plurality of tracks, which is a mental process of evaluating collected data under its broadest reasonable interpretation. Claim 18 recites substantially the same limitations as Claim 9, and is similarly rejected.
Claim 20 recites wherein the collected data comprises vision or matrix-based data, which merely narrows the extra-solution activity of data gathering to a specific embodiment.
Therefore, dependent claims 2 – 9, 11 – 18, & 20 are not patent eligible under the same rationale as provided for in the rejection of Independent Claims 1, 10, & 19.
Therefore, claim(s) 1 – 20 is/are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1, 2, 5, 7 - 11, 14, 16, & 17 - 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 8,019,529 B1) in view of Gariel (US 2021/0350716 A1) and Dmytro (US 2021/0208281 A1).
Regarding Claim 1:
Sharma discloses: A method for determining possible incursions onto a runway that could affect an aircraft landing on the runway comprising: (Sharma discloses in at least Column 3 Lines 39 – 49 a method of detecting an incursion for a first aircraft based on an airport layout, traffic, and obstacle information for an aircraft intending to land as further disclosed by Sharma in at least Column 9 Lines 5 – 11)
determining characteristics of the runway, wherein the characteristics are based on data collected from the aircraft and environmental data about the runway; (Sharma discloses in at least Column 6 Line 65 – Column 7 Line 7 wherein a runway/taxiway generator is configured to process airport chart data [i.e. environmental data about the runway] from a database, which may be stored on the aircraft [i.e. the data is collected from the aircraft] and used to compute features of the runway, including the centerline, width, end points, length, and/or direction of each runway/taxiway of the airport [i.e. characteristics of the runway])
determining a focus of incursion detection based on the characteristics and a position of the aircraft; (Sharma discloses in at least Column 11 Lines 9 – 13 wherein the own aircraft position and heading are used to associate the most likely runway upon which the own aircraft is or will be located [i.e. a focus of incursion detection based on the characteristics of the runway and position of the aircraft]. At least Column 7 Line 61 – Column 8 Line 2 of Sharma further disclose wherein the airport surface map may be presented, with traffic data and obstacle data within a pre-defined, user selectable, or automated range or region being displayed)
selecting priority data from the collected data based on a pre-defined priority scheme; (Sharma discloses in at least Column 12 Lines 17 – 32 wherein based on the determined ownship [own aircraft] taxiway/runway, a candidate list of conflicting targets is populated [i.e. priority data is selected from the collected data]. The conflict detection candidate list may be populated based on the position of the object being on an intersecting runway, the heading of the object intersecting an own taxiway/runway, or the object and own aircraft closing in on one another [i.e. the candidate list/priority data is selected based on a pre-defined priority scheme]. The candidate list of conflicting targets may be updated to remove candidates when any of the aforementioned conditions are no longer met)
Sharma however appears to be silent regarding:
locating aircraft and non-aircraft objects in the priority data and in self-reported data; the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time, and
querying a map service to determine where to focus detection based on the map of the airport, a GPS/IMU orientation of the aircraft, and locations of aircraft and non-aircraft objects within the priority data and the self-reported data;
predicting, in real time using a computer system, tracks of the aircraft and non-aircraft objects and computng probabilities that the predicted tracks are correct; and
providing a state of the runway for landing to a receiver based on the predicted tracks and the probabilities.
However Dmytro teaches wherein aircraft vehicles and other objects may be identified and used to direct a focus of an imaging system.
