DETAILED ACTION
Status of Claims
The Response filed 07/22/2026 has been acknowledged. Claims 1-2, 7-8, 10-15, 17-18, 20 have been amended. Claim 16 is cancelled. Claim 21 is added. Claims 1-15, 17-21 are currently pending and have been examined.
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 .
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 8-12, 15, 17-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fox et al. (US 20210108937 A1) (hereafter Fox), in view of Russo et al. (US 20250238813 A1) (hereafter Russo), in view of Swann et al. (US 20230323820 A1) (hereafter Swann).
As per claim 1:
A method for reducing greenhouse emission impact, comprising:
obtaining travel pathway data for a vehicle, the travel pathway data comprising current environmental data and historical environmental data in accordance with at least one portion of a travel pathway; (See Fox ¶0051, “The planning request, sometimes referred to as a trip request, may be a request for a single trip and/or a plurality of trips. The planning request may include preferences for modalities, emissions models for the various modalities, route information, and budget information. For example, for a robotaxi, the planning request may include an origin and destination for the trip. The car used by the robotaxi may be one of connected cars 206 and thus one of a fleet of vehicles. The planning request may also be for a plurality of trips. For example, the planning request may be for a plurality of delivery vehicles that may have respective routes to deliver packages. As another example, the planning request may be for a plurality of buses, trains, or other public transit-on-demand modalities that may have respective routes to transport passengers.” Fox discloses the concept of obtaining travel pathway data. See also Fox ¶0025, “These models may include not only vehicle data, but may also include environmental data such as temperature, rainfall, etc. The environmental data may be past recorded data and/or forecasts for expected environmental conditions the vehicles may encounter during operations. Switching their propulsion methods may be dependent on locations of charge points for EVs, the actual trip planned by the passenger, and battery state of charge.” Fox further discloses the concept of collecting environmental data including historical and present/forecasted information.)
generating, via a generative artificial intelligence (AI) model, a fueling plan for the vehicle along the at least one portion of the travel pathway based at least in part on the current environmental data and the historical environmental data, wherein: (See Fox ¶0015, “As disclosed herein, recharging and refueling planning may select and account for the emissions of the energy medium (e.g., coal, wind, solar, etc.) used to create the electricity used to charge an EV. This data may be used to simulate the emissions while driving either with “dirty” sources, such as coal, or “clean” sources, such as solar or wind.” Fox discloses the concept of generating a fueling plan.)
a reward function of the generative Al model is based at least in part on a negative summation of at least one greenhouse emission estimate for the vehicle along the at least one portion of the travel pathway and at least one fuel availability parameter; and (See also Fox ¶0049, “Budget predictions may also include utilizing optimization techniques to minimize emissions. For example, during stage 312 Monte Carlo simulations and other optimization techniques may be used to minimize emissions. For example, the various trips connected vehicles 206 may be planned to take may be simulated using different combinations of vehicles, routes, modalities, etc. to determine a plurality of trip plans and vehicle assignments that result in reduced emissions.” Fox discloses optimizing the reduction of greenhouse gases.)
executing the fueling plan to control fuel utilized by the vehicle during navigation along the travel pathway, (See Fox ¶0056,” Outputting the trip plan may include providing a listing of the trip plan including directions for a driver. The outputted trip plan may also define operating characteristics of the trip plan such as speed. Outputting the trip plan may also include transmitting an activation signal to an autonomous vehicle. For example, if the trip plan is for a robotaxi to be conducted by an autonomous vehicle, the activation signal may be transmitted to the autonomous vehicle. Upon receiving the activation signal, a controller of the autonomous vehicle may cause the autonomous vehicle to perform the trip.” Fox discloses the concept of outputting the optimized plan including executing the plan.)
Although Fox discloses the above-enclosed invention including utilizing machine learning in the analysis and prediction of emissions, Fox fails to explicitly disclose utilizing generative artificial intelligence to generate the plan.
