Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in the Republic of India on 06/25/2024. It is noted, however, that applicant has not filed a certified copy of the IN202411048720 application as required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/26/2026 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
The following is a non-final office action in response to the communication filed on 06/09/2025.
Claims 1-21 are pending and have been examined.
Claims 1-21 are rejected.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Villa et al. (US 2019/0340934 A1, hereinafter Villa).
Claim 1 Discloses:
“A flight plan optimization system comprising:”
Villa teaches, (Abstract, Lines 1-3) “generating an optimized network of flight paths and an operations volume around each of these flight paths.”
“a graph representation operator configured to generate a graph of potential flight paths and actual obstacles in the potential flight paths,”
Villa teaches, (Paragraph [0108]) “The network creation module 1205 manages the creation of a source network of flight paths or routes. The source network represents nearly a complete set of all possible flight paths between any two points (e.g., vertiports or hubs) given constraints … A series of edges between two points (e.g., origin and destination) forms a flight path or route,” wherein, (Paragraph [0109], Lines 1-6) “Once the source network of paths or routes is created, the cost module 1210 assigns a cost for traversing each edge. Accordingly, the cost module 1210 accesses data sets for use in the cost determination from the datastores 1230. The data sets include one or more of historical aircraft track data, terrain altitude data, obstacle/building height.”
“the potential flight paths extending from a takeoff location to a destination location;”
Villa teaches, (Paragraph [0115], Lines 1-3) “Once the cost for traversal of each edge is assigned, the cost module 1210 determines a lowest or minimum cost path between each relevant origin and destination.”
Villa additionally teaches, (Paragraph [0080]) “The weather prediction module 725 may monitor ground effect and gusts at final approaches and take offs (FATOs) and touchdowns and liftoffs (TLOFs). Using sensors to characterize velocity and pressure fields close to the ground plane which can then be compared to predictive characterizations of the vertiport may help schedule vehicle takeoffs and landings.”
“a predictive analysis model configured to receive the graph and environmental data and generate predictions of future environmental conditions based on the graph and the environmental data;”
Villa teaches, (Paragraph [0047], Lines 1-5) “The parameter selection module 305 provides a user interface for defining various parameters to be used in the optimization of VTOL route selection … In one embodiment, the definable parameters include network and environmental parameters and objectives,” wherein, (Paragraph [0049], Lines 1-4) “The data processing module 310 accesses network and environmental data needed to calculate candidate routes for VTOL travel based on one or more selected parameters and/or objectives,” further wherein, (Paragraph [0056], Lines 5-14) “Each candidate route 400A, 400B, and 400C is calculated based on network and environmental parameters and objectives, such as the presence and location of other VTOL hubs, current locations of other VTOL aircraft 220, planned routes of other VTOL aircraft 220, predetermined acceptable noise levels and current and predicted weather between Hub A 405 and Hub B 410, and localized weather (e.g., sudden downbursts, localized hail, lightening, unsteady wind conditions) in the vicinity of the planned routes.”
“a cost function and heuristic estimate operator configured to determine costs associated with flight paths of the potential flight paths based on the future environmental conditions;”
Villa teaches, (Paragraph [0065], Lines 1-8) “At operation 550, the route selection module 320 determines a route for the VTOL aircraft 220 based on the vehicle noise profile and noise conditions of the map data. The route selection module 320 may determine a route cost for a number of predetermined candidate routes and select the candidate route with the lowest route cost. In one embodiment, route cost may be a function of distance, energy, cost, time, noise, observer annoyance, etc,” and that, (Paragraph [0114], Lines 1-9) “Using the accessed data, the cost engine 1210 aggregates or integrates relevant data sets for each node of an edge and assigns a scalar value of cost for each edge as it relates to each metric. Ideally, the cost engine optimizes by minimizing costs as a function of these different metrics. The cost engine 1210 determines a weighted cost for each edge by incorporating metrics and a weight vector. In example embodiments, the cost engine 1210 calculates the cost of traversing each node.”
“and a predictive pathfinding model configured to identify a flight path of the potential paths based on the costs,”
Villa teaches, (Paragraph [0116], Lines 1-2) “The route optimization engine 1215 optimizes these lowest cost paths.”
“aircraft specific performance data,”
Villa teaches, (Paragraph [0064], Lines 1-13) “The data processing module 310 also accesses, in operation 540, vehicle noise profile data based on the vehicle data type of the VTOL aircraft 220 … Such data can correspond to the vehicle type, the state of the health and maintenance of the VTOL aircraft 220, and gross weight.”
“air traffic control constraints,”
Villa teaches, (Paragraph [0063], Lines 3-11) “Map data for a VTOL aircraft 220 may include data indicative of … air traffic, etc. Map data may also utilize previous weather, noise, and air traffic data for predictive purposes,” and that, (Paragraph [0104], Lines 18-24) “the optimized network improves integration with air traffic control (ATC) by providing insight into intentions of aircraft using the optimized network and allow the ATC to determine if these aircraft are not in conformance.”
“current environmental conditions,”
Villa teaches, (Paragraph [0063], Lines 6-8) “The map data may also be indicative of dynamic real-time information such as current weather in the area, localized weather.”
“and the future environmental conditions.”
Villa teaches, (Paragraph [0071], Lines 1-6) “FIG. 7 illustrates … a weather prediction module 725,” and additionally that, (Paragraph [0040], Lines 6-10) “The computer system of the VTOL aircraft 220 may also receive information, such as routing and weather information and information regarding the current location and planned routes of VTOL aircraft 220 in the vicinity of the VTOL aircraft 220.”
Claim 8 Discloses:
“A method comprising:”
Villa teaches, (Abstract, Lines 1-3) “generating an optimized network of flight paths and an operations volume around each of these flight paths.”
“generating, by a graph representation operator, a graph of potential flight paths and actual obstacles in the potential flight paths,”
Villa teaches, (Paragraph [0108]) “The network creation module 1205 manages the creation of a source network of flight paths or routes. The source network represents nearly a complete set of all possible flight paths between any two points (e.g., vertiports or hubs) given constraints … A series of edges between two points (e.g., origin and destination) forms a flight path or route,” wherein, (Paragraph [0109], Lines 1-6) “Once the source network of paths or routes is created, the cost module 1210 assigns a cost for traversing each edge. Accordingly, the cost module 1210 accesses data sets for use in the cost determination from the datastores 1230. The data sets include one or more of historical aircraft track data, terrain altitude data, obstacle/building height.”
“the potential flight paths extending from a takeoff location to a destination location;”
Villa teaches, (Paragraph [0115], Lines 1-3) “Once the cost for traversal of each edge is assigned, the cost module 1210 determines a lowest or minimum cost path between each relevant origin and destination.”
Villa additionally teaches, (Paragraph [0080]) “The weather prediction module 725 may monitor ground effect and gusts at final approaches and take offs (FATOs) and touchdowns and liftoffs (TLOFs). Using sensors to characterize velocity and pressure fields close to the ground plane which can then be compared to predictive characterizations of the vertiport may help schedule vehicle takeoffs and landings.”