locating aircraft and non-aircraft objects in the priority data and in self-reported data; the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time, and (However Dmytro teaches in at least Paragraph 0037 wherein a “vehicle” as used in the context of the disclosure may include an aircraft, including both fixed wing aircraft and other types. At least Paragraph 0039 of Dmytro teaches wherein objects may be tracked and analyzed in the environment, including the classification of objects, the objects including as taught in at least Paragraph 0056 to include other vehicles, persons, stationary road objects, and the like [i.e. locating aircraft and non-aircraft objects in the data]. At least Paragraphs 0028, 0054, & 0055 of Dmytro teach wherein a plurality of frames may be acquired to track objects over time [i.e. the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time])
querying a map service to determine where to focus detection based on the map of the airport, a GPS/IMU orientation of the aircraft, and locations of aircraft and non-aircraft objects within the priority data and the self-reported data; (However Dmytro teaches in at least Paragraph 0029 wherein an imaging system may adjust one or more sensor parameters based on received information, the adjustment of sensor parameters determining an area of focus for the sensor [i.e. determining where to focus detection]. At least Paragraphs 0029 & 0032 of Dmytro further teach wherein the area of focus may be set based on a plurality of factors, including the position of the horizon relative to the vehicle [i.e. a GPS/IMU orientation of the aircraft], the position of a nearest or furthest object from the vehicle [i.e. locations of aircraft and non-aircraft objects within the priority data and the self-reported data]. At least Paragraph 0031 of Dmytro further teaches wherein the imaging system may be used to evaluate the configuration of the roadway to adjust the field of regard of the sensor, such as adjusting the field of regard in the horizontal/lateral direction depending on the slope of the road, or the manner in which the road turns [i.e. querying a map service to determine where to focus detection based on the map of the airport])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the determination of detection focus area for sensing systems of a vehicle as taught by Dmytro.
The motivation to do so is that, as acknowledged by Dmytro in at least Paragraphs 0030 – 0033, the imaging system may better focus on an area on relevant path areas to satisfy visibility criteria, or focus on objects that may be potentially relevant to the aircraft, improving the sensing of the surrounding environment.
However Gariel teaches wherein trajectories of surrounding objects to an own aircraft may be predicted, including associated probabilities that the predicted trajectory will occur, which are subsequently displayed to an aircraft operator.
predicting, in real time using a computer system, tracks of the aircraft and non-aircraft objects and computng probabilities that the predicted tracks are correct; and (However Gariel teaches in at least Paragraph 0072 wherein trajectories of the ownship [own aircraft] and other aircraft may be predicted based on the processed data [i.e. tracks of the objects are predicted], said predictions including extrapolated volumes of space the other aircraft are predicted to occupy at a future time as disclosed in at least Paragraph 0073. At least Paragraph 0074 of Gariel further teaches wherein the trajectory prediction may include associated probability values indicating the probability that a predicted trajectory value may occur [i.e. computing probabilities that the predicted tracks are correct])
providing a state of the runway for landing to a receiver based on the predicted tracks and the probabilities. (However Gariel teaches in at least Paragraph 0045 wherein both onboard and offboard sensors may be utilized to determine whether a runway is clear when approaching for a landing [i.e. a state of the runway for landing] based on identified and tracked potential obstacles whose behavior is predicted [i.e. based on predicted tracks and their probabilities]. At least Paragraphs 0065 & 0067 of Gariel further teach wherein if a potential conflict is recognized based on predicted zones of travel, generated in part based on associated probabilities as set forth above, a conflict zone may be rendered on a display, or an auditory cue may be provided indicating the possibility of conflict [i.e. a state of the runway is provided to a receiver based on the predicted tracks and the probabilities])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the prediction and presentation of object tracks, as well as the probability that said tracks are correct using both onboard and offboard data as taught by Gariel.
The motivation to do so is that, as acknowledged by Gariel in at least Paragraphs 0075, 0092, & 0096, by determining probabilities of future predicted object trajectories and outputting such predictions, the safety of the own aircraft may be improved by ensuring the own aircraft does not maneuver into a situation with grater probability of conflict with another mobile entity even in an instance where the potential movements of the object are uncertain.
Regarding Claim 2:
The method of claim 1, wherein the collected data comprise: images.
Sharma discloses in at least Column 6 Lines 50 – 64 wherein an obstacle detector configured to collect data regarding obstacles near a runway may include an optical camera system [i.e. the collected data comprises images].
Regarding Claim 5:
The method of claim 1, further comprising: identifying zones associated with the runway from the runway characteristics.
Sharma teaches in at least Column 11 Lines 1 – 8 wherein aspects of an aircraft runway may be computed, including intersections and hold lines [i.e. zones associated with the runway] based on the available data [i.e. from the runway characteristics].
