DETAILED ACTION
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 a Final Action in response to the communications filed on 8/22/2025.
Claims 5, 15, and 19 have been cancelled.
Applicant has amended claims 1, 9, 11, and 18.
Claims 22 – 24, are new.
Claims 1 – 4, 6 – 9, 11 – 14, 16 – 18, and 20 – 24 are pending in this application.
Response to Remarks
Examiner’s Response to Rejections
Rejection under 35 U.S.C. § 101.
Rejection under 35 U.S.C. § 103.
Rejection under 35 U.S.C. § 112.
Examiner Reply to Rejection under 35 U.S.C. § 101.
Applicant argues that the Examiner's characterization of the claims reciting mental processes and mathematical expressions is incorrect, and that the claims themselves do not recite merely a mental process evaluating data and mathematical expressions at least because claim 1 recites a variety of non-human activities.
Examiner respectfully disagrees. Applicant’s claims 1 – 4, 6 – 9, 11 – 14, 16 – 18, 20, and 21, recite the abstract ideas, mental processes and mathematical concepts. For instance, the steps of receiving, via a processor and by a device, historical data recorded on a database, the historical data including a set of actual past turn times of the subject vehicle at the first station and available turn times of the subject vehicle at the first station; creating, by the device, a two-dimensional (2D) scatter plot of the historical data via the processor, wherein the 2D scatter plot is comprised of a plurality of data points; determining, by the device, two straight lines on the 2D scatter plot based on a volume of the plurality of data points; identifying, by the device, an inflection point on the 2D scatter plot as a point of intersection of the two straight lines on the 2D scatter plot; determining, by the device, the cost-optimal minimum turn time via the processor using the inflection point; and executing, by the device, a scheduling action of the subject vehicle via the processor using the cost-optimal minimum turn time, including rescheduling a departure of the subject vehicle from the first station based on the cost-optimal minimum turn time, wherein executing the scheduling action of the subject vehicle comprises: displaying the cost-optimal minimum turn time associated with the subject vehicle on a heatmap chart via a display screen at the first station, and causing the cost-optimal minimum turn time associated with the subject vehicle on the heatmap chart to be provided to a display screen at the second station, wherein the subject vehicle is at the first station and impacts scheduling at the second station are merely mental processes. Applicant’s claim 1 merely receives historical data, plots the historical data in a 2D scatter plot, plots two straight lines on the 2D scatterplot, observes an inflection point of intersection of the two straight lines, evaluates the cost-optimal minimum turn time using the inflection point, and evaluates a scheduling action using the cost-optimal minimum turn time and all can be performed with the human mind, pen a paper. Thus claim 1 recites mental processes.
Claim 1 recites mathematical concepts. For example, the steps of creating, a two-dimensional (2D) scatter plot… plurality of data points; determining two straight lines on the 2D scatter plot based on a volume of the plurality of data points, identifying an inflection point on the 2D scatter plot as a point of intersection of the two straight lines on the 2D scatter plot, and determining the cost-optimal minimum turn time using the inflection point, and executing a scheduling action of the subject vehicle using the cost- optimal minimum turn time, including rescheduling a departure of the subject vehicle from the station based on the cost-optimal minimum turn time, wherein executing the scheduling action of the subject vehicle comprises: displaying the cost-optimal minimum turn time associated with the subject vehicle on a heatmap chart via a display screen at the first station, and causing the cost-optimal minimum turn time associated with the subject vehicle on the heatmap chart to be provided to a display screen at the second station, wherein the subject vehicle is at the first station and impacts scheduling at the second station recites mathematical concepts that are particularly mathematical relationships, as the heatmap scores different areas by a threshold indicated by a color. Accordingly claim 1 recites mathematical concepts. Claims 11 and 18, are similar to claim 1 and recite the same abstract ideas as claim 1.
Examiner Reply to I. The Claims Integrate any Alleged Abstract Idea into a Practical Application.
Applicant argues claim 1 integrates the alleged abstract idea into a practical application.