“receiving, by a predictive analysis model, the graph and environmental data; generating, by the predictive analysis model, predictions of future environmental conditions based on the graph and the environmental data;”
Villa teaches, (Paragraph [0047], Lines 1-5) “The parameter selection module 305 provides a user interface for defining various parameters to be used in the optimization of VTOL route selection … In one embodiment, the definable parameters include network and environmental parameters and objectives,” wherein, (Paragraph [0049], Lines 1-4) “The data processing module 310 accesses network and environmental data needed to calculate candidate routes for VTOL travel based on one or more selected parameters and/or objectives,” further wherein, (Paragraph [0056], Lines 5-14) “Each candidate route 400A, 400B, and 400C is calculated based on network and environmental parameters and objectives, such as the presence and location of other VTOL hubs, current locations of other VTOL aircraft 220, planned routes of other VTOL aircraft 220, predetermined acceptable noise levels and current and predicted weather between Hub A 405 and Hub B 410, and localized weather (e.g., sudden downbursts, localized hail, lightening, unsteady wind conditions) in the vicinity of the planned routes.”
“determining, by a cost function and heuristic estimate operator, costs associated with flight paths of the potential flight paths based on the future environmental conditions;”
Villa teaches, (Paragraph [0065], Lines 1-8) “At operation 550, the route selection module 320 determines a route for the VTOL aircraft 220 based on the vehicle noise profile and noise conditions of the map data. The route selection module 320 may determine a route cost for a number of predetermined candidate routes and select the candidate route with the lowest route cost. In one embodiment, route cost may be a function of distance, energy, cost, time, noise, observer annoyance, etc,” and that, (Paragraph [0114], Lines 1-9) “Using the accessed data, the cost engine 1210 aggregates or integrates relevant data sets for each node of an edge and assigns a scalar value of cost for each edge as it relates to each metric. Ideally, the cost engine optimizes by minimizing costs as a function of these different metrics. The cost engine 1210 determines a weighted cost for each edge by incorporating metrics and a weight vector. In example embodiments, the cost engine 1210 calculates the cost of traversing each node.”
“and identifying, by a predictive pathfinding model, a flight path of the potential paths based on the costs,”
Villa teaches, (Paragraph [0116], Lines 1-2) “The route optimization engine 1215 optimizes these lowest cost paths.”
“aircraft specific performance data,”
Villa teaches, (Paragraph [0064], Lines 1-13) “The data processing module 310 also accesses, in operation 540, vehicle noise profile data based on the vehicle data type of the VTOL aircraft 220 … Such data can correspond to the vehicle type, the state of the health and maintenance of the VTOL aircraft 220, and gross weight.”
“air traffic control constraints,”
Villa teaches, (Paragraph [0063], Lines 3-11) “Map data for a VTOL aircraft 220 may include data indicative of … air traffic, etc. Map data may also utilize previous weather, noise, and air traffic data for predictive purposes,” and that, (Paragraph [0104], Lines 18-24) “the optimized network improves integration with air traffic control (ATC) by providing insight into intentions of aircraft using the optimized network and allow the ATC to determine if these aircraft are not in conformance.”
“current environmental conditions,”
Villa teaches, (Paragraph [0063], Lines 6-8) “The map data may also be indicative of dynamic real-time information such as current weather in the area, localized weather.”
“and the future environmental conditions.”
Villa teaches, (Paragraph [0071], Lines 1-6) “FIG. 7 illustrates … a weather prediction module 725,” and additionally that, (Paragraph [0040], Lines 6-10) “The computer system of the VTOL aircraft 220 may also receive information, such as routing and weather information and information regarding the current location and planned routes of VTOL aircraft 220 in the vicinity of the VTOL aircraft 220.”
Claim 15 Discloses:
“A non-transitory machine-readable medium including instructions that, when executed by a machine, cause the machine to perform operations for flight plan optimization,”
Villa teaches, (Abstract, Lines 1-3) “generating an optimized network of flight paths and an operations volume around each of these flight paths,” wherein, (Paragraph [0022], Lines 1-4) “Example aspects of the present disclosure are directed to various systems, apparatuses, non -transitory computer-readable media, user interfaces, and electronic devices,” further wherein, (Paragraph [0060], Lines 12-15) “as discussed below with respect to FIG. 15, instructions 1524 stored in a memory such as 1504 or 1506 may configure one or more processors 1502 to perform one or more of the functions discussed below.”
“the operations comprising: generating, by a graph representation operator, a graph of potential flight paths and actual obstacles in the potential flight paths,”
Villa teaches, (Paragraph [0108]) “The network creation module 1205 manages the creation of a source network of flight paths or routes. The source network represents nearly a complete set of all possible flight paths between any two points (e.g., vertiports or hubs) given constraints … A series of edges between two points (e.g., origin and destination) forms a flight path or route,” wherein, (Paragraph [0109], Lines 1-6) “Once the source network of paths or routes is created, the cost module 1210 assigns a cost for traversing each edge. Accordingly, the cost module 1210 accesses data sets for use in the cost determination from the datastores 1230. The data sets include one or more of historical aircraft track data, terrain altitude data, obstacle/building height.”
“the potential flight paths extending from a takeoff location to a destination location;”
Villa teaches, (Paragraph [0115], Lines 1-3) “Once the cost for traversal of each edge is assigned, the cost module 1210 determines a lowest or minimum cost path between each relevant origin and destination.”
Villa additionally teaches, (Paragraph [0080]) “The weather prediction module 725 may monitor ground effect and gusts at final approaches and take offs (FATOs) and touchdowns and liftoffs (TLOFs). Using sensors to characterize velocity and pressure fields close to the ground plane which can then be compared to predictive characterizations of the vertiport may help schedule vehicle takeoffs and landings.”
“receiving, by a predictive analysis model, the graph and environmental data; generating, by the predictive analysis model, predictions of future environmental conditions based on the graph and the environmental data;”
Villa teaches, (Paragraph [0047], Lines 1-5) “The parameter selection module 305 provides a user interface for defining various parameters to be used in the optimization of VTOL route selection … In one embodiment, the definable parameters include network and environmental parameters and objectives,” wherein, (Paragraph [0049], Lines 1-4) “The data processing module 310 accesses network and environmental data needed to calculate candidate routes for VTOL travel based on one or more selected parameters and/or objectives,” further wherein, (Paragraph [0056], Lines 5-14) “Each candidate route 400A, 400B, and 400C is calculated based on network and environmental parameters and objectives, such as the presence and location of other VTOL hubs, current locations of other VTOL aircraft 220, planned routes of other VTOL aircraft 220, predetermined acceptable noise levels and current and predicted weather between Hub A 405 and Hub B 410, and localized weather (e.g., sudden downbursts, localized hail, lightening, unsteady wind conditions) in the vicinity of the planned routes.”
“determining, by a cost function and heuristic estimate operator, costs associated with flight paths of the potential flight paths based on the future environmental conditions;”
Villa teaches, (Paragraph [0065], Lines 1-8) “At operation 550, the route selection module 320 determines a route for the VTOL aircraft 220 based on the vehicle noise profile and noise conditions of the map data. The route selection module 320 may determine a route cost for a number of predetermined candidate routes and select the candidate route with the lowest route cost. In one embodiment, route cost may be a function of distance, energy, cost, time, noise, observer annoyance, etc,” and that, (Paragraph [0114], Lines 1-9) “Using the accessed data, the cost engine 1210 aggregates or integrates relevant data sets for each node of an edge and assigns a scalar value of cost for each edge as it relates to each metric. Ideally, the cost engine optimizes by minimizing costs as a function of these different metrics. The cost engine 1210 determines a weighted cost for each edge by incorporating metrics and a weight vector. In example embodiments, the cost engine 1210 calculates the cost of traversing each node.”