However Russo as shown, which talks about carbon emission optimization, teaches the concept of utilizing generative artificial intelligence to generate a plan.
generating, via a generative artificial intelligence (AI) model, a fueling plan for the vehicle along the at least one portion of the travel pathway based at least in part on the current environmental data and the historical environmental data, wherein: (See Russo ¶0065, “With continued reference to FIG. 1, one or more generative machine learning models may utilize one or more predefined templates representing, for example, and without limitation, standard transportation routes, common transportation modes, historical transportation patterns, optimal load distributions, and/or the like. In a non-limiting example, one or more transportation plan templates may outline a standard route for shipping goods from a warehouse in City A to retail outlet in City B using road transportation, such transportation plan templates may detail preferred highway routes, rest stops, refueling points, and event time-of-day recommendations for avoiding traffic. In some cases, transportation plan templates may serve as benchmarks for comparing and evaluating the efficiency, cost-effectiveness, and carbon emission implication or alternative routes or modes proposed by one or more generative machine learning models.” Russo teaches the concept of utilizing generative AI to generate a travel/fueling plan.)
a reward function of the generative Al model is based at least in part on a negative summation of at least one greenhouse emission estimate for the vehicle along the at least one portion of the travel pathway and at least one fuel availability parameter; and (See Russo ¶0054, “Still referring to FIG. 1, in some cases, at least one transportation plan 144 may be formulated, by processor 104, through analyzing historical data (e.g., historical carbon emissions), current conditions (e.g., weather patterns, traffic congestion, fuel availability and the like), and predictive models (e.g., future demand forecasts, anticipated environmental changes, and the like).” Russo teaches accounting for fuel availability. See also Russo ¶0055, “ Generating at least one transportation plan 144 may include generating at least one transportation plan 144 using trained transportation plan generation model as a function of integrated logistics data collection 112. In a case where more than one transportation plans are generated, processor 104 may be configured to select at least one transportation plan 144 from multiple transportation plans based on one or more criterion such as minimizing projected carbon emission, optimizing delivery times, or balancing between the two. In a non-limiting example, if a first and a second transportation plans offer similar delivery times, but the first transportation plan has a significantly lower projected carbon emission, processor 104 may prioritize first transportation plan over the second transportation plan. Conversely, if faster delivery time weight more, processor 104 may opt for a transportation plan that includes transportation vehicles having highest speed event if it may have a slightly higher projected carbon emission.” Russo teaches optimizing/minimizing greenhouse emissions in the planning.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Russo with the invention of Fox. As shown, Fox discloses the concept of managing vehicle emissions for planned trips including utilizing machine learning to perform analysis of emission information and projecting emissions for each trips. Russo further teaches the concept of utilizing generative AI to generate the plans. Russo further teaches this concept to allow for continuous and multivariable considerations in the generation of plans to allow for the testing of different plans, accounting for different variables, and optimizing for different requirements (See Russo ¶0057-¶0058). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Russo to further generate optimized plans.
Although Fox and Russo discloses the above-enclosed invention, the combination fails to explicitly disclose executing the fuel to plan to include utilizing different fuel types during the trip.
However Swann as shown, which talks about aircraft fuel allocation and management, teaches the concept of utilizing different fuel types during the trip.
wherein executing the fueling plan comprises at least one of:
(i) switching, during navigation along the travel pathway, active fuel consumption from a primary fuel type to a secondary fuel type, or
(ii) combining, aboard the vehicle and during navigation along the travel pathway, a first fuel type and a second fuel type.
(See Swann ¶0783, “The one or more fuel usage parameters may include any one or more of: [0784] i) a blending schedule according to which fuel from the first fuel source 302 and the second fuel source 304 is blended by the fuel delivery regulator 306 (e.g. using the fuel blender 318 described above); [0785] ii) a switching schedule according to which the fuel delivery regulator 306 is configured to switch between delivery of fuel from the first fuel source 302 and the second fuel source 304; [0786] iii) an onboard fuel blending ratio according to which the fuel delivery regulator 306 is configured to blend fuel from the sources on board the aircraft; [0787] iv) an allocation of fuel tanks 53, 55 provided in the aircraft to form the first fuel source 302 and the second fuel source 304. This may allow the allocation of fuel tanks to be configured so that the amount of fuel of each type required for the flight can be stored on board the aircraft; and/or [0788] ii) an isolation valve setting for fuel tanks 53, 55 forming the first fuel source 302 and the second fuel source 304. This may allow the configuration of the fuel tanks to be configured by determining which tanks on board the aircraft are isolated or in fluidic communication with each other.” Swann teaches the concept of executing blending of fuel according to a preplanned schedule.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Wann with the combination of Fox and Russo. As shown, the combination discloses the concept of utilizing machine learning to determine optimize fuel plan including accounting for different types of fuels and blends (See Russo ¶0042). Swann further teaches the concept of allowing for switching/blending of fuels onboard. Swann teaches this concept to allow for improved efficiency and reduced emissions while also adapting to real-time conditions (See Swann ¶0074 and ¶0243). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Swann to further allow for execution of scheduled fuel plans while further adapting to real-time conditions.