Regarding Claim 7:
The method of claim 1, further comprising: determining position data, positional accuracy and uncertainty data, velocity data, and identification data of targets of interest from the self-reported data; fusing the self-reported data with the collected data; and determining an uncertainty associated with the fused data.
Sharma does not appear to specifically disclose the fusion of data as set forth above.
However Gariel teaches in at least Paragraphs 0045 & 0071 wherein a plurality of different sources of tracking data may be fused to output final tracking data for each of a plurality of aircraft [i.e. fusing the self-reported data with the collected data]. At least Paragraph 0121 of Gariel further teaches wherein an uncertainty of measurements in the relevant sensor data may be determined and presented, indicating a likelihood of the sensor measurement accuracy, such as the likelihood of the intruding object being within a specified ellipsoid location [i.e. positional accuracy and uncertainty data, an uncertainty associated with the fused data]. At least Paragraph 0079 of Gariel teaches wherein the attitude, position, and velocity of the other aircraft may be determined and used to predict the other aircraft trajectory [i.e. position data and velocity data], the relevant aircraft identifier further being reported as taught in at least Paragraph 0121 [i.e. identification data].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the determination of fused data as taught by Gariel.
The motivation to do so is that, as acknowledged by Gariel in at least Paragraph 0082, fusing data from a plurality of sensors enables the production of more accurate tracking data, improving the tracking of surrounding objects.
Regarding Claim 8:
The method of claim 1, further comprising: tracking the aircraft and non-aircraft objects; propagating trajectories of the aircraft and non-aircraft objects; computing predictions of the trajectories; and smoothing the predictions.
Sharma does not appear to specifically disclose tracking the objects, propagating trajectories of the objects, computing predictions of the trajectories, and smoothing the predictions.
However Gariel teaches in at least Paragraph 0072 wherein trajectories of the ownship and other aircraft may be predicted based on the processed data, said predictions including extrapolated volumes of space the other aircraft are predicted to occupy at a future time as disclosed in at least Paragraph 0073. At least Paragraphs 0030 & 0031 of Gariel further teach wherein this prediction may be based on the historic and current state of the other aircraft, including position, attitude, and velocity at the present time or at a sequence of previous times, as further taught in at least Paragraph 0079 [i.e. tracking the objects, propagating trajectories of the objects, and computing predictions of the trajectories]. At least Paragraph 0073 of Gariel further teaches wherein predicted trajectories may be computed as geometries, such as cones or ellipsoids, in order to define a possible space for the future movement(s) of the aircraft [i.e. the predictions are smoothed to a shape representing the predicted trajectory area with uncertainties included].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the tracking of objects to predict their trajectories as taught by Gariel.
The motivation to do so is that, as acknowledged by Gariel in at least Paragraph 0030, evaluating the historic trajectory of surrounding objects at different positions enables the future path of the object to be predicted with greater certainty, improving the prediction of object trajectories.
Regarding Claim 9:
The method of claim 8, further comprising: determining, based on the trajectories and the predictions, a probability of existence for each of the tracks; and determining a confidence decision associated with each of the tracks based at least on the probability of existence.
Sharma does not appear to specifically disclose determining, based on the trajectories and the predictions, a probability of existence for each of the tracks, and determining a confidence decision associated with each of the tracks based at least on the probability of existence.
However Gariel teaches in at least Paragraph 0072 wherein trajectories of the ownship and other aircraft may be predicted based on the processed data [i.e. tracks of the objects are predicted], said predictions including extrapolated volumes of space the other aircraft are predicted to occupy at a future time as disclosed in at least Paragraph 0073. At least Paragraph 0074 of Gariel further teaches wherein the trajectory prediction may include associated probability values indicating the probability that a predicted trajectory value may occur [i.e. a probability of existence for each of the tracks based on the trajectories and the predictions]. At least Paragraphs 0092, 0095, & 0096 of Gariel further teach determining a maneuver for the own aircraft based on the predicted conflicts and their respective probabilities, such that the own aircraft does not maneuver into a situation with a greater probability of conflict [i.e. a confidence decision associated with each of the tracks based at least on the probability of existence].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the determination of an aircraft maneuver based on predicted tracks and associated probabilities as taught by Gariel.