“and identifying, by a predictive pathfinding model, a flight path of the potential paths based on the costs,”
Villa teaches, (Paragraph [0116], Lines 1-2) “The route optimization engine 1215 optimizes these lowest cost paths.”
“aircraft specific performance data,”
Villa teaches, (Paragraph [0064], Lines 1-13) “The data processing module 310 also accesses, in operation 540, vehicle noise profile data based on the vehicle data type of the VTOL aircraft 220 … Such data can correspond to the vehicle type, the state of the health and maintenance of the VTOL aircraft 220, and gross weight.”
“air traffic control constraints,”
Villa teaches, (Paragraph [0063], Lines 3-11) “Map data for a VTOL aircraft 220 may include data indicative of … air traffic, etc. Map data may also utilize previous weather, noise, and air traffic data for predictive purposes,” and that, (Paragraph [0104], Lines 18-24) “the optimized network improves integration with air traffic control (ATC) by providing insight into intentions of aircraft using the optimized network and allow the ATC to determine if these aircraft are not in conformance.”
“current environmental conditions,”
Villa teaches, (Paragraph [0063], Lines 6-8) “The map data may also be indicative of dynamic real-time information such as current weather in the area, localized weather.”
“and the future environmental conditions.”
Villa teaches, (Paragraph [0071], Lines 1-6) “FIG. 7 illustrates … a weather prediction module 725,” and additionally that, (Paragraph [0040], Lines 6-10) “The computer system of the VTOL aircraft 220 may also receive information, such as routing and weather information and information regarding the current location and planned routes of VTOL aircraft 220 in the vicinity of the VTOL aircraft 220.”
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Villa in view of Martinez et al. (US 2018/0075758 A1, hereinafter Martinez)
Claim 2 Discloses:
“The flight plan optimization system of claim 1, wherein the costs include emissions and fuel efficiency.”
Villa does not explicitly teach the costs including emissions and fuel efficiency.
Villa does teach, (Paragraph [0069], Lines 3-8) “In one embodiment, the route cost is a function of the network and/or environmental factors, such as the distance of the route, the anticipated amount of energy required to transport the VTOL aircraft 220 along the route, the cost to transport the VTOL aircraft 220 along the route, and the like,” wherein, (Paragraph [0097], Lines 1-3) “the VTOL aircraft 220 may make adjustments to the way it is flying to reduce environmental impact.”
Martinez does teach the preceding limitations.
Martinez teaches, (Abstract, Lines 1-2) “for managing the revising of a flight plan of an aircraft,” wherein, (Paragraph [0016], Lines 1-4) “The invention also makes it possible to perform reliable and realistic comparisons between comparable flight plans. In effect, in one embodiment, each revision is scored according to several criteria (cost, punctuality, etc.),” and further wherein, (Paragraph [0053]) “The avionics parameters of the flight plans calculated by the FMS can comprise in particular fuel consumption predictions and times of passage at predefined flight plan points.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the system of Villa which is capable of measuring the anticipated amount of energy required for an aircraft to travel on a route, with the explicit methodology of predicting fuel consumption, a well-known form of aircraft energy consumption, as part of a flight route cost measurement as taught by Martinez, in order to yield predictable results.
Combining the references would yield the well-known benefits of measuring/predicting fuel consumption to achieve flight path outcomes desirable by a particular airline. As Martinez describes, (BACKGROUND, Paragraph [0004]) “airlines define a “company policy” as being the weighting of numerous criteria comprising in particular the operational cost of the flight, flight duration, reliability, safety, environment, customer satisfaction, personnel availability, maintenance or even the life of the aeroplane,” and further describes, (Paragraph [0059]) “the combination of revisions can optimize the observance of one or more predefined criteria comprising in particular the fuel cost … environmental criteria, the observance of company rules AOC and regulatory rules ATC.”
Claim 9 Discloses:
“The method of claim 8, wherein the costs include emissions and fuel efficiency.”
Villa does not explicitly teach the costs including emissions and fuel efficiency.
Villa does teach, (Paragraph [0069], Lines 3-8) “In one embodiment, the route cost is a function of the network and/or environmental factors, such as the distance of the route, the anticipated amount of energy required to transport the VTOL aircraft 220 along the route, the cost to transport the VTOL aircraft 220 along the route, and the like,” wherein, (Paragraph [0097], Lines 1-3) “the VTOL aircraft 220 may make adjustments to the way it is flying to reduce environmental impact.”
Martinez does teach the preceding limitations.
Martinez teaches, (Abstract, Lines 1-2) “for managing the revising of a flight plan of an aircraft,” wherein, (Paragraph [0016], Lines 1-4) “The invention also makes it possible to perform reliable and realistic comparisons between comparable flight plans. In effect, in one embodiment, each revision is scored according to several criteria (cost, punctuality, etc.),” and further wherein, (Paragraph [0053]) “The avionics parameters of the flight plans calculated by the FMS can comprise in particular fuel consumption predictions and times of passage at predefined flight plan points.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the system of Villa which is capable of measuring the anticipated amount of energy required for an aircraft to travel on a route, with the explicit methodology of predicting fuel consumption, a well-known form of aircraft energy consumption, as part of a flight route cost measurement as taught by Martinez, in order to yield predictable results.
Combining the references would yield the well-known benefits of measuring/predicting fuel consumption to achieve flight path outcomes desirable by a particular airline. As Martinez describes, (BACKGROUND, Paragraph [0004]) “airlines define a “company policy” as being the weighting of numerous criteria comprising in particular the operational cost of the flight, flight duration, reliability, safety, environment, customer satisfaction, personnel availability, maintenance or even the life of the aeroplane,” and further describes, (Paragraph [0059]) “the combination of revisions can optimize the observance of one or more predefined criteria comprising in particular the fuel cost … environmental criteria, the observance of company rules AOC and regulatory rules ATC.”
Claim 16 Discloses:
“The non-transitory machine-readable medium of claim 15, wherein the costs include emissions and fuel efficiency.”
Villa does teach, (Paragraph [0069], Lines 3-8) “In one embodiment, the route cost is a function of the network and/or environmental factors, such as the distance of the route, the anticipated amount of energy required to transport the VTOL aircraft 220 along the route, the cost to transport the VTOL aircraft 220 along the route, and the like,” wherein, (Paragraph [0097], Lines 1-3) “the VTOL aircraft 220 may make adjustments to the way it is flying to reduce environmental impact.”
Martinez teaches, (Abstract, Lines 1-2) “for managing the revising of a flight plan of an aircraft,” wherein, (Paragraph [0016], Lines 1-4) “The invention also makes it possible to perform reliable and realistic comparisons between comparable flight plans. In effect, in one embodiment, each revision is scored according to several criteria (cost, punctuality, etc.),” and further wherein, (Paragraph [0053]) “The avionics parameters of the flight plans calculated by the FMS can comprise in particular fuel consumption predictions and times of passage at predefined flight plan points.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the system of Villa which is capable of measuring the anticipated amount of energy required for an aircraft to travel on a route, with the explicit methodology of prediction fuel consumption, a well-known form of aircraft energy consumption, as part of a flight route cost measurement as taught by Martinez, in order to yield predictable results.
Combining the references would yield the well-known benefits of measuring/predicting fuel consumption to achieve flight path outcomes desirable by a particular airline. As Martinez describes, (BACKGROUND, Paragraph [0004]) “airlines define a “company policy” as being the weighting of numerous criteria comprising in particular the operational cost of the flight, flight duration, reliability, safety, environment, customer satisfaction, personnel availability, maintenance or even the life of the aeroplane,” and further describes, (Paragraph [0059]) “the combination of revisions can optimize the observance of one or more predefined criteria comprising in particular the fuel cost … environmental criteria, the observance of company rules AOC and regulatory rules ATC.”