As per claim 8:
The method of claim 1, wherein:
the current environmental data comprises current carbon dioxide concentration along the at least one portion of the travel pathway; (See Fox ¶0037, “As disclosed herein the granularity of data collection and/or presentation may be varied from suburbs, blocks, lengths of road, intersections, and/or even in a fine grid, depending on data sources and computational power/data storage available. Emissions model 206 may also factor in the decay of the emissions. For example, the weather may play a role in emissions decay. For instance, wind, and rain may carry emissions from one area to another. Thus, while one area may not have much vehicle traffic, the wind and/or rain may carry emissions from other areas into the area, thus causing an increase in emission that may be accounted for using emissions model 208.” Fox discloses the concept of collecting current environmental information including emission information.)
the historical environmental data comprises historical carbon dioxide concentration along the at least one portion of the travel pathway; and (See Fox ¶0030, “Emissions model 208 may include data for use in trip planning as well as modeling emissions of the connected vehicles 206. For example, emissions model 208 may include environmental data such as past and forecasted weather data. Emissions model 208 may include performance data for each of connected vehicles 206. Using the weather data and the performance data, computing system 202 may determiner performance of each of connected vehicles 206 to account for things such as engine and/or battery performance at various temperatures. For example, internal combustion engines generally operate at higher efficiencies in colder weather and batteries, generally have lower performance at cold temperature. Thus, models for a delivery truck that has an internal combustion engine may show reduced emissions during winter months than in summer months. An electric vehicle with a lithium-ion battery may be modeled to show a reduced range in winter months.” Fox discloses collecting historical emission information.)
the fueling plan is configured to reduce emissions of the vehicle along the at least one portion of the travel pathway to maintain the current carbon dioxide concentration within a predetermined range. (See Fox ¶0017, “Fleet management and route planning using the systems and methods disclosed herein may ensure vehicles are suitable for a trip planned and their operations do not impact the operation of other vehicles in the fleet. For example, the fleet management and route planning may allow for vehicles to travel without triggering an emissions limit by operating too many vehicles of the same kind in the same area.” Fox discloses optimization to reduce emissions within an area.)
As per claim 9:
The method of claim 1, wherein:
the generative Al model is configured to map the current environmental data to the fueling plan based at least in part on a policy; and
the method further comprises: performing at least one reinforcement learning technique to generate an optimal iteration of the policy, the optimal iteration of the policy being configured to maximize the reward function of the generative Al model.
(See Fox ¶0024, “As disclosed herein, as emission controls become stricter and more dynamic, real-time emissions data may be used to plan trips, distribute emissions from vehicles, and/or switch between vehicle modes. For example, using the systems and methods disclosed herein emissions may be monitored and control to allow for a switch in propulsion methods from internal combustion engines to hybrid and/or electric only so that the fleet of vehicles does not exceed legal limits.” Fox discloses the concept of optimizing emission reduction based on policy.)
As per claim 10:
The method of claim 1, wherein:
the current environmental data comprises at least one weather condition pursuant to the at least one portion of the travel pathway. (See Fox ¶0030, “Emissions model 208 may include data for use in trip planning as well as modeling emissions of the connected vehicles 206. For example, emissions model 208 may include environmental data such as past and forecasted weather data. Emissions model 208 may include performance data for each of connected vehicles 206. Using the weather data and the performance data, computing system 202 may determiner performance of each of connected vehicles 206 to account for things such as engine and/or battery performance at various temperatures. For example, internal combustion engines generally operate at higher efficiencies in colder weather and batteries, generally have lower performance at cold temperature. Thus, models for a delivery truck that has an internal combustion engine may show reduced emissions during winter months than in summer months. An electric vehicle with a lithium-ion battery may be modeled to show a reduced range in winter months.” Fox discloses utilizing weather condition information.)
As per claim 11:
The method of claim 1, wherein:
the current environmental data indicates respective vehicle traffic in accordance with the at least one portion of the travel pathway. (See Fox ¶0058, “At stage 405 trip data may be loaded. The trip data may be retrieved from a memory of computer system 202 or may be generated by computer system 202 as part of planning stage 406. The trip data may include routing information, payload information, such as weight and dimensions, etc. The trip data may further include estimated wait times to pick up and/or drop off passengers/cargo, wait times for traffic (i.e., waiting at intersections, traffic delays, etc.), as well as physical characteristics of the trip. For example, the trip information may include heights of overpasses and/or tunnels the vehicle may encounter.” Fox discloses the concept of utilizing traffic information.)