Examiner respectfully disagrees. Applicant’s claim 1 does not integrate the judicial exceptions into a practical application. Claim 1 recites the additional elements of a scheduling system, a device, subject vehicle, a first station, second station, heatmap chart, first airport, second airport, a database, communicatively coupled to the processor, and a processor, but these additional elements are merely generic computer components, and uses a computer as a tool to perform generic computer functions. Claim 1, amounts to no more than mere instructions using generic computer components to implement the judicial exception, and there are no additional elements that integrate the judicial exceptions into a practical application. Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components. Applicant recites in Remarks “From the perspective of aircrews, ground crews, passengers, and customers, it is desirable to minimize turn time. However, the performance of the myriad of different tasks at the station 12 requires at least a minimum amount of turn time. This minimum turn time is impacted by a host of factors, some of which are fixed/predetermined and others of which will vary with the particular location of station 12, as well as the date, time of day/week/month/year, type of aircraft 10, etc. As a result, it is often difficult to accurately predict turn times on a given day of operations. This uncertainty, represented in FIG. 1 by symbol 14, results in unreliability of generated flight schedules, which in turn leads to elevated operating and opportunity costs, passenger dissatisfaction, crew fatigue, and other potential problems. The present solutions are therefore intended to address these and other potential problems, thereby improving upon the current state of the art of flight scheduling and related systems.” However, these are merely variables used when seeking to optimize a problem of uncertainty as we have here where Applicant is merely modeling flight operations and using the computer as a tool to perform the judicial exceptions. There is no improvement here, as Applicant’s claim 1 merely receives historical data, plots the historical data in a 2D scatter plot, plots two straight lines on the 2D scatterplot, observes an inflection point of intersection of the two straight lines, evaluates the cost-optimal minimum turn time using the inflection point, and evaluates a scheduling action using the cost-optimal minimum turn time that includes a heatmap chart with three different colors. This is not an improvement to a technological field nor is this an improvement to the computer, and Applicant’s claim 1 is not significantly more than the judicial exceptions. Claims 11 and 18, are similar to claim 1 and are not integrated into a practical application and are not significantly more than the judicial exceptions. Accordingly, claims 1 – 4, 6 – 9, 11 – 14, 16 – 18, 20, and 21 remain rejected under 35 U.S.C. § 101.
Examiner’s Reply to Rejection under 35 U.S.C. § 103 based on Basanets, Agrawal, and Herriot.
Applicant argues the applied references, whether taken alone or in any reasonable combination, do not disclose claims 1, 6, 11, 16, 18, 20, and 21 and the cited sections of the applied references do not discloses Applicant’s amendments.
Examiner respectfully disagrees. Claims 6 and 16 recite “wherein the subject vehicle is an aircraft, and the first station is an airport or a terminal of the airport” and Basanets teaches these claims in the following paragraphs teaches 0019 and 0023; Basanets further teaches claims 6 and 16, where it is recited in 0021, turn time data can be separated into data for different aircraft types and for particular airports. Basanets teaches Applicant’s Claim 20, where Basanets recites in 0051, Basanets teaches in ¶ 0051, After constructing the sequential flights for all or most of the available aircraft within a fleet (e.g., a few aircraft may be reserved as spares), the Jeppesen® flight planning software performs the “crew pairing” step.… the system may seek to select a particular aircraft and crew which will yield the best operating result. Basanets, Agrawal, and Herriot teach claims 1, 11, and 18 and Agrawal further teaches claim 21, where Agrawal teaches in Fig. 4, Airport 402A, Airport 402B, and Airport 402C where there is monitoring of aircraft at multiple airports. Claims 2 – 4, 7 – 9, 12 – 14, and 17 depend from claims 1 and 11; claims 1, 11, and 18 are rejected under 35 U.S.C. § 103. Applicant has amended claims 1, 11, and 18 and further search and consideration is required to respond to Applicant’s amendments and new claims 22 – 24.
Examiner’s Response to Rejection under 35 U.S.C. § 112(a).
Examiner finds that Applicant’s arguments from 08-20-2025 Interview, Applicant’s arguments from Remarks, and Specification ¶ 0052, are persuasive as Applicant has presented support for a first station, second station, first airport, and second airport. Accordingly, rejection under 35 U.S.C. § 112(a) is removed.
Claim Rejections – 35 U.S.C. § 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 – 4, 6 – 9, 11 – 14, 16 – 18, and 20 – 24, are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
Claims 1, 11, and 18 recites:
receiving historical data the historical data including a set of actual past turn times and available turn times;
creating, a two-dimensional (2D) scatter plot of the historical data, wherein the 2D scatter plot is comprised of a plurality of data points;
determining two straight lines on the 2D scatter plot based on a volume of the plurality of data points;
identifying an inflection point on the 2D scatter plot as a point of intersection of the two straight lines on the 2D scatter plot;
determining the cost-optimal minimum turn time using the inflection point;
and executing a scheduling action using the cost-optimal minimum turn time, including rescheduling a departure based on the cost-optimal minimum turn time, wherein executing the scheduling action comprises: displaying the cost-optimal minimum turn time on a display screen,
a set period of time, includes a color-coded background indicative of a relative difference between the cost-optimal minimum turn time and an expected value, wherein a first color in the color-coded background indicates the expected value above a high threshold associated with the cost-optimal minimum turn time, wherein a second color in the color-coded background indicates the expected value below a low threshold associated with cost-optimal minimum turn time, and wherein a third color in the color-coded background indicates the expected value between the high threshold associated with cost-optimal minimum turn time and the low threshold associated with cost-optimal minimum turn time.