The motivation to do so is that, as acknowledged by Gariel in at least Paragraph 0096, the safety of the own aircraft may be improved by ensuring the own aircraft does not maneuver into a situation with grater probability of conflict with another mobile entity.
Regarding Claim 10:
Sharma discloses: A computer system for determining possible incursions onto a runway that could affect an aircraft landing on the runway comprising: (Sharma discloses in at least Column 3 Lines 29 – 49 an apparatus for detecting an incursion for a first aircraft based on an airport layout, traffic, and obstacle information for an aircraft intending to land as disclosed in at least Column 9 Lines 5 – 11, which may be implemented using a computer system by an algorithm or software routine as disclosed in at least Column 5 Lines 9 – 16)
a hardware processor; and a non-volatile storage medium storing instructions that when executed by the hardware processor perform operations comprising: (Sharma discloses in at least Column 5 Lines 9 – 16 wherein the system may be implemented on a computing platform using software [i.e. instructions to be executed], said computing platform including a general purpose processor as disclosed in at least Column 6 Lines 6 – 10, and utilizing storage media as disclosed in at least Column 6 Lines 58 – 61)
determining characteristics of the runway, wherein the characteristics are based on data collected from the aircraft and environmental data about the runway; (Sharma discloses in at least Column 6 Line 65 – Column 7 Line 7 wherein a runway/taxiway generator is configured to process airport chart data [i.e. environmental data] from a database, which may be stored on the aircraft [i.e. the data is collected from the aircraft] and used to compute features of the runway, including the centerline, width, end points, length, and/or direction of each runway/taxiway of the airport [i.e. characteristics of the runway])
determining a focus of incursion detection based on the characteristics and a position of the aircraft; (Sharma discloses in at least Column 11 Lines 9 – 13 wherein the own aircraft position and heading are used to associate the most likely runway upon which the own aircraft is located [i.e. a focus of incursion detection based on the characteristics of the runway and position of the aircraft]. At least Column 7 Line 61 – Column 8 Line 2 of Sharma further disclose wherein the airport surface map may be presented, with traffic data and obstacle data within a pre-defined, user selectable, or automated range or region being displayed)
selecting priority data from the collected data based on a pre-defined priority scheme; (Sharma discloses in at least Column 12 Lines 17 – 32 wherein based on the determined ownship taxiway/runway, a candidate list of conflicting targets is populated [i.e. priority data is selected from the collected data]. The conflict detection candidate list may be populated based on the position of the object being on an intersecting runway, the heading of the object intersecting an own taxiway/runway, or the object and own aircraft closing in on one another [i.e. the candidate list/priority data is selected based on a pre-defined priority scheme]. The candidate list of conflicting targets may be updated to remove candidates when any of the aforementioned conditions are no longer met)
Sharma does not appear to specifically disclose:
locating aircraft and non-aircraft objects in the priority data and in self-reported data; the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time, and
querying a map service to determine where to focus detection based on the map of the airport, a GPS/IMU orientation of the aircraft, and locations of aircraft and non-aircraft objects within the priority data and the self-reported data;
predicting, in real time by the computer system, tracks of the aircraft and non-aircraft objects and computing probabilities that the predicted tracks are correct; and
providing a state of the runway for landing to a receiver based on the predicted tracks and the probabilities.
However Dmytro teaches wherein aircraft vehicles and other objects may be identified and used to direct a focus of an imaging system.