As per claim 12:
An apparatus comprising at least one processor and at least one non-transitory memory having computer-coded instructions stored thereon that, in execution with at least one processor, cause the apparatus to:
obtain travel pathway data for a vehicle, the travel pathway data comprising current environmental data and historical environmental data in accordance with at least one portion of the travel pathway; (See Fox ¶0051, “The planning request, sometimes referred to as a trip request, may be a request for a single trip and/or a plurality of trips. The planning request may include preferences for modalities, emissions models for the various modalities, route information, and budget information. For example, for a robotaxi, the planning request may include an origin and destination for the trip. The car used by the robotaxi may be one of connected cars 206 and thus one of a fleet of vehicles. The planning request may also be for a plurality of trips. For example, the planning request may be for a plurality of delivery vehicles that may have respective routes to deliver packages. As another example, the planning request may be for a plurality of buses, trains, or other public transit-on-demand modalities that may have respective routes to transport passengers.” Fox discloses the concept of obtaining travel pathway data. See also Fox ¶0025, “These models may include not only vehicle data, but may also include environmental data such as temperature, rainfall, etc. The environmental data may be past recorded data and/or forecasts for expected environmental conditions the vehicles may encounter during operations. Switching their propulsion methods may be dependent on locations of charge points for EVs, the actual trip planned by the passenger, and battery state of charge.” Fox further discloses the concept of collecting environmental data including historical and present/forecasted information.)
generate, via a generative Al model, a fueling plan for the vehicle along the at least one portion of the travel pathway based at least in part on the current environmental data and the historical environmental data, wherein: (See Fox ¶0015, “As disclosed herein, recharging and refueling planning may select and account for the emissions of the energy medium (e.g., coal, wind, solar, etc.) used to create the electricity used to charge an EV. This data may be used to simulate the emissions while driving either with “dirty” sources, such as coal, or “clean” sources, such as solar or wind.” Fox discloses the concept of generating a fueling plan.)
a reward function of the generative Al model is based at least in part on a negative summation of at least one greenhouse emission estimate for the vehicle along the at least the portion of the travel pathway and at least one fuel availability parameter; and (See also Fox ¶0049, “Budget predictions may also include utilizing optimization techniques to minimize emissions. For example, during stage 312 Monte Carlo simulations and other optimization techniques may be used to minimize emissions. For example, the various trips connected vehicles 206 may be planned to take may be simulated using different combinations of vehicles, routes, modalities, etc. to determine a plurality of trip plans and vehicle assignments that result in reduced emissions.” Fox discloses optimizing the reduction of greenhouse gases.)
execute the fueling plan to control fuel utilized by the vehicle during navigation along the travel pathway, (See Fox ¶0056,” Outputting the trip plan may include providing a listing of the trip plan including directions for a driver. The outputted trip plan may also define operating characteristics of the trip plan such as speed. Outputting the trip plan may also include transmitting an activation signal to an autonomous vehicle. For example, if the trip plan is for a robotaxi to be conducted by an autonomous vehicle, the activation signal may be transmitted to the autonomous vehicle. Upon receiving the activation signal, a controller of the autonomous vehicle may cause the autonomous vehicle to perform the trip.” Fox discloses the concept of outputting the optimized plan including executing the plan.)
Although Fox discloses the above-enclosed invention including utilizing machine learning in the analysis and prediction of emissions, Fox fails to explicitly disclose utilizing generative artificial intelligence to generate the plan.
However Russo as shown, which talks about carbon emission optimization, teaches the concept of utilizing generative artificial intelligence to generate a plan.
generate, via a generative Al model, a fueling plan for the vehicle along the at least one portion of the travel pathway based at least in part on the current environmental data and the historical environmental data, wherein: (See Russo ¶0065, “With continued reference to FIG. 1, one or more generative machine learning models may utilize one or more predefined templates representing, for example, and without limitation, standard transportation routes, common transportation modes, historical transportation patterns, optimal load distributions, and/or the like. In a non-limiting example, one or more transportation plan templates may outline a standard route for shipping goods from a warehouse in City A to retail outlet in City B using road transportation, such transportation plan templates may detail preferred highway routes, rest stops, refueling points, and event time-of-day recommendations for avoiding traffic. In some cases, transportation plan templates may serve as benchmarks for comparing and evaluating the efficiency, cost-effectiveness, and carbon emission implication or alternative routes or modes proposed by one or more generative machine learning models.” Russo teaches the concept of utilizing generative AI to generate a travel/fueling plan.)