The limitations of claim 1, under its broadest reasonable interpretation recites mental processes, related to observation and evaluation of data, but for the recitation of generic computer components, and uses a computer as a tool to perform a mental process. For example, observing historical data; evaluating a two-dimensional (2D) scatter plot; evaluating two straight lines on the 2D scatter plot; observing an inflection point on the 2D scatter plot; evaluating the cost-optimal minimum turn time; and evaluating a scheduling action using the cost-optimal minimum turn time… causing the cost-optimal minimum time to be provided to a display screen wherein a set period of time includes a color-coded background indicative of a relative difference between the cost-optimal minimum turn time and an expected value wherein a first color in the color-coded background indicates the expected value above a high threshold associated with the cost-optimal minimum turn time, wherein a second color in the color-coded background indicates the expected value below a low threshold associated with cost-optimal minimum turn time, and wherein a third color in the color-coded background indicates the expected value between the high threshold associated with cost-optimal minimum turn time and the low threshold associated with cost-optimal minimum turn time, all involve evaluation and observation of data. Accordingly, the claim recites an abstract idea of mental processes.
The limitations of claim 1 under its broadest reasonable interpretation recites mathematical concepts. For example, the steps of creating, a two-dimensional (2D)) scatter plot… plurality of data points; "determining two straight lines on the 2D scatter plot based on a volume of the plurality of data points," "identifying an inflection point on the 2D scatter plot as a point of intersection of the two straight lines on the 2D scatter plot," "determining the cost-optimal minimum turn time using the inflection point," and observing a color-coded background indicative of a relative difference between the cost-optimal minimum turn time and an expected value wherein a first color in the color-coded background indicates the expected value above a high threshold associated with the cost-optimal minimum turn time, wherein a second color in the color-coded background indicates the expected value below a low threshold associated with cost-optimal minimum turn time, and wherein a third color in the color-coded background indicates the expected value between the high threshold associated with cost-optimal minimum turn time and the low threshold associated with cost-optimal minimum turn time can all be performed with the human mind, pen a paper and are mathematical relationships based on coloring a background and plotting data points to reflect an optimal turn time and an inflection point. Further, the step of executing a scheduling action using the cost-optimal minimum turn time, including rescheduling a departure based on the cost-optimal minimum turn time, wherein executing the scheduling action comprises: displaying the cost-optimal minimum turn time associated with the subject vehicle on a heatmap chart via a display screen, and causing the cost-optimal minimum turn time associated with the subject vehicle on the heatmap chart to be provided to a display screen are all mathematical relationships, as the heatmap chart provides a value for different areas and indicates the value by color, such as green, yellow, and red. Accordingly, claim 1 recites mathematical concepts.
The dependent claims encompass the same abstract ideas as well. For instance, claim 2 is directed towards evaluating a Hough transform, claim 3 is directed towards evaluating the two straight lines on a 2D scatter plot using an iterative procedure, claim 4 is directed towards a predetermined static slope parameter; claim 6 is directed towards observing the subject vehicle is an aircraft, and the first station is an airport or a terminal of the airport; claim 7 is directed towards modeling flight delay propagation; claim 8 is directed towards evaluating a Gumbel approximation; claim 9 is directed towards evaluating the scheduling action is observing the cost-optimal minimum turn time to evaluate a future impact on a predicted reliability level of the expected value; claim 12 is directed towards evaluating a Hough transform; claim 13 is directed towards evaluating two straight lines using an iterative procedure, including applying a predetermined static slope parameter and a dynamic intercept parameter; claim 14 is directed towards observing a predetermined static slope parameter; claim 16 is directed towards observing the subject vehicle is an aircraft, and the first station is an airport or a terminal; claim 17 is directed towards observing a scheduling action includes modeling propagation of a flight delay at the airport through a plurality of airports; claim 20 is directed towards a scheduling action; claim 21 is observing the cost-optimal minimum turn time includes modeling flight delay propagation through a plurality of airports; and claims 22, 23, and 24, are directed towards observing a location where travel begins and is a different location where travel ends. Thus, the dependent claims further limit the abstract concepts found in the independent claims.