locating aircraft and non-aircraft objects in the priority data and in self-reported data; the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time, and (However Dmytro teaches in at least Paragraph 0037 wherein a “vehicle” as used in the context of the disclosure may include an aircraft, including both fixed wing aircraft and other types. At least Paragraph 0039 of Dmytro teaches wherein objects may be tracked and analyzed in the environment, including the classification of objects, the objects including as taught in at least Paragraph 0056 to include other vehicles, persons, stationary road objects, and the like [i.e. locating aircraft and non-aircraft objects in the data]. At least Paragraphs 0028, 0054, & 0055 of Dmytro teach wherein a plurality of frames may be acquired to track objects over time [i.e. the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time])
querying a map service to determine where to focus detection based on the map of the airport, a GPS/IMU orientation of the aircraft, and locations of aircraft and non-aircraft objects within the priority data and the self-reported data; (However Gariel teaches in at least Paragraph 0045 wherein both onboard and offboard sensors may be utilized to determine whether a runway is clear when approaching for a landing [i.e. a state of the runway for landing] based on identified and tracked potential obstacles whose behavior is predicted [i.e. based on predicted tracks and their probabilities]. At least Paragraphs 0065 & 0067 of Gariel further teach wherein if a potential conflict is recognized based on predicted zones of travel, generated in part based on associated probabilities as set forth above, a conflict zone may be rendered on a display, or an auditory cue may be provided indicating the possibility of conflict [i.e. a state of the runway is provided to a receiver based on the predicted tracks and the probabilities])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the prediction and presentation of object tracks, as well as the probability that said tracks are correct using both onboard and offboard data as taught by Gariel.
The motivation to do so is that, as acknowledged by Gariel in at least Paragraphs 0075, 0092, & 0096, by determining probabilities of future predicted object trajectories and outputting such predictions, the safety of the own aircraft may be improved by ensuring the own aircraft does not maneuver into a situation with grater probability of conflict with another mobile entity even in an instance where the potential movements of the object are uncertain.
However Gariel teaches wherein trajectories of surrounding objects to an own aircraft may be predicted, including associated probabilities that the predicted trajectory will occur, which are subsequently displayed to an aircraft operator.
predicting, in real time by the computer system, tracks of the aircraft and non-aircraft objects and computing probabilities that the predicted tracks are correct; and (However Gariel teaches in at least Paragraph 0072 wherein trajectories of the ownship and other aircraft may be predicted based on the processed data [i.e. tracks of the objects are predicted], said predictions including extrapolated volumes of space the other aircraft are predicted to occupy at a future time as disclosed in at least Paragraph 0073. At least Paragraph 0074 of Gariel further teaches wherein the trajectory prediction may include associated probability values indicating the probability that a predicted trajectory value may occur [i.e. probabilities that the predicted tracks are correct])
providing a state of the runway for landing to a receiver based on the predicted tracks and the probabilities. (However Gariel teaches in at least Paragraph 0045 wherein both onboard and offboard sensors may be utilized to determine whether a runway is clear when approaching for a landing [i.e. a state of the runway for landing] based on identified and tracked potential obstacles whose behavior is predicted [i.e. based on predicted tracks and their probabilities]. At least Paragraphs 0065 & 0067 of Gariel further teach wherein if a potential conflict is recognized based on predicted zones of travel, a conflict zone may be rendered on a display, or an auditory cue may be provided indicating the possibility of conflict [i.e. a state of the runway is provided to a receiver])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the prediction and presentation of object tracks, as well as the probability that said tracks are correct using both onboard and offboard data as taught by Gariel.
The motivation to do so is that, as acknowledged by Gariel in at least Paragraphs 0075, 0092, & 0096, by determining probabilities of future predicted object trajectories and outputting such predictions, the safety of the own aircraft may be improved by ensuring the own aircraft does not maneuver into a situation with grater probability of conflict with another mobile entity even in an instance where the potential movements of the object are uncertain.
Regarding Claim 11:
Claim 11 recites substantially similar limitations as those found in Claim 2, above, and is rejected under similar rationale.
Regarding Claim 14:
Claim 14 recites substantially similar limitations as those found in Claim 5, above, and is rejected under similar rationale.
Regarding Claim 16:
Claim 16 recites substantially similar limitations as those found in Claim 7, above, and is rejected under similar rationale.
Regarding Claim 17:
Claim 17 recites substantially similar limitations as those found in Claim 8, above, and is rejected under similar rationale.
Regarding Claim 18:
Claim 18 recites substantially similar limitations as those found in Claim 9, above, and is rejected under similar rationale.