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Villa in view of Wu et al. ( DTMamba: Dual Twin Mamba for Time Series Forecasting Zexue Wu, Aoqian Zhang, 11 May 2024, Pages 1-9, hereinafter Wu)
Claim 3 Discloses:
“The flight plan optimization system of claim 1, wherein the predictive analysis model is implemented as a Mamba model.”
Villa does not teach the predictive analysis model as being implemented as Mamba model.
However, Villa does teach, (Paragraph [0095], Lines 1-7) “a machine learning (ML) model is trained using a training set of noise data. The ML model is configured to generate predictive temporal data for noise signature mitigation. In other words, the ML model predicts future noise signatures based on current sensor data. This may be used to modify VTOL routing to reduce environmental noise impacts,” and that, (Paragraph [0065], Lines 8-12) “Due to the temporal nature of the data utilized for optimization, the route selection module 320 may use Kalman filtering and/or predictive neural nets to filter and weight inputs and constraints to the VTOL routing algorithm.”
It would have been obvious to a person of ordinary skill in the art to arrive at the preceding limitations in light of Wu.
Wu teaches, (Page1, Introduction, Paragraph 1, Lines 1-3 & 5-8) “Long-term time series forecasting (LTSF) is of paramount importance in various domains, enabling accurate predictions of future trends … Accurate time series forecasting significantly benefits various domains, including … weather forecasting [13],” and further teaches that, (Page 4, 4.1.1, Lines 1-2) “datasets widely used in the LTSF domain, including Weather, Traffic.”
Wu additionally teaches that, (Page 1, Introduction, Paragraph 3, Lines 1-8) “Recently, Mamba [7] has emerged as an innovative linear time series modeling approach that cleverly combines the characteristics of both Recurrent Neural Networks (RNN) [6] and Convolutional Neural Networks (CNN) [15], effectively addressing the computational efficiency challenges when dealing with long sequences. By leveraging the framework of State Space Models (SSM) [8], Mamba achieves a fusion of RNN’s sequential processing capability and CNN’s global information processing capability,” and further that, (Pag 1, Introduction, Paragraph 4, Lines 1-2) “Given the remarkable success of Mamba in sequence data, it is natural to consider applying Mamba to LTSF.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the predictive analysis model which incorporates environmental/weather data taught by Villa, with an explicit mamba model implementation, the rationale to do so as evidenced by Wu, in order to yield predictable results.
As Wu describes, (Page1, Introduction, Paragraph 1, Lines 1-3 & 5-8) “Long-term time series forecasting (LTSF) is of paramount importance in various domains, enabling accurate predictions of future trends … Accurate time series forecasting significantly benefits various domains, including … weather forecasting [13],” especially regarding, (Page 4, 4.1.1, Lines 1-2) “datasets widely used in the LTSF domain, including Weather, Traffic,” and further describes that, (Pag 1, Introduction, Paragraph 4, Lines 1-2) “Given the remarkable success of Mamba in sequence data, it is natural to consider applying Mamba to LTSF.”
Claim 10 Discloses:
“The method of claim 8, wherein the predictive analysis model is implemented as a Mamba model.”
Villa does not teach the predictive analysis model as being implemented as Mamba model.
However, Villa does teach, (Paragraph [0095], Lines 1-7) “a machine learning (ML) model is trained using a training set of noise data. The ML model is configured to generate predictive temporal data for noise signature mitigation. In other words, the ML model predicts future noise signatures based on current sensor data. This may be used to modify VTOL routing to reduce environmental noise impacts,” and that, (Paragraph [0065], Lines 8-12) “Due to the temporal nature of the data utilized for optimization, the route selection module 320 may use Kalman filtering and/or predictive neural nets to filter and weight inputs and constraints to the VTOL routing algorithm.”
It would have been obvious to a person of ordinary skill in the art to arrive at the preceding limitations in light of Wu.
Wu teaches, (Page1, Introduction, Paragraph 1, Lines 1-3 & 5-8) “Long-term time series forecasting (LTSF) is of paramount importance in various domains, enabling accurate predictions of future trends … Accurate time series forecasting significantly benefits various domains, including … weather forecasting [13],” and further teaches that, (Page 4, 4.1.1, Lines 1-2) “datasets widely used in the LTSF domain, including Weather, Traffic.”
Wu additionally teaches that, (Page 1, Introduction, Paragraph 3, Lines 1-8) “Recently, Mamba [7] has emerged as an innovative linear time series modeling approach that cleverly combines the characteristics of both Recurrent Neural Networks (RNN) [6] and Convolutional Neural Networks (CNN) [15], effectively addressing the computational efficiency challenges when dealing with long sequences. By leveraging the framework of State Space Models (SSM) [8], Mamba achieves a fusion of RNN’s sequential processing capability and CNN’s global information processing capability,” and further that, (Pag 1, Introduction, Paragraph 4, Lines 1-2) “Given the remarkable success of Mamba in sequence data, it is natural to consider applying Mamba to LTSF.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the predictive analysis model which incorporates environmental/weather data taught by Villa, with an explicit mamba model implementation, the rationale to do so as evidenced by Wu, in order to yield predictable results.
As Wu describes, (Page 1, Introduction, Paragraph 1, Lines 1-3 & 5-8) “Long-term time series forecasting (LTSF) is of paramount importance in various domains, enabling accurate predictions of future trends … Accurate time series forecasting significantly benefits various domains, including … weather forecasting [13],” especially regarding, (Page 4, 4.1.1, Lines 1-2) “datasets widely used in the LTSF domain, including Weather, Traffic,” and further describes that, (Pag 1, Introduction, Paragraph 4, Lines 1-2) “Given the remarkable success of Mamba in sequence data, it is natural to consider applying Mamba to LTSF.”
Claim 17 Discloses:
“The non-transitory machine-readable medium of claim 15, wherein the predictive analysis model is implemented as a Mamba model.”
Villa does not teach the predictive analysis model as being implemented as Mamba model.
However, Villa does teach, (Paragraph [0095], Lines 1-7) “a machine learning (ML) model is trained using a training set of noise data. The ML model is configured to generate predictive temporal data for noise signature mitigation. In other words, the ML model predicts future noise signatures based on current sensor data. This may be used to modify VTOL routing to reduce environmental noise impacts,” and that, (Paragraph [0065], Lines 8-12) “Due to the temporal nature of the data utilized for optimization, the route selection module 320 may use Kalman filtering and/or predictive neural nets to filter and weight inputs and constraints to the VTOL routing algorithm.”
It would have been obvious to a person of ordinary skill in the art to arrive at the preceding limitations in light of Wu.
Wu teaches, (Page1, Introduction, Paragraph 1, Lines 1-3 & 5-8) “Long-term time series forecasting (LTSF) is of paramount importance in various domains, enabling accurate predictions of future trends … Accurate time series forecasting significantly benefits various domains, including … weather forecasting [13],” and further teaches that, (Page 4, 4.1.1, Lines 1-2) “datasets widely used in the LTSF domain, including Weather, Traffic.”