These judicial exceptions are not integrated into a practical application. Claims 1, 11, and 18 recite the additional elements of a scheduling system, a device, subject vehicle, a first station, second station, heatmap chart, first airport, second airport, a database, communicatively coupled to the processor, and a processor, and the additional elements are merely generic computer components, and uses a computer as a tool to perform generic computer functions. The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., a processor) per Applicant’s Specification as shown below:
“[0034] Referring briefly to FIG. lA, such a scheduling system 11 may include a flight historical database (DB) 13,memory (M) 15, and one or more processors (P) 17,6 e.g., application-specific integrated circuits, microprocessors, or processing cores. Although omitted from the Figures for illustrative simplicity, the scheduling system 11 may include other hardware and software elements, such as but not limited to input/output (I/O) devices, graphics boards, filters, and the like. Memory 15 for its part may include application suitable amounts of random access memory (RAM), read only memory (ROM), flash memory or other solid-state memory, etc. Together, these and other possible hardware components execute instructions embodying a method 50, an example of which is depicted in Fig. 3 and described below. To that end, the database 13 includes recorded flight historical data 13D, including a set of actual turn times of the aircraft 10 or other subject vehicle at the station 12. The flight historical database 13 and/or an external client database 130 also includes available turn times 130D of the aircraft 10 at the station 12, e.g., as predetermined turn times for a given aircraft 10, station 12, etc. The instructions for determining a cost-optimal minimum turn time of the aircraft 10 at the station 12 are recorded in memory 15. Execution of the instructions by the processor 17 causes the processor 17 to execute the present method 50, with the processor 17 ultimately outputting publication data 60 for consumption by a variety of end users, with the publication data 60 in some embodiments being displayable via a display screen 110 of the system 11 as described below with particular reference to Figs. 10-12.”
and thus are not practically integrated nor significantly more.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a scheduling system, a device, subject vehicle, a first station, second station, heatmap chart, first airport, second airport, database, and a processor are considered a generic computer components, and the claims amount to no more than mere instructions using generic computer components to implement the judicial exception. Each of the additional limitations are no more than mere instructions to apply the exception using a generic computer component (e.g., a processor). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Dependent claims 2 – 4, 6 – 9, 12 – 14, 16 – 17, and 20 – 24, when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1 – 4, 6 – 9, 11 – 14, 16 – 18, and 20 – 24, are not patent eligible.
Claim Rejections – 35 U.S.C. §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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 6, 9, 16, 11, 18 and 20 – 21 are rejected under 35 U.S.C. § 103 as being unpatentable over Basanets, Oleksandr et al. (U.S. Publication No. 2018/026,8335) hereinafter “Basanets” in view of Agrawal, Ashutosh (U.S. Publication No. 2015/034,8422) hereinafter “Agrawal ‘8422” in view of Herriot, James W (U.S. Patent No. 9,417,070) hereinafter “Herriot”.
Claims 1, 11, and 18:
A method for determining a cost-optimal minimum turn time of a subject vehicle at a first station, comprising: Basanets teaches in ¶ 0046 – 0047, a method for calculating the shortest realistic turn time for an airplane at an airport;
receiving, via a processor and by a device, historical data recorded on a database, the historical data including a set of actual past turn times of the subject vehicle at the first station and available turn times of the subject vehicle at the first station; Basanets teaches in ¶ 0003, receiving electronic data related to instances of past operations of at least one vehicle that are available durations of time and actual durations of time for an aspect of the past operations using a computer processor;
creating, by the device, a two-dimensional (2D) scatter plot of the historical data via the processor, wherein the 2D scatter plot is comprised of a plurality of data points; Basanets teaches in ¶ 0014, a scatterplot of available turn time versus actual turn time;
determining, by the device, two straight lines on the 2D scatter plot based on a volume of the plurality of data points; Basanets teaches in Fig. 1, a scatterplot where two straight lines (110 and 112) with multiple data points shown in the scatterplot;
identifying, by the device, an inflection point on the 2D scatter plot as a point of intersection of the two straight lines on the 2D scatter plot; Basanets teaches in Fig. 1, a scatterplot with an intersecting point, 114, of two straight lines;
determining, by the device, the cost-optimal minimum turn time via the processor using the inflection point; Basanets teaches in ¶ 0021, using linear regression, identifying the shortest realistic turn time. Basanets further teaches in ¶ 0022, Fig. 1, a scatterplot with the available turn times on the horizontal axis and actual turn times on the vertical axis of the scatterplot with an intersecting point identified that is likened to an inflection point.