Regarding Claim 19:
Sharma discloses: A non-transitory computer readable medium comprising a computer program for determining possible incursions onto a runway that could affect an aircraft landing on the runway comprising, (Sharma discloses in at least Column 3 Lines 29 – 49 an apparatus for detecting an incursion for a first aircraft based on an airport layout, traffic, and obstacle information for an aircraft intending to land as disclosed in at least Column 9 Lines 5 – 11, which may be implemented using a computer system by an algorithm or software routine [i.e. via a computer program] as disclosed in at least Column 5 Lines 9 – 16)
the computer program comprising machine readable instructions that, when executed by a processor, perform operations comprising: (Sharma discloses in at least Column 5 Lines 9 – 16 wherein the system may be implemented on a computing platform using software [i.e. instructions to be executed], said computing platform including a general purpose processor as disclosed in at least Column 6 Lines 6 – 10, and utilizing storage media as disclosed in at least Column 6 Lines 58 – 61)
determining characteristics of the runway, wherein the characteristics are based on data collected from the aircraft and environmental data about the runway; (Sharma discloses in at least Column 6 Line 65 – Column 7 Line 7 wherein a runway/taxiway generator is configured to process airport chart data [i.e. environmental data] from a database, which may be stored on the aircraft [i.e. the data is collected from the aircraft] and used to compute features of the runway, including the centerline, width, end points, length, and/or direction of each runway/taxiway of the airport [i.e. characteristics of the runway])
determining a focus of incursion detection based on the characteristics and a position of the aircraft; (Sharma discloses in at least Column 11 Lines 9 – 13 wherein the own aircraft position and heading are used to associate the most likely runway upon which the own aircraft is located [i.e. a focus of incursion detection based on the characteristics of the runway and position of the aircraft]. At least Column 7 Line 61 – Column 8 Line 2 of Sharma further disclose wherein the airport surface map may be presented, with traffic data and obstacle data within a pre-defined, user selectable, or automated range or region being displayed)
selecting priority data from the collected data based on a pre-defined priority scheme; (Sharma discloses in at least Column 12 Lines 17 – 32 wherein based on the determined ownship taxiway/runway, a candidate list of conflicting targets is populated [i.e. priority data is selected from the collected data]. The conflict detection candidate list may be populated based on the position of the object being on an intersecting runway, the heading of the object intersecting an own taxiway/runway, or the object and own aircraft closing in on one another [i.e. the candidate list/priority data is selected based on a pre-defined priority scheme]. The candidate list of conflicting targets may be updated to remove candidates when any of the aforementioned conditions are no longer met)
Sharma does not appear to specifically disclose:
locating aircraft and non-aircraft objects in the priority data and in self-reported data; the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time, and
querying a map service to determine where to focus detection based on the map of the airport, a GPS/IMU orientation of the aircraft, and locations of aircraft and non-aircraft objects within the priority data and the self-reported data;
predicting in real time by the computer system, tracks of the aircraft and non-aircraft objects and computing probabilities that the predicted tracks are correct; and
providing a state of the runway for landing to a receiver based on the predicted tracks and the probabilities.
However Dmytro teaches wherein aircraft vehicles and other objects may be identified and used to direct a focus of an imaging system.
locating aircraft and non-aircraft objects in the priority data and in self-reported data; the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time, and (However Dmytro teaches in at least Paragraph 0037 wherein a “vehicle” as used in the context of the disclosure may include an aircraft, including both fixed wing aircraft and other types. At least Paragraph 0039 of Dmytro teaches wherein objects may be tracked and analyzed in the environment, including the classification of objects, the objects including as taught in at least Paragraph 0056 to include other vehicles, persons, stationary road objects, and the like [i.e. locating aircraft and non-aircraft objects in the data]. At least Paragraphs 0028, 0054, & 0055 of Dmytro teach wherein a plurality of frames may be acquired to track objects over time [i.e. the aircraft and non-aircraft objects being located for every frame of data to provide predictions in real time])
querying a map service to determine where to focus detection based on the map of the airport, a GPS/IMU orientation of the aircraft, and locations of aircraft and non-aircraft objects within the priority data and the self-reported data; (However Gariel teaches in at least Paragraph 0045 wherein both onboard and offboard sensors may be utilized to determine whether a runway is clear when approaching for a landing [i.e. a state of the runway for landing] based on identified and tracked potential obstacles whose behavior is predicted [i.e. based on predicted tracks and their probabilities]. At least Paragraphs 0065 & 0067 of Gariel further teach wherein if a potential conflict is recognized based on predicted zones of travel, generated in part based on associated probabilities as set forth above, a conflict zone may be rendered on a display, or an auditory cue may be provided indicating the possibility of conflict [i.e. a state of the runway is provided to a receiver based on the predicted tracks and the probabilities])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the prediction and presentation of object tracks, as well as the probability that said tracks are correct using both onboard and offboard data as taught by Gariel.