Wu additionally teaches that, (Page 1, Introduction, Paragraph 3, Lines 1-8) “Recently, Mamba [7] has emerged as an innovative linear time series modeling approach that cleverly combines the characteristics of both Recurrent Neural Networks (RNN) [6] and Convolutional Neural Networks (CNN) [15], effectively addressing the computational efficiency challenges when dealing with long sequences. By leveraging the framework of State Space Models (SSM) [8], Mamba achieves a fusion of RNN’s sequential processing capability and CNN’s global information processing capability,” and further that, (Pag 1, Introduction, Paragraph 4, Lines 1-2) “Given the remarkable success of Mamba in sequence data, it is natural to consider applying Mamba to LTSF.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the predictive analysis model which incorporates environmental/weather data taught by Villa, with an explicit mamba model implementation, the rationale to do so as evidenced by Wu, in order to yield predictable results.
As Wu describes, (Page 1, Introduction, Paragraph 1, Lines 1-3 & 5-8) “Long-term time series forecasting (LTSF) is of paramount importance in various domains, enabling accurate predictions of future trends … Accurate time series forecasting significantly benefits various domains, including … weather forecasting [13],” especially regarding, (Page 4, 4.1.1, Lines 1-2) “datasets widely used in the LTSF domain, including Weather, Traffic,” and further describes that, (Pag 1, Introduction, Paragraph 4, Lines 1-2) “Given the remarkable success of Mamba in sequence data, it is natural to consider applying Mamba to LTSF.”
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Villa in view of Wu, further in view of Xi et al. (CN 117311384 A, hereinafter Xi)
Claim 4 Discloses:
“The flight plan optimization system of claim 3, wherein the predictive pathfinding model is implemented as a D* algorithm.”
Villa and Wu do not teach an explicit D* algorithm.
However, Villa teaches, (Paragraph [0115]) “Once the cost for traversal of each edge is assigned, the cost module 1210 determines a lowest or minimum cost path between each relevant origin and destination. In one embodiment, Dijkstra's algorithm is used to determine the minimum cost paths. However other algorithms that compute a minimum cost between any two points on a graph can be used.”
Xi does teach an explicit D* algorithm in the context of (Paragraph [n0001], Line 3) “generating UAV flight paths,” wherein, (Paragraph [n0002]) “it is necessary to plan a flight path for the drones that can avoid threats and obstacles, while meeting various constraints and taking into account actual environmental information.”
Xi teaches, (Paragraph [n0064], Lines 1-7) “Although there are many different algorithms for drone path planning, each algorithm has its own advantages, disadvantages, and applicable scenarios. The A* algorithm does not form local optima, is flexible and has many iterations, and is highly optimizable, but it has a large computational load and may sometimes cause excessive turns. The D* algorithm improves the efficiency of quadratic path planning, but in complex environments, the search space of the algorithm will increase significantly, thus reducing the efficiency of path planning and increasing the time cost of the algorithm.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the flight plan optimization system of Villa with the explicit D* algorithm as taught by Xi, in order to yield predictable results.
As Xi describes, (Paragraph [n0064], Lines 1-7) “there are many different algorithms for drone path planning, each algorithm has its own advantages, disadvantages, and applicable scenarios … The D* algorithm improves the efficiency of quadratic path planning.”
Claim 11 Discloses:
“The method of claim 10, wherein the predictive pathfinding model is implemented as a D* algorithm.”
Villa and Wu do not teach an explicit D* algorithm.
However, Villa teaches, (Paragraph [0115]) “Once the cost for traversal of each edge is assigned, the cost module 1210 determines a lowest or minimum cost path between each relevant origin and destination. In one embodiment, Dijkstra's algorithm is used to determine the minimum cost paths. However other algorithms that compute a minimum cost between any two points on a graph can be used.”
Xi does teach an explicit D* algorithm in the context of (Paragraph [n0001], Line 3) “generating UAV flight paths,” wherein, (Paragraph [n0002]) “it is necessary to plan a flight path for the drones that can avoid threats and obstacles, while meeting various constraints and taking into account actual environmental information.”
Xi teaches, (Paragraph [n0064], Lines 1-7) “Although there are many different algorithms for drone path planning, each algorithm has its own advantages, disadvantages, and applicable scenarios. The A* algorithm does not form local optima, is flexible and has many iterations, and is highly optimizable, but it has a large computational load and may sometimes cause excessive turns. The D* algorithm improves the efficiency of quadratic path planning, but in complex environments, the search space of the algorithm will increase significantly, thus reducing the efficiency of path planning and increasing the time cost of the algorithm.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the flight plan optimization system of Villa with the explicit D* algorithm as taught by Xi, in order to yield predictable results.
As Xi describes, (Paragraph [n0064], Lines 1-7) “there are many different algorithms for drone path planning, each algorithm has its own advantages, disadvantages, and applicable scenarios … The D* algorithm improves the efficiency of quadratic path planning.”
Claim 18 Discloses:
“The non-transitory machine-readable medium of claim 17, wherein the predictive pathfinding model is implemented as a D* algorithm.”
Villa and Wu do not teach an explicit D* algorithm.
However, Villa teaches, (Paragraph [0115]) “Once the cost for traversal of each edge is assigned, the cost module 1210 determines a lowest or minimum cost path between each relevant origin and destination. In one embodiment, Dijkstra's algorithm is used to determine the minimum cost paths. However other algorithms that compute a minimum cost between any two points on a graph can be used.”
Xi does teach an explicit D* algorithm in the context of (Paragraph [n0001], Line 3) “generating UAV flight paths,” wherein, (Paragraph [n0002]) “it is necessary to plan a flight path for the drones that can avoid threats and obstacles, while meeting various constraints and taking into account actual environmental information.”
Xi teaches, (Paragraph [n0064], Lines 1-7) “Although there are many different algorithms for drone path planning, each algorithm has its own advantages, disadvantages, and applicable scenarios. The A* algorithm does not form local optima, is flexible and has many iterations, and is highly optimizable, but it has a large computational load and may sometimes cause excessive turns. The D* algorithm improves the efficiency of quadratic path planning, but in complex environments, the search space of the algorithm will increase significantly, thus reducing the efficiency of path planning and increasing the time cost of the algorithm.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the flight plan optimization system of Villa with the explicit D* algorithm as taught by Xi, in order to yield predictable results.
As Xi describes, (Paragraph [n0064], Lines 1-7) “there are many different algorithms for drone path planning, each algorithm has its own advantages, disadvantages, and applicable scenarios … The D* algorithm improves the efficiency of quadratic path planning.”
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Villa in view of Chambers et al. (US 2016/0253908 A1, hereinafter Chambers)
Claim 5 Discloses:
“The flight plan optimization system of claim 1, further comprising a graph update operator configured to generate updates to the graph based on the graph and the future environmental conditions from the predictive analysis model and provide the updates to the graph to the graph representation operator and the cost function and heuristic estimate operator.”
Villa does not explicitly teach the preceding limitations.
However, Villa does teach, (Paragraph [0095], Lines 10-13) “The ML model may be part of the data processing module 310, with updated routing information being provided to the VTOL aircraft 220 by the route selection module 320,” wherein, (Paragraph [0115], Lines 5-7) “algorithms that compute a minimum cost between any two points on a graph can be used.”
Chambers does explicitly teach the preceding limitations, in the context of an, (Abstract, Lines 6-11) “Unmanned Aerial System [which] is configured to provide real-time information about the flight route to the Unmanned Aerial Vehicle during its flight, and the Unmanned Aerial Vehicle is configured to dynamically update its mission based on information received from the Unmanned Aerial System,” wherein, (Paragraph [0085], Lines 1-3) “The mission manager 305 utilizes the other components of the distribution center management system 304 to monitor the status of the local environment,” and further wherein, (Paragraph [0060], Lines 1-4) “The mission planner 200 will determine 254 whether a given piece of data received by the UAV 102 constitutes a local skymap update by applying rules and heuristics to the received information.”
