Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the amendments filed on 10/15/2025, in which claims 1, 10, and 19 are amended. Claims 1-19 are rejected.
Response to Arguments
With respect to claim 1, the Applicant argues:
While Applicant disagrees that previous Claim 1 rendered obvious by Howell, alone or with Jiang, Applicant submits that Claim 1, as amended herein, overcomes the rejection under 35 U.S.C. 103 for at least the reason that Howell, alone or with Jiang, fails to disclose each and every element of Claim 1. For example, the cited references at least fail to disclose "generat[ing] a weighted landing distance based at least in part on the predicted landing distance for the approaching vehicle, the second predicted landing distance, and the compensation factor, wherein the compensation factor is configured to control a level of contribution of the predicted landing distance to computation of the weighted landing distance" (emphasis added).
On page 7 the office action alleges that para. [0092] of Howell discloses "generate a weighted landing distance based at least in part on the predicted landing distance for the approaching vehicle, the second predicted landing distance, and the compensation factor." Applicant respectfully disagrees. The cited portion of Howell fails to disclose a "compensation factor," let alone "generat[ing] a weighted landing distance based at least in part on the predicted landing distance for the approaching vehicle, the second predicted landing distance, and the compensation factor, wherein the compensation factor is configured to control a level of contribution of the predicted landing distance to computation of the weighted landing distance." For example, para. [0092] of Howell describes "the processor 110 is configured to determine 341 the performance factor as a ratio, or difference, between the average landing distance 630 of the landing aircraft 10 and the average landing distance 620 of the median aircraft." However, the "performance factor" is fundamentally different from the claimed "compensation factor." That is, instead of being based on "the average landing distance 630 of the landing aircraft 10 and the average landing distance 620 of the median aircraft," the compensation factor is "based at least in part on the historical true landing distance for the at least one vehicle and the historical predicted landing distance for the at least one vehicle." This distinction between a performance factor that is derived from average actual landing distances and a compensation factor that is derived from historical prediction accuracy reflects a different technical approach and function, which Howell does not teach or suggest.
Howell discloses analyzing landing event data to determine landing distance and landing controls for the current aircraft. The landing event data includes environmental conditions, retardation demands, and a performance indicator. (¶ [0014]) The performance indicator can include a landing distance factor, (¶ [0007]) a relationship between performance indicators and environmental conditions and retardation demands, (¶ [0018]) and a performance factor. (¶ [0022]) The performance factor may include a statistical distribution of recorded landing distances and a difference/ratio between a typical landing distance of the current aircraft and the average landing distance of other aircraft. (¶ [0090], [0024]) Here, the statistical distribution of recorded landing distances is a “true landing distance” and the difference/ratio is a “predicted landing distance” for the aircraft.
Because the landing event data is stored for every landing event over time (¶ [0016]), then each performance indicator will include a performance factor, i.e., historical data of the statistical distribution of landing distances (a true landing distance) and historical data of the ratio/difference of the previously approaching aircraft and the average landing distance of other aircraft ( a predicted landing distance). Thus, when a new aircraft approaches its performance indicator, e.g., compensation factor, is based at least in part on historical true landing distance for the at least one vehicle and the historical predicted landing distance for the at least one vehicle.
Therefore, the Examiner finds this argument unpersuasive.
Applicant further argues:
Further, nothing in the cited portion of Howell, or elsewhere, discloses "generating a weighted landing distance," much less the above-emphasized portion of Claim 1 where a "compensation factor is configured to control a level of contribution of the predicted landing distance to computation of the weighted landing distance." For example, at para. [0094] Howell describes "the average landing distance 630 of the landing aircraft 10 may be a weighted average landing distance 630, weighted towards more recent landing events." However, this reference to a "weighted average" pertains solely to historical landing data and does not involve combining multiple predicted landing distances with a compensation factor configured to "control a level of contribution of the predicted landing distance to computation of the weighted landing distance."
Jiang cannot compensate for the deficiencies of Howell. For example, Jiang is silent as to "generat[ing] a weighted landing distance based at least in part on the predicted landing distance for the approaching vehicle, the second predicted landing distance, and the compensation factor, wherein the compensation factor is configured to control a level of contribution of the predicted landing distance to computation of the weighted landing distance." Further, the office action implicitly recognizes that Jiang does not remedy these deficiencies, as the rejection only relies on Jiang as allegedly teaching "generating, at a ground station, at least one runway condition associated with a runway based at least in part on sensor data from at least one sensor.