The motivation to do so is that, as acknowledged by Gariel in at least Paragraphs 0075, 0092, & 0096, by determining probabilities of future predicted object trajectories and outputting such predictions, the safety of the own aircraft may be improved by ensuring the own aircraft does not maneuver into a situation with grater probability of conflict with another mobile entity even in an instance where the potential movements of the object are uncertain.
However Gariel teaches wherein trajectories of surrounding objects to an own aircraft may be predicted, including associated probabilities that the predicted trajectory will occur, which are subsequently displayed to an aircraft operator.
predicting in real time by the computer system, tracks of the aircraft and non-aircraft objects and computing probabilities that the predicted tracks are correct; and (However Gariel teaches in at least Paragraph 0072 wherein trajectories of the ownship and other aircraft may be predicted based on the processed data [i.e. tracks of the objects are predicted], said predictions including extrapolated volumes of space the other aircraft are predicted to occupy at a future time as disclosed in at least Paragraph 0073. At least Paragraph 0074 of Gariel further teaches wherein the trajectory prediction may include associated probability values indicating the probability that a predicted trajectory value may occur [i.e. probabilities that the predicted tracks are correct])
providing a state of the runway for landing to a receiver based on the predicted tracks and the probabilities. (However Gariel teaches in at least Paragraph 0045 wherein both onboard and offboard sensors may be utilized to determine whether a runway is clear when approaching for a landing [i.e. a state of the runway for landing] based on identified and tracked potential obstacles whose behavior is predicted [i.e. based on predicted tracks and their probabilities]. At least Paragraphs 0065 & 0067 of Gariel further teach wherein if a potential conflict is recognized based on predicted zones of travel, a conflict zone may be rendered on a display, or an auditory cue may be provided indicating the possibility of conflict [i.e. a state of the runway is provided to a receiver])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the prediction and presentation of object tracks, as well as the probability that said tracks are correct using both onboard and offboard data as taught by Gariel.
The motivation to do so is that, as acknowledged by Gariel in at least Paragraphs 0075, 0092, & 0096, by determining probabilities of future predicted object trajectories and outputting such predictions, the safety of the own aircraft may be improved by ensuring the own aircraft does not maneuver into a situation with grater probability of conflict with another mobile entity even in an instance where the potential movements of the object are uncertain.
Regarding Claim 20:
The computer program product of claim 19, wherein the collected data comprise: vision-based or matrix-based data.
Sharma discloses in at least Column 6 Lines 50 – 64 wherein an obstacle detector configured to collect data regarding obstacles near a runway may include an optical camera system [i.e. the collected data comprises images].
Claim(s) 3 & 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 8,019,529 B1) in view of Gariel (US 2021/0350716 A1) and Dmytro (US 2021/0208281 A1) as applied to claims 1 & 10 above, and further in view of Hager (US 2003/0210181 A1).
Regarding Claim 3:
The method of claim 1, further comprising: time-synchronizing the collected data.
Sharma does not appear to specifically disclose time-synchronizing the collected data.
However Hager teaches in at least Paragraphs 0036 & 0037 wherein radar, IMU, and GPS data are time-synchronized with one another [i.e. the collected data is time-synchronized].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the time-synchronization of data as taught by Hager.
The motivation to do so is that, as acknowledged by Hager in at least Paragraphs 0037 & 0045, data points collected may be corresponded to one another, such that, for example, each GPS data point has a corresponding radar point, improving the logged data of the aircraft system with which control determinations may be made.
Regarding Claim 12:
Claim 12 recites substantially similar limitations as those found in Claim 3, above, and is rejected under similar rationale.