a reward function of the generative Al model is based at least in part on a negative summation of at least one greenhouse emission estimate for the vehicle along the at least one portion of the travel pathway and at least one fuel availability parameter; and (See Russo ¶0054, “Still referring to FIG. 1, in some cases, at least one transportation plan 144 may be formulated, by processor 104, through analyzing historical data (e.g., historical carbon emissions), current conditions (e.g., weather patterns, traffic congestion, fuel availability and the like), and predictive models (e.g., future demand forecasts, anticipated environmental changes, and the like).” Russo teaches accounting for fuel availability. See also Russo ¶0055, “ Generating at least one transportation plan 144 may include generating at least one transportation plan 144 using trained transportation plan generation model as a function of integrated logistics data collection 112. In a case where more than one transportation plans are generated, processor 104 may be configured to select at least one transportation plan 144 from multiple transportation plans based on one or more criterion such as minimizing projected carbon emission, optimizing delivery times, or balancing between the two. In a non-limiting example, if a first and a second transportation plans offer similar delivery times, but the first transportation plan has a significantly lower projected carbon emission, processor 104 may prioritize first transportation plan over the second transportation plan. Conversely, if faster delivery time weight more, processor 104 may opt for a transportation plan that includes transportation vehicles having highest speed event if it may have a slightly higher projected carbon emission.” Russo teaches optimizing/minimizing greenhouse emissions in the planning.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Russo with the invention of Fox. As shown, Fox discloses the concept of managing vehicle emissions for planned trips including utilizing machine learning to perform analysis of emission information and projecting emissions for each trips. Russo further teaches the concept of utilizing generative AI to generate the plans. Russo further teaches this concept to allow for continuous and multivariable considerations in the generation of plans to allow for the testing of different plans, accounting for different variables, and optimizing for different requirements (See Russo ¶0057-¶0058). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Russo to further generate optimized plans.
Although Fox and Russo discloses the above-enclosed invention, the combination fails to explicitly disclose executing the fuel to plan to include utilizing different fuel types during the trip.
However Swann as shown, which talks about aircraft fuel allocation and management, teaches the concept of utilizing different fuel types during the trip.
wherein executing the fueling plan comprises at least one of:
(i) switching, during navigation along the travel pathway, active fuel consumption from a primary fuel type to a secondary fuel type, or
(ii) combining, aboard the vehicle and during navigation along the travel pathway, a first fuel type and a second fuel type.
(See Swann ¶0783, “The one or more fuel usage parameters may include any one or more of: [0784] i) a blending schedule according to which fuel from the first fuel source 302 and the second fuel source 304 is blended by the fuel delivery regulator 306 (e.g. using the fuel blender 318 described above); [0785] ii) a switching schedule according to which the fuel delivery regulator 306 is configured to switch between delivery of fuel from the first fuel source 302 and the second fuel source 304; [0786] iii) an onboard fuel blending ratio according to which the fuel delivery regulator 306 is configured to blend fuel from the sources on board the aircraft; [0787] iv) an allocation of fuel tanks 53, 55 provided in the aircraft to form the first fuel source 302 and the second fuel source 304. This may allow the allocation of fuel tanks to be configured so that the amount of fuel of each type required for the flight can be stored on board the aircraft; and/or [0788] ii) an isolation valve setting for fuel tanks 53, 55 forming the first fuel source 302 and the second fuel source 304. This may allow the configuration of the fuel tanks to be configured by determining which tanks on board the aircraft are isolated or in fluidic communication with each other.” Swann teaches the concept of executing blending of fuel according to a preplanned schedule.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Wann with the combination of Fox and Russo. As shown, the combination discloses the concept of utilizing machine learning to determine optimize fuel plan including accounting for different types of fuels and blends (See Russo ¶0042). Swann further teaches the concept of allowing for switching/blending of fuels onboard. Swann teaches this concept to allow for improved efficiency and reduced emissions while also adapting to real-time conditions (See Swann ¶0074 and ¶0243). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Swann to further allow for execution of scheduled fuel plans while further adapting to real-time conditions.