The performance indicator is further used as a “multiplication factor” to determine a landing distance. A multiplication factor is a compensation factor to control a level of contribution of the performance indicator data, e.g., predicted landing distance, to the final computed landing distance.
Therefore, the Examiner finds this argument unpersuasive.
With respect to the rejection of claims 2-20 the Applicant argues:
For at least the above reasons, Applicant asserts that Claim 1 overcomes the rejection. Also, Applicant submits that Claims 10 and 20 overcome the rejection at least insofar that Claims 10 and 20 recite subject matter similar to Claim 1. Also, Applicant submits that Claims 2-9 and 11- 19 overcome the rejection for at least the reason that the claims depend from Claim 1 and Claim 10, respectively. Accordingly, Applicant respectfully requests that the Examiner reconsider and withdraw the rejection.
For the reasons provided above, the Examiner finds these arguments unpersuasive.
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) 1-12, 14, 15, 17, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Howell et al. (US 2022/0348317 A1, “Howell”) in view of Jiang (US 2017/0363774 A1, “Jiang”).
Regarding claims 1, 10, and 20, Howell discloses aircraft landing event system and method and teaches:
An apparatus comprising at least one processor and at least one non-transitory memory (FIG. 3 shows a schematic diagram of the aircraft landing event system 100. The aircraft landing event system 100 comprises a processor 110 communicatively coupled, or couplable, with memory 130, which is illustrated in FIG. 3 as being comprised in the aircraft landing event system 100 – See at least ¶ [0062]) having computer-coded instructions stored thereon that, in execution with at least one processor, cause the apparatus to: (FIG. 5 shows a schematic diagram of a non-transitory computer-readable storage medium 500 according to an example. The non-transitory computer-readable storage medium 500 stores instructions 530 that, if executed by a processor 520 of a controller 510 or system 510, cause the processor 520 to perform a method according to an example – See at least ¶ [0105])
generate, at a ground station, (The runway system 120 in this example is comprised in an air traffic control tower 30 of runway 20 – See at least ¶ [0061] and Fig. 2) at least one runway condition associated with a runway [] (The runway system 120 is configured to determine an environmental condition of the runway 20. As will be described in more detail hereinafter, the environmental condition is representative of a condition of a surface of the runway 20, and/or an atmosphere surrounding the runway 20. The runway system 120 is configured to communicate environment information representative of the environmental condition, such as in the form of an environment signal – See at least ¶ [0063])
retrieve, at the ground station, (It will be understood that while the above examples are described in relation to an aircraft landing event system 100 of an aircraft 10, in some examples, the aircraft landing event system 100 is comprised in an air traffic control tower 30 of a runway 20, or in any other suitable location – See at least ¶ [0107]) one of a plurality of stored runway models based at least in part on the at least one runway condition; (As will be described herein, to determine the performance indicator, the aircraft landing event data selected from the database is used to determine a relationship between the landing distance factors of previous landing events and the associated environment conditions and retardation demands. That is, the aircraft landing event system uses landing event data of many similar landing events of the current aircraft and plural other aircraft to establish a statistical model—specifically a regression model representing a variation in the landing distance factor as a function of each of the environmental conditions and retardation demands – See at least ¶ [0057])
generate, at the ground station and using the retrieved runway model, a predicted landing distance for an approaching vehicle upon the runway based at least in part on model input (The landing distance factor for the upcoming landing event of the approaching aircraft is then determined from the statistical model, by inputting the specific environmental conditions and anticipated retardation demands of the upcoming landing event – See at least ¶ [0057]) comprising real-time vehicle data for the approaching vehicle; and (In some examples, the one or more sensors and/or systems comprises any one or more of: a Global Positioning System (GPS); (real-time vehicle data) an accelerometer; an airspeed sensor; a fuel level sensor; and an altitude sensor. In some examples, the status of the aircraft 10, as detected by the sensors, is used to determine one or more of the environmental conditions and/or retardation demands of the upcoming landing event – See at least ¶ [0079])