Chambers teaches, (Paragraph [0053]) “The mission planner 200 may modify the dynamic route during the mission as the flight corridor updates are received. For example, in some embodiments, the mission planner 200 may alter the dynamic route to avoid flight hazards such as inclement weather, aircraft trespassing into a flight corridor, etc. When the route is modified, the mission planner 200 will re-determine the sequence of flight corridors that will be traversed to reach the goal location.”
Chambers additionally teaches, (Paragraphs [0062-0063]) “As long as the mission planner 200 determines 254 that no data requiring an update to the local skymap has been received, the UAV 102 continues to fly on the lowest cost route that has already been determined 252. However, if a local skymap update has been received, then the mission planner 200 will update 255 the traversal cost for each affected flight corridor in the local skymap. The mission planner 200 will then re-determine 252 the lowest cost route to the goal location based on the updated traversal costs of the flight corridors in the local skymap.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the cost function and heuristic estimate operator capable to predicting aircraft pathing as taught by Villa, with the explicit graph update operator as taught by Chambers, in order to yield predictable results.
Combining the reference would yield the benefits of being able to adapt a graph to real-time information that affects the output of the cost function. As Chambers describes, (Paragraph [0063]) “if a local skymap update has been received, then the mission planner 200 will update 255 the traversal cost for each affected flight corridor in the local skymap. The mission planner 200 will then re-determine 252 the lowest cost route to the goal location based on the updated traversal costs of the flight corridors in the local skymap.”
Claim 12 Discloses:
“The method of claim 8, further comprising: generating, by a graph update operator, updates to the graph based on the graph and the future environmental conditions from the predictive analysis model; and providing, by the graph update operator, the updates to the graph to the graph representation operator and the cost function and heuristic estimate operator.”
Villa does not explicitly teach the preceding limitations.
However, Villa does teach, (Paragraph [0095], Lines 10-13) “The ML model may be part of the data processing module 310, with updated routing information being provided to the VTOL aircraft 220 by the route selection module 320,” wherein, (Paragraph [0115], Lines 5-7) “algorithms that compute a minimum cost between any two points on a graph can be used.”
Chambers does explicitly teach the preceding limitations, in the context of an, (Abstract, Lines 6-11) “Unmanned Aerial System [which] is configured to provide real-time information about the flight route to the Unmanned Aerial Vehicle during its flight, and the Unmanned Aerial Vehicle is configured to dynamically update its mission based on information received from the Unmanned Aerial System,” wherein, (Paragraph [0085], Lines 1-3) “The mission manager 305 utilizes the other components of the distribution center management system 304 to monitor the status of the local environment,” and further wherein, (Paragraph [0060], Lines 1-4) “The mission planner 200 will determine 254 whether a given piece of data received by the UAV 102 constitutes a local skymap update by applying rules and heuristics to the received information.”
Chambers teaches, (Paragraph [0053]) “The mission planner 200 may modify the dynamic route during the mission as the flight corridor updates are received. For example, in some embodiments, the mission planner 200 may alter the dynamic route to avoid flight hazards such as inclement weather, aircraft trespassing into a flight corridor, etc. When the route is modified, the mission planner 200 will re-determine the sequence of flight corridors that will be traversed to reach the goal location.”
Chambers additionally teaches, (Paragraphs [0062-0063]) “As long as the mission planner 200 determines 254 that no data requiring an update to the local skymap has been received, the UAV 102 continues to fly on the lowest cost route that has already been determined 252. However, if a local skymap update has been received, then the mission planner 200 will update 255 the traversal cost for each affected flight corridor in the local skymap. The mission planner 200 will then re-determine 252 the lowest cost route to the goal location based on the updated traversal costs of the flight corridors in the local skymap.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the cost function and heuristic estimate operator capable to predicting aircraft pathing as taught by Villa, with the explicit graph update operator as taught by Chambers, in order to yield predictable results.
Combining the reference would yield the benefits of being able to adapt a graph to real-time information that affects the output of the cost function. As Chambers describes, (Paragraph [0063]) “if a local skymap update has been received, then the mission planner 200 will update 255 the traversal cost for each affected flight corridor in the local skymap. The mission planner 200 will then re-determine 252 the lowest cost route to the goal location based on the updated traversal costs of the flight corridors in the local skymap.”
Claim 19 Discloses:
“The non-transitory machine-readable medium of claim 15, wherein the operations further comprise: generating, by a graph update operator, updates to the graph based on the graph and the future environmental conditions from the predictive analysis model; and providing, by the graph update operator, the updates to the graph to the graph representation operator and the cost function and heuristic estimate operator.”
Villa does not explicitly teach the preceding limitations.
However, Villa does teach, (Paragraph [0095], Lines 10-13) “The ML model may be part of the data processing module 310, with updated routing information being provided to the VTOL aircraft 220 by the route selection module 320,” wherein, (Paragraph [0115], Lines 5-7) “algorithms that compute a minimum cost between any two points on a graph can be used.”
Chambers does explicitly teach the preceding limitations, in the context of an, (Abstract, Lines 6-11) “Unmanned Aerial System [which] is configured to provide real-time information about the flight route to the Unmanned Aerial Vehicle during its flight, and the Unmanned Aerial Vehicle is configured to dynamically update its mission based on information received from the Unmanned Aerial System,” wherein, (Paragraph [0085], Lines 1-3) “The mission manager 305 utilizes the other components of the distribution center management system 304 to monitor the status of the local environment,” and further wherein, (Paragraph [0060], Lines 1-4) “The mission planner 200 will determine 254 whether a given piece of data received by the UAV 102 constitutes a local skymap update by applying rules and heuristics to the received information.”
Chambers teaches, (Paragraph [0053]) “The mission planner 200 may modify the dynamic route during the mission as the flight corridor updates are received. For example, in some embodiments, the mission planner 200 may alter the dynamic route to avoid flight hazards such as inclement weather, aircraft trespassing into a flight corridor, etc. When the route is modified, the mission planner 200 will re-determine the sequence of flight corridors that will be traversed to reach the goal location.”
Chambers additionally teaches, (Paragraphs [0062-0063]) “As long as the mission planner 200 determines 254 that no data requiring an update to the local skymap has been received, the UAV 102 continues to fly on the lowest cost route that has already been determined 252. However, if a local skymap update has been received, then the mission planner 200 will update 255 the traversal cost for each affected flight corridor in the local skymap. The mission planner 200 will then re-determine 252 the lowest cost route to the goal location based on the updated traversal costs of the flight corridors in the local skymap.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the cost function and heuristic estimate operator capable to predicting aircraft pathing as taught by Villa, with the explicit graph update operator as taught by Chambers, in order to yield predictable results.
Combining the reference would yield the benefits of being able to adapt a graph to real-time information that affects the output of the cost function. As Chambers describes, (Paragraph [0063]) “if a local skymap update has been received, then the mission planner 200 will update 255 the traversal cost for each affected flight corridor in the local skymap. The mission planner 200 will then re-determine 252 the lowest cost route to the goal location based on the updated traversal costs of the flight corridors in the local skymap.”