As per claim 15:
The apparatus of claim 12, wherein: the generative Al model comprises a deep-Q network (DQN). (See Russo ¶0125, “With continued reference to FIG. 3, in an embodiment, neural network 300 may include a deep neural network (DNN). As used in this disclosure, a “deep neural network” is defined as a neural network with two or more hidden layers. In a non-limiting example, neural network 300 may include a convolutional neural network (CNN). A “convolutional neural network,” for the purpose of this disclosure, is a neural network in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel,” along with one or more additional layers such as pooling layers, fully connected layers, and the like. In some cases, CNN may include, without limitation, a deep neural network (DNN) extension. Mathematical (or convolution) operations performed in the convolutional layer may include convolution of two or more functions, where the kernel may be applied to input data e.g., one or more data points or datasets of integrated logistics data collection as described above with reference to FIG. 1, through a sliding window approach. In some cases, convolution operations may enable processor 104 to detect local/global patterns, edges, textures, and any other features described herein within. features may be passed through one or more activation functions, such as without limitation, Rectified Linear Unit (ReLU), to introduce non-linearities into one or more processing step described herein. Additionally, or alternatively, CNN may also include one or more pooling layers, wherein each pooling layer is configured to reduce the dimensionality of input data while preserving essential features within the input data. In a non-limiting example, CNN may include one or more pooling layer configured to reduce the dimensions of feature maps by applying downsampling, such as max-pooling or average pooling, to small, non-overlapping regions of one or more features.” Russo teaches the AI model to include deep neural network including convolution layer filters.)
As per claim 17:
The apparatus of claim 12, wherein:
the historical environmental data comprises:
historical weather data pursuant to the at least one portion of the travel pathway and a current season; and
historical GHG concentration pursuant to the at least one portion of the travel pathway.
(See Fox ¶0030, “Emissions model 208 may include data for use in trip planning as well as modeling emissions of the connected vehicles 206. For example, emissions model 208 may include environmental data such as past and forecasted weather data. Emissions model 208 may include performance data for each of connected vehicles 206. Using the weather data and the performance data, computing system 202 may determiner performance of each of connected vehicles 206 to account for things such as engine and/or battery performance at various temperatures. For example, internal combustion engines generally operate at higher efficiencies in colder weather and batteries, generally have lower performance at cold temperature. Thus, models for a delivery truck that has an internal combustion engine may show reduced emissions during winter months than in summer months. An electric vehicle with a lithium-ion battery may be modeled to show a reduced range in winter months.” Fox discloses collecting historical emission and weather information.)
As per claim 18:
The apparatus of claim 12, wherein: the computer-coded instructions, in execution with the at least one processor, further cause the apparatus to:
obtain at least one measurement of GHG impact pursuant to navigation of the vehicle over the at least one portion of the travel pathway, the navigation occurring in accordance with the fueling plan; and
retrain the generative Al model based at least in part on the at least one measurement of GHG impact.
(See Russo ¶0121, “Still referring to FIG. 2, retraining and/or additional training may be performed using any process for training described above, using any currently or previously deployed version of a machine-learning model and/or algorithm as a starting point. Training data for retraining may be collected, preconditioned, sorted, classified, sanitized or otherwise processed according to any process described in this disclosure. Training data may include, without limitation, training examples including inputs and correlated outputs used, received, and/or generated from any version of any system, module, machine-learning model or algorithm, apparatus, and/or method described in this disclosure; such examples may be modified and/or labeled according to user feedback or other processes to indicate desired results, and/or may have actual or measured results from a process being modeled and/or predicted by system, module, machine-learning model or algorithm, apparatus, and/or method as “desired” results to be compared to outputs for training processes as described above.” Russo teaches collecting additional information and performing retraining of the model.)
As per claim 19:
The apparatus of claim 12, wherein: the vehicle comprises the apparatus. (See Fox ¶0078, “FIG. 7 is a block diagram illustrating a machine in the example form of a computer system 700, such as computer system 202, within which a set or sequence of instructions may be executed to cause the machine to perform any one of the methodologies discussed herein, according to an embodiment. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of either a server or a client machine in server-client network environments, or it may act as a peer machine in peer-to-peer (or distributed) network environments. The machine may be a vehicle subsystem, a personal computer (PC), a tablet PC, a hybrid tablet, a personal digital assistant (PDA), a mobile telephone, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. Similarly, the term “processor-based system” shall be taken to include any set of one or more machines that are controlled by or operated by a processor (e.g., a computer) to individually or jointly execute instructions to perform any one or more of the methodologies discussed herein.” Fox discloses the apparatus to be part of the vehicle.)