and executing, by the device, a scheduling action of the subject vehicle via the processor using the cost-optimal minimum turn time, including rescheduling a departure of the subject vehicle from the first station based on the cost-optimal minimum turn time, wherein executing the scheduling action of the subject vehicle comprises: displaying the cost-optimal minimum turn time associated with the subject vehicle on a heatmap chart via a display screen at the first station; Basanets teaches in ¶ 0025, delay propagation effects of aircraft vehicles during operations; Basanets further teaches displaying turn time in ¶ 0049, Jeppesen® flight planning software outputs the shortest realistic turn time; Basanets teaches in ¶ 0021, identifying the shortest realistic turn times and automatically using these turn times in the planning of future vehicle operations;
While Basanets teaches delay propagation effects during operations and cost optimal turn time, Basanets does not explicitly teach displaying at a first station and second station. However, Agrawal ‘8422 teaches the following:
and causing the cost-optimal minimum turn time associated with the subject vehicle on the heatmap chart to be provided to a display screen at the second station, wherein the subject vehicle is at the first station and impacts scheduling at the second station; Agrawal teaches in ¶ 0017, the time taken for each aircraft turnaround activity at each airport during journey of the aircraft is obtained by the ground station system. Agrawal teaches in Fig. 4, monitoring turn around activities at each airport; Agrawal teaches in ¶ 0032, aircraft turnaround optimizer sends alerts to one or more ground handling units associated with the one or more aircraft turnaround activities via the ground handling units interface, where alerts sent is likened to displaying. Agrawal teaches in ¶ 0033, prior to landing of the aircraft, the aircraft on-board system provides the optimized aircraft turnaround schedule to one or more ground handling units via the ground handling units interface. Exemplary ground handling units interface include a display of a computing system. The aircraft on-board system sends alerts to the one or more ground handling units based on the progress of the aircraft turnaround schedule, where the one or more ground handling units may be likened to one or more terminals, airports, and the first station and a second station.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine automatically identifying a shortest duration of time for an aspect of an operation of Basanets with a system and method for providing an aircraft turnaround schedule of Agrawal to assist businesses with sending alerts to airports of turnaround activities for the aircraft (Agrawal, Spec. ¶ 0031).
While Basanets teaches delay propagation effects during operations and cost optimal turn time, and Agrawal teaches monitoring turnaround activities at each airport, neither Basanets nor Agrawal ‘8422 explicitly teach heatmap. However, Herriot teaches the following:
wherein the heatmap chart includes at least the first station and a second station for a set period of time, and wherein the heatmap chart includes a color-coded background indicative of a relative difference between the cost-optimal minimum turn time and an expected value of the subject vehicle, wherein a first color in the color-coded background indicates the expected value above a high threshold associated with the cost-optimal minimum turn time, wherein a second color in the color-coded background indicates the expected value below a low threshold associated with cost-optimal minimum turn time, and wherein a third color in the color-coded background indicates the expected value between the high threshold associated with cost-optimal minimum turn time and the low threshold associated with cost-optimal minimum turn time; Herriot teaches in col. 5, lines 61 – 67, and col. 6, lines 1 – 7, Fig. 2 illustrates conceptually a grid 200 with fewer grid tiles 202, than that illustrated in Fig. 1, and in which the individual grid tiles are labeled with a numeric value of the summation magnitude associated with each grid tile following propagation of the repulsion signals and attraction signal. In the color scheme of Fig. 2, high numbers are ‘hot’ (red), while low numbers are ‘cold’ (green, blue). Obstacles can be thought of as ‘hot’ or ‘high’ and the Primary Aircraft flight path as ‘cold’ or ‘low’. The net effect of all of the flight objects propagating their signals through the grid tiles comprising the airspace is that the best path for the Primary Aircraft flies through the coldest, lowest area or ‘canyon’ away from the hot obstacles and towards its cold flight path to the extent possible. Herriot teaches in col. 6 lines 6 – 56, As illustrated in Fig. 2, the canyon bottom is far from monotonic in color. It may be the lowest path between the obstacles 204, e.g. walls, but it still can have various ups and downs that create difficulty identifying the optimal path, i.e. the bottom of the canyon. In order to further identify the safest and most efficient path among a plurality of stationery and/or moving obstacles, in addition to the ‘heat’ values illustrated in Fig. 2, the disclosed technique further contemplates the addition of another value to the summation value 1334 of each grid tile 202, which value represents the shortest ‘distance’ to the vehicle origin on the grid from each grid tile, where “shortest” represents the smallest sum of the heat values to get there. Next, the disclosed technique further contemplates the addition of another value to the summation value 1334 of each grid tile 202, which value represents the shortest ‘distance’ to the vehicle destination on the grid from each grid tile, where “shortest” represents the smallest sum of the heat values to get there. This method essentially floods the grid, beginning from the grid tile 406 representing the destination location of the Primary Aircraft within the grid 400, e.g., middle tile on the right side of Fig. 4, and propagates distance “signals” or values throughout all grid tiles 202. Fig. 4 illustrates conceptually a grid 400 that results from the flooding the grid tiles 202 outward from the destination 406, summing up the repulsion quantities, and attraction quantities and favoring the smallest sum, i.e. shortest ‘distance’ (in these terms) from the destination. This operation floods the grid, beginning from the destination, e.g., middle tile on the right side of Fig. 4, generating shortest ‘distance’ values from the destination. When the two shortest distances from the Primary Aircraft origin and vehicle destination are summed, the resulting color map grid is illustrated in Fig. 4. The ‘canyon’ or safest most efficient path is now a series of adjacent grid tiles 402 having identical summation values 1334, here all 8306, representing the safest most efficient path, and, therefore bottom of the canyon, is readily identifiable. Once the bottom of the canyon is computed a path or trajectory therethrough is clearly identifiable as illustrated by the way line 502 in grid 500 Fig. 5, which is illustrated with a larger number of grid tiles since the individual value of each grid tile is over no longer necessary;
on the heatmap chart via display screen; Herriot teaches in col. 2, lines 31 – 33, heat map with an optimal flight path.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine automatically identifying a shortest duration of time for an aspect of an operation of Basanets and a system and method for providing an aircraft turnaround schedule of Agrawal ‘8422 with a method and system for continuously re-planning a vehicle's path, in the face of stationary and moving obstacles, dynamically calculating a new path in real time and displaying a grid heat map of Herriot to assist businesses with displaying an optimal flight path using a heat map (Herriot, Spec. col. 2 line 32).