provide, from the ground station to the approaching vehicle, (Examiner notes that the landing event system 100, i.e., memory 130 and processor 110, in the example is provided in the aircraft, but it can alternatively be provided in the traffic control tower - See at least ¶ [0107]) a historical true landing distance for at least one vehicle, a historical predicted landing distance for the at least one vehicle, (The memory 130 stores aircraft landing event data. The aircraft landing event data comprises data associated with multiple previous aircraft landing events of the landing aircraft 10 and other aircraft. Specifically, the aircraft landing event data comprises landing event data associated with many hundreds, thousands, tens of thousands, or hundreds of thousands of previous landing events of a plurality of aircraft. More specifically, for each landing event, the aircraft landing event data comprises environmental conditions, retardation demands, and performance indicators, such as landing distances or landing distance factors, associated with the respective landing events. In some examples, the memory 130 stores aircraft landing event data associated with landing events performed on a plurality of runways, including the runway 20 being approached by the landing aircraft 10. In other examples, the landing event data stored in the memory 130 is associated with landing events only on the particular runway 20 being approached – See at least ¶ [0065]) and the predicted landing distance for the approaching vehicle to cause the approaching vehicle to: (In some examples, the processor 110, or the aircraft landing system 200, is configured to determine a landing distance for the upcoming landing on the basis of the performance indicator– See at least ¶ [0069])
generate a compensation factor based at least in part on the historical true landing distance for the at least one vehicle and the historical predicted landing distance for the at least one vehicle; (That is, the aircraft landing event system uses landing event data of many similar landing events of the current aircraft and plural other aircraft to establish a statistical model—specifically a regression model representing a variation in the landing distance factor as a function of each of the environmental conditions and retardation demands. The landing distance factor for the upcoming landing event of the approaching aircraft is then determined from the statistical model, by inputting the specific environmental conditions and anticipated retardation demands of the upcoming landing event – See at least ¶ [0058])
generate a second predicted landing distance for the approaching vehicle based at least in part on the real-time vehicle data for the approaching vehicle; and (To determine 341 the performance factor, the processor 110 is configured to determine an average landing distance 620, such as a mean or a median landing distance 620, on the basis of the distribution 600. FIG.6 shows a dashed line numbered 620 indicating such a median landing distance 620, which represents the typical performance of a “median aircraft” for the environmental conditions and retardation demands of the upcoming landing event of the landing aircraft 10. The processor 110 is then configured to determine a landing distance 630, or average landing distance 630, such as a mean or median landing distance 630, of the landing aircraft 10. Specifically, the average landing distance 630 of the landing aircraft 10 is determined based on aircraft landing event data of previous landing events of the landing aircraft 10 under similar environmental conditions and retardation demands to those of the upcoming landing event - See at least ¶ [0091]; Examiner notes that to determine if an aircraft is a “landing aircraft” there needs to be a determination of position. In the disclosed invention this location may be provided via GPS and other real-time position sensors – See at least ¶ [0079])
generate a weighted landing distance based at least in part on the predicted landing distance for the approaching vehicle, the second predicted landing distance, and the compensation factor. (The processor 110 is then configured to compare 341 the average landing distance 630 of the landing aircraft 10 with the average landing distance 620 of the “median aircraft”. In the illustrated example, the comparing 341 is performed by determining 341 the performance factor on the basis of the average landing distances of both the landing aircraft 10 and the median aircraft. Specifically, the processor 110 is configured to determine 341 the performance factor as a ratio, or difference, between the average landing distance 630 of the landing aircraft 10 and the average landing distance 620 of the median aircraft. In other examples, the performance factor is represented as a standard deviation of the average landing distance 630 of the landing aircraft 10 from the average landing distance 20 of the median aircraft. The performance factor is then stored in the memory 130, along with the associated environmental conditions and retardation demands – See at least ¶ [0092])
Howell discloses identifying environmental conditions of the runway at the grounds station. Howell does not explicitly disclose that the ground station uses local sensors to determine the environmental conditions. However, Jiang discloses integrated weather projection systems, methods, and apparatuses and teaches:
generate, at a ground station, at least one runway condition associated with a runway based at least in part on sensor data from at least one sensor; (In this example, another weather projection sub module of such local weather projection module may comprise a local severe weather alert sub-module configured to generate weather nowcasts covering airport runway and terminal areas for up to 60 minutes prior to the operational event or set of events… Such a local severe weather alert sub-module may integrate multiple data sources from the dense and specialized sensor networks around runways and in the vicinity of airports – See at least ¶ [0037])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the aircraft landing event system and method of Howell to provide for the integrated weather projection systems, methods, and apparatuses, as taught in Jiang, to apply nowcasting algorithms, and generate very rapidly updated alerts for the area on and around airport runways. (At Jiang ¶ [0037])
The combination of Howell and Jiang does not explicitly teach wherein the compensation factor is configured to control a level of contribution of the predicted landing distance to computation of the weighted landing distance.