As per claim 20:
A computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon that, in execution with at least one processor, is configured to:
obtain travel pathway data for a vehicle, the travel pathway data comprising current environmental data and historical environmental data in accordance with at least one portion of the travel pathway; (See Fox ¶0051, “The planning request, sometimes referred to as a trip request, may be a request for a single trip and/or a plurality of trips. The planning request may include preferences for modalities, emissions models for the various modalities, route information, and budget information. For example, for a robotaxi, the planning request may include an origin and destination for the trip. The car used by the robotaxi may be one of connected cars 206 and thus one of a fleet of vehicles. The planning request may also be for a plurality of trips. For example, the planning request may be for a plurality of delivery vehicles that may have respective routes to deliver packages. As another example, the planning request may be for a plurality of buses, trains, or other public transit-on-demand modalities that may have respective routes to transport passengers.” Fox discloses the concept of obtaining travel pathway data. See also Fox ¶0025, “These models may include not only vehicle data, but may also include environmental data such as temperature, rainfall, etc. The environmental data may be past recorded data and/or forecasts for expected environmental conditions the vehicles may encounter during operations. Switching their propulsion methods may be dependent on locations of charge points for EVs, the actual trip planned by the passenger, and battery state of charge.” Fox further discloses the concept of collecting environmental data including historical and present/forecasted information.)
generate, via a generative AI model, a fueling plan for the vehicle along the at least one portion of the travel pathway based at least in part on the current environmental data and the historical environmental data, wherein: (See Fox ¶0015, “As disclosed herein, recharging and refueling planning may select and account for the emissions of the energy medium (e.g., coal, wind, solar, etc.) used to create the electricity used to charge an EV. This data may be used to simulate the emissions while driving either with “dirty” sources, such as coal, or “clean” sources, such as solar or wind.” Fox discloses the concept of generating a fueling plan.)
a reward function of the generative AI model is based at least in part on a negative summation of at least one greenhouse emission estimate for the vehicle along the at least one portion of the travel pathway and at least one fuel availability parameter; and (See also Fox ¶0049, “Budget predictions may also include utilizing optimization techniques to minimize emissions. For example, during stage 312 Monte Carlo simulations and other optimization techniques may be used to minimize emissions. For example, the various trips connected vehicles 206 may be planned to take may be simulated using different combinations of vehicles, routes, modalities, etc. to determine a plurality of trip plans and vehicle assignments that result in reduced emissions.” Fox discloses optimizing the reduction of greenhouse gases.)
execute the fueling plan to control fuel utilized by the vehicle during navigation along the travel pathway, (See Fox ¶0056,” Outputting the trip plan may include providing a listing of the trip plan including directions for a driver. The outputted trip plan may also define operating characteristics of the trip plan such as speed. Outputting the trip plan may also include transmitting an activation signal to an autonomous vehicle. For example, if the trip plan is for a robotaxi to be conducted by an autonomous vehicle, the activation signal may be transmitted to the autonomous vehicle. Upon receiving the activation signal, a controller of the autonomous vehicle may cause the autonomous vehicle to perform the trip.” Fox discloses the concept of outputting the optimized plan including executing the plan.)
Although Fox discloses the above-enclosed invention including utilizing machine learning in the analysis and prediction of emissions, Fox fails to explicitly disclose utilizing generative artificial intelligence to generate the plan.
However Russo as shown, which talks about carbon emission optimization, teaches the concept of utilizing generative artificial intelligence to generate a plan.
generate, via a generative AI model, a fueling plan for the vehicle along the at least one portion of the travel pathway based at least in part on the current environmental data and the historical environmental data, wherein: (See Russo ¶0065, “With continued reference to FIG. 1, one or more generative machine learning models may utilize one or more predefined templates representing, for example, and without limitation, standard transportation routes, common transportation modes, historical transportation patterns, optimal load distributions, and/or the like. In a non-limiting example, one or more transportation plan templates may outline a standard route for shipping goods from a warehouse in City A to retail outlet in City B using road transportation, such transportation plan templates may detail preferred highway routes, rest stops, refueling points, and event time-of-day recommendations for avoiding traffic. In some cases, transportation plan templates may serve as benchmarks for comparing and evaluating the efficiency, cost-effectiveness, and carbon emission implication or alternative routes or modes proposed by one or more generative machine learning models.” Russo teaches the concept of utilizing generative AI to generate a travel/fueling plan.)