Claims 6 and 16:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Basanets further discloses the following limitation:
the subject vehicle is an aircraft, and the first station is an airport or a terminal of the airport; see at least Basanets teaches in ¶ 0019, … aircraft… Basanets further teaches in ¶ 0023, the vehicle is a commercial aircraft, the scatterplot 200 may pertain to a particular aircraft model at a particular airport.
Claim 9:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Basanets further discloses the following limitation:
wherein executing the scheduling action includes using the cost-optimal minimum turn time to determine a future impact on a predicted reliability level of the expected value; see at least Basanets teaches in ¶ 0021, turn time data of commercial aircraft and airports is analyzed to automatically identify shortest realistic turn times for different vehicles at different locations. The identified shortest realistic turn times can then be automatically used to plan future vehicle operations. Basanets teaches in ¶ 0049, identify shortest realistic turn times for the aircraft type and for the different airports the aircraft is expected to fly to.
Claim 20:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Basanets further discloses the following limitation:
using the cost-optimal minimum turn time to schedule a crew pairing of the aircraft; see at least Basanets teaches in ¶ 0051, After constructing the sequential flights for all or most of the available aircraft within a fleet (e.g., a few aircraft may be reserved as spares), the Jeppesen® flight planning software performs the “crew pairing” step.… the system may seek to select a particular aircraft and crew which will yield the best operating result…).
Claim 21:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Agrawal further teaches the following:
wherein the cost-optimal minimum turn time includes modeling flight delay propagation through a plurality of airports; Agrawal ‘8422 teaches in Fig. 4, Airport 402A, Airport 402B, and Airport 402C where there is monitoring of aircraft at multiple airports.
Claims 22, 23, and 24:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Herriot further teaches the following:
wherein the first station is a location where the subject vehicle's travel begins and wherein the second station is a different location where the subject vehicle's travel ends; Herriot teaches in col. 6, lines 20 – 28, This method essentially floods the grid, beginning from the grid tile 302 representing the origin location of the primary craft within the grid, e.g., middle tile on the left side of FIG. 3, and propagates distance “signals” or values throughout all grid tiles 202. FIG. 3 illustrates conceptually a grid 300 that results from the flooding the grid tiles 202 outward from the origin 302, summing up the repulsion quantities, and favoring the smallest sum, i.e. shortest ‘distance’ (in these terms) from the origin.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine automatically identifying a shortest duration of time for an aspect of an operation of Basanets and a system and method for providing an aircraft turnaround schedule of Agrawal ‘8422 with a method and system for continuously re-planning a vehicle's path, in the face of stationary and moving obstacles, dynamically calculating a new path in real time and displaying a grid heat map of Herriot to assist businesses with displaying an optimal flight path using a heat map (Herriot, Spec. col. 2 line 32).
Claims 2 and 12 are rejected under 35 U.S.C. § 103 as being unpatentable over Basanets, Oleksandr et al. (U.S. Publication No. 2018/026,8335) hereinafter “Basanets” in view of Agrawal, Ashutosh et al. (U.S. Publication No. 2015/034,8422) hereinafter “Agrawal ‘8422” in view of Herriot, James W (U.S. Patent No. 9,417,070) hereinafter “Herriot” in view of Pestun, Vadim et al. (U.S. Patent No. 10,127,449) hereinafter “Pestun”.
Claim 2:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Basanets further teaches the plurality of data points. Neither Basanets nor Agrawal ‘8422 explicitly disclose Hough transform. However, Pestun discloses the following:
performing a Hough transform on the plurality of data points via the processor to thereby derive the two straight lines; see at least Pestun col. 22, lines 42 – 44, the entity detector may locate short straight lines based on edges by using techniques, such as, for example, Hough Line Transform…
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine automatically identifying a shortest duration of time for an aspect of an operation of Basanets and a system and method for providing an aircraft turnaround schedule of Agrawal and a method and system for continuously re-planning a vehicle's path, in the face of stationary and moving obstacles, dynamically calculating a new path in real time and displaying a grid heat map of Herriot with a method for condition detection using image processing on vehicles (e.g., aircraft) of Pestun to determine straight lines based on points of edges using Hough transform in order to assist businesses with locating straight lines using images and Hough transform to optimize business operations (Pestun, Spec. ¶¶ col. 4 lines 8-12, col. 4 line 30, and col. 22 lines 42-45).