wherein the compensation factor is configured to control a level of contribution of the predicted landing distance to computation of the weighted landing distance.
Regarding claim 2, Howell further teaches:
obtaining, at the ground station, a true landing distance of the approaching vehicle upon the runway; and (That is, the aircraft landing event system uses landing event data of many similar landing events of the current aircraft and plural other aircraft to establish a statistical model—specifically a regression model representing a variation in the landing distance factor as a function of each of the environmental conditions and retardation demands. The landing distance factor for the upcoming landing event of the approaching aircraft is then determined from the statistical model, by inputting the specific environmental conditions and anticipated retardation demands of the upcoming landing event – See at least ¶ [0058])
storing, at the ground station, the true landing distance of the approaching vehicle in association with the predicted landing distance for the approaching vehicle. (In some examples, the performance indicator for the upcoming landing event and/or for each landing event of the aircraft landing event data stored in the memory 130, is, or is representative of, any one of: a landing distance factor for the respective landing event; a landing distance of the respective landing event; and a time to slow the respective aircraft from a landing speed to a predetermined threshold speed. It will be understood that the performance indicator can be defined in any other suitable way – See at least ¶ [0080])
Regarding claim 3, Howell further teaches:
the ground station receives the true landing distance of the approaching vehicle from the approaching vehicle. (Examiner notes that the landing event system 100, i.e., memory 130 and processor 110, in the example is provided in the aircraft, but it can alternatively be provided in the traffic control tower - See at least ¶ [0107]; if the system is implemented in the aircraft, then the true landing distance would be received from the approaching vehicle instead of being determined at the ground station.)
Regarding claim 4, Howell further teaches:
receiving, at the ground station, additional real-time vehicle data for the approaching vehicle during landing, wherein the ground station generates the true landing of the approaching vehicle based at least in part on the additional real-time vehicle data. (In some examples, the one or more sensors and/or systems comprises any one or more of: a Global Positioning System (GPS); (real-time vehicle data) an accelerometer; an airspeed sensor; a fuel level sensor; and an altitude sensor. In some examples, the status of the aircraft 10, as detected by the sensors, is used to determine one or more of the environmental conditions and/or retardation demands of the upcoming landing event – See at least ¶ [0079]; Examiner notes that the ground based system can be used to determine the true landing based on the retardation demands of the upcoming landing event. If the retardation demands are determined based on real-time vehicle data, then the ground station necessarily receives real-time data for an approaching vehicle and then uses that data to determine the true landing.)
Regarding claim 5, Howell further teaches:
receiving, at the ground station, the real-time vehicle data for the approaching vehicle from at least one flight monitoring system of the approaching vehicle. (In some examples, the one or more sensors and/or systems comprises any one or more of: a Global Positioning System (GPS); (real-time vehicle data from a flight monitoring system) an accelerometer; an airspeed sensor; a fuel level sensor; and an altitude sensor. In some examples, the status of the aircraft 10, as detected by the sensors, is used to determine one or more of the environmental conditions and/or retardation demands of the upcoming landing event – See at least ¶ [0079]; Examiner notes that the ground based system can be used to determine the true landing based on the retardation demands of the upcoming landing event. If the retardation demands are determined based on real-time vehicle data, then the ground station necessarily receives real-time data for an approaching vehicle.)