Claim(s) 4 & 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 8,019,529 B1) in view of Gariel (US 2021/0350716 A1) and Dmytro (US 2021/0208281 A1) as applied to claims 1 & 10 above, and further in view of Thomas (US 2009/0262008 A1).
Regarding Claim 4:
The method of claim 1, further comprising: calibrating a sensor that produces the collected data against an inertial measurement unit (IMU) onboard the aircraft.
Sharma does not appear to specifically disclose calibrating a sensor producing collected data based on an aircraft IMU.
However Thomas teaches in at least Paragraphs 0039 & 0040 wherein a radar altimeter of an aircraft [i.e. a sensor producing the corrected data] may receive inertial data from an inertial measurement unit (IMU), with which compensation is made to the readings of the radar altimeter [i.e. calibration is performed]. Said compensation is further taught in at least Paragraph 0029 to include error correction to the readings of the radar sensor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the calibration of radar sensor(s) using data from an aircraft IMU as taught by Thomas.
The motivation to do so is that, as acknowledged by Thomas in at least Paragraphs 0039 & 0040, error in the signal reading from the radar sensors may be reduced, improving the data acquired by the sensor(s).
Regarding Claim 13:
Claim 13 recites substantially similar limitations as those found in Claim 4, above, and is rejected under similar rationale.
Claim(s) 6 & 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 8,019,529 B1) in view of Gariel (US 2021/0350716 A1) and Dmytro (US 2021/0208281 A1) as applied to claims 5 & 14 above, and further in view of Su (US 2021/0201049 A1).
Regarding Claim 6:
The method of claim 5, further comprising: identifying regions of interest in the collected data that correspond to the runway and the runway zones; cropping the collected data in the regions of interest to create slices; and detecting, by an object detector, the aircraft and non-aircraft objects in the slices.
Sharma discloses in at least Column 13 Lines 18 – 24 wherein hold lines [i.e. regions of interest in the collected data that correspond to the runway and the runway zones] are monitored to determine if a traffic object has crossed a hold line. Sharma however appears to be silent regarding cropping the collected data in the regions of interest to create slices and detecting, by an object detector, the objects in the slices as recited by the present claimed invention.
However Su teaches in at least Paragraphs 0005 & 0024 wherein a cropped image portion is generated from a larger image data, with only the cropped image data being processed to determine if any pedestrians [i.e. obstacles] are present [i.e. cropping the collected data in the regions of interest to create slices and detecting the objects in the slices]. At least Paragraph 0024 of Su further teaches wherein the region of interest cropped is based on an area most likely to include the type of object being searched for, in this case the pedestrians [i.e. regions of interest in the collected data]. At least Paragraph 0036 of Su further teaches wherein a plurality of cropped images may be generated from a singular image [i.e. a plurality of slices cropped in the region of interest].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Sharma by incorporating the cropping of sensor data to perform object detection as taught by Su.
The motivation to do so is that, as acknowledged by Su in at least Paragraph 0041, detection performance may be increased [i.e. detection performance is improved] while allowing for lower resolution imagery and decreased hardware cost when implementing the object detection system.
Regarding Claim 15:
Claim 15 recites substantially similar limitations as those found in Claim 6, above, and is rejected under similar rationale.
Conclusion
The following prior art made of record but not relied upon is considered pertinent to the Applicant’s disclosure:
Nichols (US 2010/0211237 A1): Nichols recites a system for presenting a view of an environment for an aircraft pilot, including receiving an input of a target, which may include a runway. Based on the received target, the system may be configured to generate a display view of the environment, including potential traffic and terrain in the vicinity.
Ceccom (US 2018/0181125 A1): Ceccom recites an on-ground collision avoidance system for a vehicle, including a plurality of unmanned aerial vehicles configured to detect and supply data regarding a planned path of travel for an aircraft to a destination. The UAVs may be configured to position themselves near a projected landing site, and track potential ground hazards in the vicinity.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER RYAN CARDIMINO whose telephone number is (571)272-2759. The examiner can normally be reached M-Th 8:30-5:00.
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/CHRISTOPHER R CARDIMINO/Examiner, Art Unit 3661
/RAMYA P BURGESS/Supervisory Patent Examiner, Art Unit 3661
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