Claim 12:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Basanets further teaches the plurality of data points. Neither Basanets nor Agrawal ‘8422 explicitly disclose a Hough transform to derive two straight lines. However, Pestun discloses the following:
wherein the execution of the instructions by the processor causes the processor to perform a Hough transform on the plurality of data points to thereby derive the two straight lines; see at least Pestun col. 22, lines 42 – 44, the entity detector may locate short straight lines based on edges by using techniques, such as, for example, Hough Line Transform…
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine automatically identifying a shortest duration of time for an aspect of an operation of Basanets and a system and method for providing an aircraft turnaround schedule of Agrawal and a method and system for continuously re-planning a vehicle's path, in the face of stationary and moving obstacles, dynamically calculating a new path in real time and displaying a grid heat map of Herriot with a method for condition detection using image processing on vehicles (e.g., aircraft) of Pestun to determine straight lines based on points of edges using Hough transform in order to assist businesses with locating straight lines using images and Hough transform to optimize business operations (Pestun, Spec. ¶¶ col. 4 lines 8-12, col. 4 line 30, and col. 22 lines 42-45).
Claims 3, 4, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Basanets, Oleksandr et al. (U.S. Publication No. 2018/026,8335) hereinafter “Basanets” in view of Agrawal, Ashutosh (U.S. Publication No. 2015/034,8422) hereinafter “Agrawal ‘8422” in view of Herriot, James W (U.S. Patent No. 9,417,070) hereinafter “Herriot” in view of Borkar et al. "Polar randomized Hough transform for lane detection using loose constraints of parallel lines," 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 2011, pp. 1037-1040 hereinafter “Borkar”.
Claims 3 and 13:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Neither Basanets, Agrawal ‘8422, nor Herriot explicitly disclose iterative procedure. However, Borkar discloses the following limitation:
deriving the two straight lines on the 2D scatter plot using an iterative procedure, including applying a predetermined static slope parameter and a dynamic intercept parameter; 2D scatter plot is taught above in claim 1; see at least Borkar teaches in 2.3. Polar Randomized Hough Transform in each iteration of the RHT, two non-zero pixels at (x1,y1) and (x2,y2) are randomly selected without replacement from the binary image. Since two pixels are trivially collinear, the parameters of the line on which they lie can be determined by solving the system of equations below:
y1=mx1+b
y2=mx2+b.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine automatically identifying a shortest duration of time for an aspect of an operation of Basanets and a system and method for providing an aircraft turnaround schedule of Agrawal and a method and system for continuously re-planning a vehicle's path, in the face of stationary and moving obstacles, dynamically calculating a new path in real time and displaying a grid heat map of Herriot with a methodology for detecting lane markers and using the Polar Randomized Hough Transform to fit lines and evaluate the lines of Borkar to assist businesses with finding straight lines using Hough transform to optimize business operations (Borkar, Section 4. ¶ Conclusion).
Claims 4 and 14:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Neither Basanets, Agrawal ‘8422, nor Herriot explicitly disclose slope. However, Borkar discloses the following limitation:
wherein the predetermined static slope parameter is 0.41; see at least Borkar teaches in 2.3. Polar Randomized Hough Transform when the two points lie on a near vertical line as the slope approaches +/−∞, where static slope parameter of 0.41 is included.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine automatically identifying a shortest duration of time for an aspect of an operation of Basanets and a system and method for providing an aircraft turnaround schedule of Agrawal and a method and system for continuously re-planning a vehicle's path, in the face of stationary and moving obstacles, dynamically calculating a new path in real time and displaying a grid heat map of Herriot with a methodology for detecting lane markers and using the Polar Randomized Hough Transform to fit lines and evaluate the lines of Borkar to assist businesses with finding straight lines using Hough transform to optimize business operations (Borkar, Section 4. ¶ Conclusion).
Claims 7, and 17, are rejected under 35 U.S.C. § 103 as being unpatentable over Basanets, Oleksandr et al. (U.S. Publication No. 2018/026,8335) hereinafter “Basanets” in view of Agrawal, Ashutosh (U.S. Publication No. 2015/034,8422) hereinafter “Agrawal ‘8422” in view of Herriot, James W (U.S. Patent No. 9,417,070) hereinafter “Herriot” in view of Villa, Ian Andreas et al. (U.S. Publication No. 2019/034,0934) hereinafter “Villa”.