Regarding claim 6, Howell further teaches:
obtaining, at the ground station, the historical true landing distance for the at least one vehicle; and (That is, the aircraft landing event system uses landing event data of many similar landing events of the current aircraft and plural other aircraft to establish a statistical model—specifically a regression model representing a variation in the landing distance factor as a function of each of the environmental conditions and retardation demands. The landing distance factor for the upcoming landing event of the approaching aircraft is then determined from the statistical model, by inputting the specific environmental conditions and anticipated retardation demands of the upcoming landing event – See at least ¶ [0058])
storing, at the ground station, the historical true landing distance in association with the historical predicted landing distance for the at least one vehicle upon the runway. (The memory 130 stores aircraft landing event data. The aircraft landing event data comprises data associated with multiple previous aircraft landing events of the landing aircraft 10 and other aircraft. Specifically, the aircraft landing event data comprises landing event data associated with many hundreds, thousands, tens of thousands, or hundreds of thousands of previous landing events of a plurality of aircraft. More specifically, for each landing event, the aircraft landing event data comprises environmental conditions, retardation demands, and performance indicators, such as landing distances or landing distance factors, associated with the respective landing events. In some examples, the memory 130 stores aircraft landing event data associated with landing events performed on a plurality of runways, including the runway 20 being approached by the landing aircraft 10. In other examples, the landing event data stored in the memory 130 is associated with landing events only on the particular runway 20 being approached – See at least ¶ [0065])
Regarding claim 7, Howell further teaches:
determining at least one classification of the approaching vehicle matches at least one classification of the at least one vehicle; and
in response to the determination, providing the historical true landing distance and the historical predicted distance for the at least one vehicle to the approaching vehicle. (Specifically, the aircraft landing event system is configured to consult a database of aircraft landing event data associated with previous landing events of the aircraft approaching the runway and plural other aircraft. The air craft landing event data includes, for each landing event, environmental conditions of the respective runway—such as a friction of the runway surface and/or a wind speed associated with the landing event a retardation demand representative of a demand to slow the aircraft on the runway during the landing event, and a performance indicator, such as a landing distance factor, of the aircraft on the runway. In some examples, the retardation demand is determined on the basis of the configuration or type of the aircraft, such as a weight of the aircraft, and whether the aircraft has operable reverse thrusters, i.e., classifications – See at least ¶ [0055])
Regarding claim 8, Howell further teaches:
a respective true landing distance corresponds to a length of a runway used by a vehicle to land upon the runway; and (Optionally, the retardation demand is determined on the basis of any one or more of: a weight of the aircraft approaching the runway; a brake capacity of the aircraft approaching the runway; an available length of the runway 20 on which the aircraft can perform a landing event; and a speed of the aircraft 10, such as a flight speed and/or an anticipated speed of the aircraft 10, such as a flight speed and/or an anticipated speed of the aircraft during touchdown of the aircraft on the runway – See at least ¶ [0036])
the historical true landing distance is obtained by the ground station from the at least one vehicle. (The memory 130 stores aircraft landing event data. The aircraft landing event data comprises data associated with multiple previous aircraft landing events of the landing aircraft 10 and other aircraft. Specifically, the aircraft landing event data comprises landing event data associated with many hundreds, thousands, tens of thousands, or hundreds of thousands of previous landing events of a plurality of aircraft. More specifically, for each landing event, the aircraft landing event data comprises environmental conditions, retardation demands, and performance indicators, such as landing distances or landing distance factors, associated with the respective landing events. In some examples, the memory 130 stores aircraft landing event data associated with landing events performed on a plurality of runways, including the runway 20 being approached by the landing aircraft 10. In other examples, the landing event data stored in the memory 130 is associated with landing events only on the particular runway 20 being approached – See at least ¶ [0065])
Regarding claim 9, Howell further teaches:
causing an autopilot system of the approaching vehicle to control the approaching vehicle based at least in part on at least one of the compensation factor or the weighted landing distance. (Alternatively, if the anticipated landing distance is larger than expected , given the environmental conditions of the runway, the achievable retardation demand from the aircraft 10, and the typical performance of the aircraft 10 compared to other aircraft, the flight crew may decide to land the aircraft 10 on a different runway to the runway 20, such as an adjacent runway, or even a runway at another airport. In some examples, the aircraft 10 comprises an autopilot system (not shown), which can make such decisions on behalf of flight crew. In such examples, the processor 110 and/or the aircraft landing system 200 may be configured to communicate the performance indicator and/or the determined landing distance to the autopilot system – See at least ¶ [0070])
Regarding claim 11, Howell further teaches:
the at least one runway condition comprises at least one precipitation condition associated with the runway. (The environment condition may comprise a surface condition of the runway, such as any one or more of: a surface friction; a surface coating (e.g. a level of “wetness”, or a category such as wet/dry/ icy/oil coated); and a runway material – See at least ¶ [0033])
Regarding claim 12, Howell further teaches:
the at least one precipitation condition comprises at least one of snow, ice, or frost. (The environment condition may comprise a surface condition of the runway, such as any one or more of: a surface friction; a surface coating (e.g. a level of “wetness”, or a category such as wet/dry/ icy/oil coated); and a runway material – See at least ¶ [0033])
Regarding claim 14, Howell further teaches:
the at least one runway condition comprises at least one wind condition associated with the runway. (The aircraft landing event data includes, for each landing event, environmental conditions of the respective runway — such as a friction of the runway surface and/or a wind speed associated with the landing event – See at least ¶ [0055])
Regarding claim 15, Howell does not explicitly teach, but Jiang further teaches:
the at least one runway condition comprises runway visual range. (For example, the system in the present disclosure may be used for ATM/C, wherein such weather data inputs may include, without limitation, all or a subset of the following:…ground observations, including routine surface automatic weather observation stations, and other ground surface in situ observations of weather variables including, but not limited to, cloud ceiling and visibility; runway visual range information; anemometer data – See at least ¶ [0027])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the aircraft landing event system and method of Howell to provide for the integrated weather projection systems, methods, and apparatuses, as taught in Jiang, to apply nowcasting algorithms, and generate very rapidly updated alerts for the area on and around airport runways. (At Jiang ¶ [0037])
Regarding claim 17, Howell further teaches:
receive at least one of the compensation factor, the weighted landing distance, or a true landing distance from the approaching vehicle; and (To determine 341 the performance factor, the processor 110 is configured to determine an average landing distance 620, such as a mean or a median landing distance 620, on the basis of the distribution 600. FIG . 6 shows a dashed line numbered 620 indicating such a median landing distance 620, which represents the typical performance of a “median aircraft” for the environmental conditions and retardation demands of the upcoming landing event of the landing aircraft 10. The processor 110 is then configured to determine a landing distance 630, or average landing distance 630, such as a mean or median landing distance 630, of the landing aircraft 10. Specifically, the average landing distance 630 of the landing aircraft 10 is determined based on aircraft landing event data of previous landing events of the landing aircraft 10 under similar environmental conditions and retardation demands to those of the upcoming landing event – See at least ¶ [0092])
store at least one of the compensation factor, the weighted landing distance, or the true landing distance in association with at least one of the historical predicted landing distance for the at least one vehicle or the predicted landing distance for the at least one vehicle. (In other examples , the performance factor is represented as a standard deviation of the average landing distance 630 of the landing aircraft 10 from the average landing distance 20 of the median aircraft. The performance factor is then stored in the memory 130, along with the associated environmental conditions and retardation demands – See at least ¶ [0093])
Regarding claim 18, Howell further teaches:
update a respective runway model based at least in part on at least one of the compensation factor, the weighted landing distance, or the true landing distance. (It will be understood that the bias of the landing aircraft 10, and/or the determined 342 relationship, or regression model, may be determined more accurately by providing a larger dataset from which to select 330 the aircraft landing event data of the landing aircraft 10 and other aircraft. As such, the aircraft landing event data stored in the memory 130 should be built up over time to provide aircraft landing event data for landing events of the landing aircraft 10 and other aircraft on many different runways, and under many different environmental conditions and retardation demands – See at least ¶ [0104])
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over Howell et in view of Jiang, as applied to claim 10, and in further view of Jones (US 2014/0012437 A1, “Jones”).