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Villa in view of Schwartz et al. (US 2023/0386346 A1, hereinafter Schwartz)
Claim 6 Discloses:
“The flight plan optimization system of claim 1, further comprising a feedback operator configured to provide the identified flight path to the predictive analysis model.”
Villa does not explicitly teach a feedback operator.
However, Villa does teach, (Paragraph [0109], Lines 3-6) “the cost module 1210 accesses data sets for use in the cost determination from the datastores 1230. The data sets include one or more of historical aircraft track data.”
Schwartz does teach using a feedback operator.
Schwartz teaches, (Abstract) “A system and a method include a rerouting control unit configured to generate one or more reroute options for an aircraft based on an analysis of a current position of the aircraft, a predicted future position of the aircraft, a current position of an in-flight hazard, a predicted future position of the in-flight hazard, and one or both of: (i) a flight path of one or more other aircraft within an airspace, or (ii) one or both of a minimum amount of fuel of the aircraft or a minimum weight of the aircraft at a destination location,” wherein, (Paragraph [0083], Lines “The systems may be trained and re-trained using feedback from one or more prior analyses of flight paths, reroute options, and/or other such data. Based on this feedback, the systems may be trained by adjusting one or more parameters, weights, rules, criteria, or the like, used in the analysis of the same.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the predictive analysis model as taught by Villa with an explicit feedback operator as taught by Schwartz, in order to yield predictable results.
Combining the references would yield the well-known benefits of feedback operators using the output of an initial path iteration to assist with generating subsequent path iterations. As Schwartz describes, (Paragraph [0083], Lines 28-35) “The training of the record matching system minimizes conflicts and interference with other flight paths by performing an iterative training algorithm, in which the systems are retrained with an updated set of data and based on the feedback examined prior to the most recent training of the systems. This provides a robust analysis model that can better determine whether reroute options are viable, accurate, efficient, and the like.”
Claim 13 Discloses:
“The method of claim 8, further comprising providing, by a feedback operator, the identified flight path to the predictive analysis model.”
Villa does not explicitly teach a feedback operator.
However, Villa does teach, (Paragraph [0109], Lines 3-6) “the cost module 1210 accesses data sets for use in the cost determination from the datastores 1230. The data sets include one or more of historical aircraft track data.”
Schwartz does teach using a feedback operator.
Schwartz teaches, (Abstract) “A system and a method include a rerouting control unit configured to generate one or more reroute options for an aircraft based on an analysis of a current position of the aircraft, a predicted future position of the aircraft, a current position of an in-flight hazard, a predicted future position of the in-flight hazard, and one or both of: (i) a flight path of one or more other aircraft within an airspace, or (ii) one or both of a minimum amount of fuel of the aircraft or a minimum weight of the aircraft at a destination location,” wherein, (Paragraph [0083], Lines “The systems may be trained and re-trained using feedback from one or more prior analyses of flight paths, reroute options, and/or other such data. Based on this feedback, the systems may be trained by adjusting one or more parameters, weights, rules, criteria, or the like, used in the analysis of the same.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the predictive analysis model as taught by Villa with an explicit feedback operator as taught by Schwartz, in order to yield predictable results.
Combining the references would yield the well-known benefits of feedback operators using the output of an initial path iteration to assist with generating subsequent path iterations. As Schwartz describes, (Paragraph [0083], Lines 28-35) “The training of the record matching system minimizes conflicts and interference with other flight paths by performing an iterative training algorithm, in which the systems are retrained with an updated set of data and based on the feedback examined prior to the most recent training of the systems. This provides a robust analysis model that can better determine whether reroute options are viable, accurate, efficient, and the like.”
Claim 20 Discloses:
“The non-transitory machine-readable medium of claim 15, wherein the operations further comprise providing, by a feedback operator, the identified flight path to the predictive analysis model.”
Villa does not explicitly teach a feedback operator.
However, Villa does teach, (Paragraph [0109], Lines 3-6) “the cost module 1210 accesses data sets for use in the cost determination from the datastores 1230. The data sets include one or more of historical aircraft track data.”
Schwartz does teach using a feedback operator.
Schwartz teaches, (Abstract) “A system and a method include a rerouting control unit configured to generate one or more reroute options for an aircraft based on an analysis of a current position of the aircraft, a predicted future position of the aircraft, a current position of an in-flight hazard, a predicted future position of the in-flight hazard, and one or both of: (i) a flight path of one or more other aircraft within an airspace, or (ii) one or both of a minimum amount of fuel of the aircraft or a minimum weight of the aircraft at a destination location,” wherein, (Paragraph [0083], Lines “The systems may be trained and re-trained using feedback from one or more prior analyses of flight paths, reroute options, and/or other such data. Based on this feedback, the systems may be trained by adjusting one or more parameters, weights, rules, criteria, or the like, used in the analysis of the same.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the predictive analysis model as taught by Villa with an explicit feedback operator as taught by Schwartz, in order to yield predictable results.
Combining the references would yield the well-known benefits of feedback operators using the output of an initial path iteration to assist with generating subsequent path iterations. As Schwartz describes, (Paragraph [0083], Lines 28-35) “The training of the record matching system minimizes conflicts and interference with other flight paths by performing an iterative training algorithm, in which the systems are retrained with an updated set of data and based on the feedback examined prior to the most recent training of the systems. This provides a robust analysis model that can better determine whether reroute options are viable, accurate, efficient, and the like.”
Claims 7, 14, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Villa in view of Shamasundar et al. (“System and Method for Flight Efficiency Real-time Prediction for departure/arrival (SID/STAR) procedure”, hereinafter Shamasundar)
Claim 7 Discloses:
“The flight plan optimization system of claim 1, further comprising a reactive standard instrument departures (SIDS)/standard instrument arrivals (STARS) operator configured to adjust SIDS and STARS data based on ATC data and the future environmental data and provide the adjusted SIDS and STARS data to the predictive analysis model.”
Villa does not explicitly teach a reactive standard instrument departures (SIDS)/standard instrument arrivals (STARS) operator.
Villa does teach that, (Paragraph [0063], Lines 3-11) “Map data for a VTOL aircraft 220 may include data indicative of … air traffic, etc. Map data may also utilize previous weather, noise, and air traffic data for predictive purposes,” and that, (Paragraph [0104], Lines 18-24) “the optimized network improves integration with air traffic control (ATC) by providing insight into intentions of aircraft using the optimized network and allow the ATC to determine if these aircraft are not in conformance,” and further that, (Paragraph [0063], Lines 6-8) “The map data may also be indicative of dynamic real-time information such as current weather in the area, localized weather.”
Villa additionally does teach that, (Paragraph [0071], Lines 1-6) “FIG. 7 illustrates … a weather prediction module 725,” and additionally that, (Paragraph [0040], Lines 6-10) “The computer system of the VTOL aircraft 220 may also receive information, such as routing and weather information and information regarding the current location and planned routes of VTOL aircraft 220 in the vicinity of the VTOL aircraft 220.”
However, it would have been obvious to a person of ordinary skill in the art to arrive at the preceding limitations in light of Shamasundar.
Shamasundar teaches, (2. Proposed Solution of the Problem) “a flight efficiency real-time prediction tool to improve the prediction of flight efficiency for departure/arrival (SID/STAR) procedure in real-time. The proposed system offers more reliable real-time predictive runway/departure/arrival procedures by evaluating the various departure and arrival procedures identified by the flight efficiency prediction tool in real time against the current aircraft state, aircraft performance, mission and external conditions like weather, Airport traffic, Notice to Airmen, Temporary flight restriction with historical database. This tool can predict the opportunity for SID/STAR with fuel points against the dynamic real time condition.”