Claim 7:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Neither Basanets, Agrawal ‘8422, nor Herriot explicitly disclose flight delay; however, Villa discloses the following:
wherein executing the scheduling action using the cost-optimal minimum turn time includes modeling flight delay propagation through a plurality of airports. see at least Villa teaches in ¶ 0067, receiving, in operation, a request to route a VTOL aircraft from a first location to a second location. Villa teaches in ¶ 0068, the candidate route selection module calculates a set of candidate routes from the first location to the second location. Villa teaches in ¶ 0054, The route selection module may delay departure of the VTOL and periodically (e.g., every five minutes) repeat the process until conditions have changed. Villa further teaches in ¶ 0056, the transport network coordination system identifies candidate routes for transport between Hub A and Hub B, where Hub A and Hub B are likened to plurality of airports.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine automatically identifying a shortest duration of time for an aspect of an operation of Basanets and a system and method for providing an aircraft turnaround schedule of Agrawal and a method and system for continuously re-planning a vehicle's path, in the face of stationary and moving obstacles, dynamically calculating a new path in real time and displaying a grid heat map of Herriot with generating an optimized network of flight paths and an operations volume around each of these flight paths of Villa in order to assist businesses with selecting the lowest cost route (Villa, Spec ¶ 0070).
Claim 17:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Neither Basanets, Agrawal ‘8422, nor Herriot explicitly disclose flight delay; however, Villa discloses the following:
wherein the scheduling action includes modeling propagation of a flight delay at the airport through a plurality of airports; see at least Villa teaches in ¶ 0021 in aspects described herein, turn time data is analyzed to automatically identify shortest realistic turn times for different vehicles at different locations. The identified shortest realistic turn times can then be automatically used to plan future vehicle operations …. or particular airports); Villa further teaches in ¶ 0021, isolated instances can be identified using noise reduction algorithms).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine automatically identifying a shortest duration of time for an aspect of an operation of Basanets and a system and method for providing an aircraft turnaround schedule of Agrawal and a method and system for continuously re-planning a vehicle's path, in the face of stationary and moving obstacles, dynamically calculating a new path in real time and displaying a grid heat map of Herriot with generating an optimized network of flight paths and an operations volume around each of these flight paths of Villa in order to assist businesses with selecting the lowest cost route (Villa, Spec ¶ 0070).
Claim 8 is rejected under 35 U.S.C. § 103 as being unpatentable over Basanets, Oleksandr et al. (U.S. Publication No. 2018/026,8335) hereinafter “Basanets” in view of Agrawal, Ashutosh (U.S. Publication No. 2015/034,8422) hereinafter “Agrawal ‘8422” in view of Herriot, James W (U.S. Patent No. 9,417,070) hereinafter “Herriot” in view of Bukowski, Richard William (U.S. Publication No. 2021/013,3218) hereinafter “Bukowski”.
Claim 8:
Basanets, Agrawal ‘8422, and Herriot disclose claims 1, 11, and 18. Neither Basanets, Agrawal ‘8422, nor Herriot explicitly disclose modeling the flight delay propagation. However, Bukowski discloses the following limitation:
wherein modeling the flight delay propagation through the plurality of airports includes performing a Gumbel approximation; see at least Bukowski teaches in ¶ 0055, the vehicle can include any device used to carry or transport persons or cargo including an automobile, a bus, a cargo truck, an aircraft (e.g., airplane and/or helicopter), and/or watercraft (e.g., boat or submersible vehicle). Uncertainty may be expressed using a joint probability model. Uncertainty may be expressed using parameters derived from a joint probability model or simplifications of any probability model or density function. Covariances between likelihoods or variances representing a simplification of a more general probability density function or probability model can be used. Applicant’s Spec. ¶ 0012, … modeling flight delay propagation may include performing a Gumbel approximation, where Examiner interprets that a Gumbel approximation may use the probability density function for model approximation.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine automatically identifying a shortest duration of time for an aspect of an operation of Basanets and a system and method for providing an aircraft turnaround schedule of Agrawal and a method and system for continuously re-planning a vehicle's path, in the face of stationary and moving obstacles, dynamically calculating a new path in real time and displaying a grid heat map of Herriot with systems and methods that provide measures of uncertainty for map features provided in association with a vehicle map service and to express parameters from a probability density function of Bukowski to assist businesses with predicting parameters and uncertainty modeling for digital maps (Bukowski, Spec. ¶ 0027).
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference De Prins; Johan L. (U.S. Publication No. 2020/0168106) discloses a system and a method for planning and flying a cost-optimal cruise vertical profile in combination with a required time-of-arrival (RTA) constraint.
- Reference Miller, H. Roy (U.S. Publication No. 2007/0214033) discloses Methods for scheduling a transportation operation such as the operation of an airline.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
01/02/2026
/ROBERT D RINES/Primary Examiner, Art Unit 3625