Regarding claim 13, the combination of Howell and Jiang does not explicitly teach the at least one precipitation condition comprises snow compaction. However, Jones discloses device and process for determining a runway state, aircraft including such a device and piloting assistance system using said runway state and teaches:
the at least one precipitation condition comprises snow compaction. (The result of the on-board processing for estimation of the state of the runway 12 involved performed by this calculation module 42 is provided in the form of one or more of the following pieces of information: A runway adhesion level. This information can be a level determined from among the levels above (“good'/'good-medium/medium/medium poor/poor"/"nil, or a numeric code between 0 and 5); A concise characterization of the state of the runway encountered (if recognized according to the algorithm implemented in the device 14), in particular of the contaminant detected, for example DRY (for a dry runway), WET (for a wet run way), WATER '4" (for 6.3 mm of water), WATER/2" (for 12.7 mm of water), SLUSH 4" (for 6.3 mm of melted snow), SLUSH/2" (for 12.7 mm of melted snow), COMPACTED SNOW (for compacted Snow) and ICY (for ice), where said runway state designation (usually type and thickness) of the contaminant) is derived from known nomenclature – See at least ¶ [0076])
In summary, both Howell and Jiang disclose identifying the accumulation of snow on the runway and using that condition as a consideration for performing the functions of their systems. The combination of Howell and Jiang does not explicitly teach identifying the snow compaction of the runway. However, Jones discloses device and process for determining a runway state, aircraft including such a device and piloting assistance system using said runway state and teaches identifying the surface conditions of the runway which includes snow compaction.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the aircraft landing event system and method of Howell and Jiang to provide for the device and process for determining a runway state, aircraft including such a device and piloting assistance system using said runway state, as taught in Jones, to increases the precision and reliability of the estimation of a runway's state. (At Jones ¶ [0026])
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Howell et in view of Jiang, as applied to claim 10, and in further view of Phillips et al. (US 9,008,873 B1, “Phillips”).
Regarding claim 16, the combination of Howell and Jiang does not explicitly teach the at least one runway condition comprises runway elevation. However, Phillips discloses methods and systems for landing decision point and teaches:
the at least one runway condition comprises runway elevation. (Such received LPI data may include real time airplane flight operating parameters and data such as but not limited to instantaneous groundspeed; acceleration; instantaneous and trending indicated airspeed; GPS position; inertial position; groundtrack; runway length; runway elevation; runway slope; runway conditions; weather conditions Such as wind speed and direction; landing weight; the con figuration of the wing high-lift devices; and any health monitoring parameters related to engines, brakes, or other aforementioned systems – See at least Col. 7, ln. 48-58)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the aircraft landing event system and method of Howell and Jiang to provide for the methods and systems for landing decision point, as taught in Phillip, to provide a landing decision aid that will provide pilots feedback as too whether to continue the landing or execute a go-around. (At Phillip Col. 2, Ln. 8-10)
Claim(s) 19 is rejected under 35 U.S.C. 103 as being unpatentable over Howell et in view of Jiang, as applied to claim 10, and in further view of Moll. (US 2019/0251852 A1, “Moll”).
Regarding claim 19, Howell further discloses:
receive the weighted landing distance from the approaching vehicle; and (The processor 110 is then configured to compare 341 the average landing distance 630 of the landing aircraft 10 with the average landing distance 620 of the “median aircraft”. In the illustrated example, the comparing 341 is performed by determining 341 the performance factor on the basis of the average landing distances of both the landing aircraft 10 and the median aircraft. Specifically, the processor 110 is configured to determine 341 the performance factor as a ratio, or difference, between the average landing distance 630 of the landing aircraft 10 and the average landing distance 620 of the median aircraft. In other examples, the performance factor is represented as a standard deviation of the average landing distance 630 of the landing aircraft 10 from the average landing distance 20 of the median aircraft. The performance factor is then stored in the memory 130, along with the associated environmental conditions and retardation demands – See at least ¶ [0092])
The combination of Howell and Jiang does not explicitly teach instruct the approaching vehicle to land on a second runway. However, Moll discloses decision aid method and system for a landing on a landing runway and teaches:
instruct the approaching vehicle to land on a second runway based at least in part on the weighted landing distance.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the aircraft landing event system and method of Howell and Jiang to provide for the decision aid method and system for a landing on a landing runway, as taught in Moll, because by virtue of the invention, it is possible to offer the pilot several landing strategies which take account of several parameters and information concerning the air craft and the landing runway. The workload of the pilot is then lightened; which allows the pilot to focus on the landing. (At Moll ¶ [0014])
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHASE L COOLEY whose telephone number is (303)297-4355. The examiner can normally be reached Monday-Thursday 7-5MT.
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, Aniss Chad can be reached at 571-270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/C.L.C./Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662