Shamasundar additionally teaches, (Page 5, Lines 6-10) “The Fuel Efficiency Service provides the historical data of the current fleet based on the region and operating environment. It provides best fuel efficient SID/STAR against the ACTIVE SID with fuel points along with cost benefits dashboards. Here, the aircraft operators have a chance to review and negotiate with ATC. It also provides the SID/STAR graph based on persona difference analytics.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the predictive analysis model as taught by Villa, with a reactive standard instrument departures (SIDS)/standard instrument arrivals (STARS) operator as taught by Shamasundar, in order to yield predictable results.
Combining Villa with capability to incorporate SIDS/STARS parameters, would yield the benefits of complying with SID/STARs procedures based upon predicted paths and corresponding environmental factors. As Shamasundar describes that the methodology may, (Advantages of the Proposed System, Lines 1-5) “Optimize fuel efficiency for runway, departure, arrival procedure in real-time that unlocks savings beyond standard efficiency initiatives … Provides real-time evaluation of fuel required, time and distance for every departure/arrival procedure … Provides real-time advisories to choose best departure/arrival (SID/STAR) procedure.”
Claim 14 Discloses:
“The method of claim 8, further comprising adjusting, by a reactive standard instrument departures (SIDS)/standard instrument arrivals (STARS) operator, SIDS and STARS data based on ATC data and the future environmental data and provide the adjusted SIDS and STARS data to the predictive analysis model.”
Villa does not explicitly teach a reactive standard instrument departures (SIDS)/standard instrument arrivals (STARS) operator.
Villa does teach that, (Paragraph [0063], Lines 3-11) “Map data for a VTOL aircraft 220 may include data indicative of … air traffic, etc. Map data may also utilize previous weather, noise, and air traffic data for predictive purposes,” and that, (Paragraph [0104], Lines 18-24) “the optimized network improves integration with air traffic control (ATC) by providing insight into intentions of aircraft using the optimized network and allow the ATC to determine if these aircraft are not in conformance,” and further that, (Paragraph [0063], Lines 6-8) “The map data may also be indicative of dynamic real-time information such as current weather in the area, localized weather.”
Villa additionally does teach that, (Paragraph [0071], Lines 1-6) “FIG. 7 illustrates … a weather prediction module 725,” and additionally that, (Paragraph [0040], Lines 6-10) “The computer system of the VTOL aircraft 220 may also receive information, such as routing and weather information and information regarding the current location and planned routes of VTOL aircraft 220 in the vicinity of the VTOL aircraft 220.”
However, it would have been obvious to a person of ordinary skill in the art to arrive at the preceding limitations in light of Shamasundar.
Shamasundar teaches, (2. Proposed Solution of the Problem) “a flight efficiency real-time prediction tool to improve the prediction of flight efficiency for departure/arrival (SID/STAR) procedure in real-time. The proposed system offers more reliable real-time predictive runway/departure/arrival procedures by evaluating the various departure and arrival procedures identified by the flight efficiency prediction tool in real time against the current aircraft state, aircraft performance, mission and external conditions like weather, Airport traffic, Notice to Airmen, Temporary flight restriction with historical database. This tool can predict the opportunity for SID/STAR with fuel points against the dynamic real time condition.”
Shamasundar additionally teaches, (Page 5, Lines 6-10) “The Fuel Efficiency Service provides the historical data of the current fleet based on the region and operating environment. It provides best fuel efficient SID/STAR against the ACTIVE SID with fuel points along with cost benefits dashboards. Here, the aircraft operators have a chance to review and negotiate with ATC. It also provides the SID/STAR graph based on persona difference analytics.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the predictive analysis model as taught by Villa, with a reactive standard instrument departures (SIDS)/standard instrument arrivals (STARS) operator as taught by Shamasundar, in order to yield predictable results.
Combining Villa with capability to incorporate SIDS/STARS parameters, would yield the benefits of complying with SID/STARs procedures based upon predicted paths and corresponding environmental factors. As Shamasundar describes that the methodology may, (Advantages of the Proposed System, Lines 1-5) “Optimize fuel efficiency for runway, departure, arrival procedure in real-time that unlocks savings beyond standard efficiency initiatives … Provides real-time evaluation of fuel required, time and distance for every departure/arrival procedure … Provides real-time advisories to choose best departure/arrival (SID/STAR) procedure.”
Claim 21 Discloses:
“The non-transitory machine-readable medium of claim 15, wherein the operations further comprise adjusting, by a reactive standard instrument departures (SIDS)/standard instrument arrivals (STARS) operator, SIDS and STARS data based on ATC data and the future environmental data and provide the adjusted SIDS and STARS data to the predictive analysis model.”
Villa does not explicitly teach a reactive standard instrument departures (SIDS)/standard instrument arrivals (STARS) operator.
Villa does teach that, (Paragraph [0063], Lines 3-11) “Map data for a VTOL aircraft 220 may include data indicative of … air traffic, etc. Map data may also utilize previous weather, noise, and air traffic data for predictive purposes,” and that, (Paragraph [0104], Lines 18-24) “the optimized network improves integration with air traffic control (ATC) by providing insight into intentions of aircraft using the optimized network and allow the ATC to determine if these aircraft are not in conformance,” and further that, (Paragraph [0063], Lines 6-8) “The map data may also be indicative of dynamic real-time information such as current weather in the area, localized weather.”
However, it would have been obvious to a person of ordinary skill in the art to arrive at the preceding limitations in light of Shamasundar.
Shamasundar teaches, (2. Proposed Solution of the Problem) “a flight efficiency real-time prediction tool to improve the prediction of flight efficiency for departure/arrival (SID/STAR) procedure in real-time. The proposed system offers more reliable real-time predictive runway/departure/arrival procedures by evaluating the various departure and arrival procedures identified by the flight efficiency prediction tool in real time against the current aircraft state, aircraft performance, mission and external conditions like weather, Airport traffic, Notice to Airmen, Temporary flight restriction with historical database. This tool can predict the opportunity for SID/STAR with fuel points against the dynamic real time condition.”
Shamasundar additionally teaches, (Page 5, Lines 6-10) “The Fuel Efficiency Service provides the historical data of the current fleet based on the region and operating environment. It provides best fuel efficient SID/STAR against the ACTIVE SID with fuel points along with cost benefits dashboards. Here, the aircraft operators have a chance to review and negotiate with ATC. It also provides the SID/STAR graph based on persona difference analytics.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to combine the predictive analysis model as taught by Villa, with a reactive standard instrument departures (SIDS)/standard instrument arrivals (STARS) operator as taught by Shamasundar, in order to yield predictable results.
Combining Villa with capability to incorporate SIDS/STARS parameters, would yield the benefits of complying with SID/STARs procedures based upon predicted paths and corresponding environmental factors. As Shamasundar describes that the methodology may, (Advantages of the Proposed System, Lines 1-5) “Optimize fuel efficiency for runway, departure, arrival procedure in real-time that unlocks savings beyond standard efficiency initiatives … Provides real-time evaluation of fuel required, time and distance for every departure/arrival procedure … Provides real-time advisories to choose best departure/arrival (SID/STAR) procedure.”
Conclusion
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/ALEXANDER V GENTILE/Examiner, Art Unit 3664
/KITO R ROBINSON/Supervisory Patent Examiner, Art Unit 3664
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