a reward function of the generative AI model is based at least in part on a negative summation of at least one greenhouse emission estimate for the vehicle along the at least one portion of the travel pathway and at least one fuel availability parameter; and (See Russo ¶0054, “Still referring to FIG. 1, in some cases, at least one transportation plan 144 may be formulated, by processor 104, through analyzing historical data (e.g., historical carbon emissions), current conditions (e.g., weather patterns, traffic congestion, fuel availability and the like), and predictive models (e.g., future demand forecasts, anticipated environmental changes, and the like).” Russo teaches accounting for fuel availability. See also Russo ¶0055, “ Generating at least one transportation plan 144 may include generating at least one transportation plan 144 using trained transportation plan generation model as a function of integrated logistics data collection 112. In a case where more than one transportation plans are generated, processor 104 may be configured to select at least one transportation plan 144 from multiple transportation plans based on one or more criterion such as minimizing projected carbon emission, optimizing delivery times, or balancing between the two. In a non-limiting example, if a first and a second transportation plans offer similar delivery times, but the first transportation plan has a significantly lower projected carbon emission, processor 104 may prioritize first transportation plan over the second transportation plan. Conversely, if faster delivery time weight more, processor 104 may opt for a transportation plan that includes transportation vehicles having highest speed event if it may have a slightly higher projected carbon emission.” Russo teaches optimizing/minimizing greenhouse emissions in the planning.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Russo with the invention of Fox. As shown, Fox discloses the concept of managing vehicle emissions for planned trips including utilizing machine learning to perform analysis of emission information and projecting emissions for each trips. Russo further teaches the concept of utilizing generative AI to generate the plans. Russo further teaches this concept to allow for continuous and multivariable considerations in the generation of plans to allow for the testing of different plans, accounting for different variables, and optimizing for different requirements (See Russo ¶0057-¶0058). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Russo to further generate optimized plans.
Although Fox and Russo discloses the above-enclosed invention, the combination fails to explicitly disclose executing the fuel to plan to include utilizing different fuel types during the trip.
However Swann as shown, which talks about aircraft fuel allocation and management, teaches the concept of utilizing different fuel types during the trip.
wherein executing the fueling plan comprises at least one of:
(i) switching, during navigation along the travel pathway, active fuel consumption from a primary fuel type to a secondary fuel type, or
(ii) combining, aboard the vehicle and during navigation along the travel pathway, a first fuel type and a second fuel type.
(See Swann ¶0783, “The one or more fuel usage parameters may include any one or more of: [0784] i) a blending schedule according to which fuel from the first fuel source 302 and the second fuel source 304 is blended by the fuel delivery regulator 306 (e.g. using the fuel blender 318 described above); [0785] ii) a switching schedule according to which the fuel delivery regulator 306 is configured to switch between delivery of fuel from the first fuel source 302 and the second fuel source 304; [0786] iii) an onboard fuel blending ratio according to which the fuel delivery regulator 306 is configured to blend fuel from the sources on board the aircraft; [0787] iv) an allocation of fuel tanks 53, 55 provided in the aircraft to form the first fuel source 302 and the second fuel source 304. This may allow the allocation of fuel tanks to be configured so that the amount of fuel of each type required for the flight can be stored on board the aircraft; and/or [0788] ii) an isolation valve setting for fuel tanks 53, 55 forming the first fuel source 302 and the second fuel source 304. This may allow the configuration of the fuel tanks to be configured by determining which tanks on board the aircraft are isolated or in fluidic communication with each other.” Swann teaches the concept of executing blending of fuel according to a preplanned schedule.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Wann with the combination of Fox and Russo. As shown, the combination discloses the concept of utilizing machine learning to determine optimize fuel plan including accounting for different types of fuels and blends (See Russo ¶0042). Swann further teaches the concept of allowing for switching/blending of fuels onboard. Swann teaches this concept to allow for improved efficiency and reduced emissions while also adapting to real-time conditions (See Swann ¶0074 and ¶0243). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Swann to further allow for execution of scheduled fuel plans while further adapting to real-time conditions.
As per claim 21:
The method of claim 1, wherein executing the fueling plan comprises at least one of:
controlling a fuel switch mechanism to switch, during navigation, active fuel consumption from the primary fuel type to the secondary fuel type; or
controlling a fuel blend mechanism to combine, aboard the vehicle, the first fuel type and the second fuel type.
(See Swann ¶0783, “The one or more fuel usage parameters may include any one or more of: [0784] i) a blending schedule according to which fuel from the first fuel source 302 and the second fuel source 304 is blended by the fuel delivery regulator 306 (e.g. using the fuel blender 318 described above); [0785] ii) a switching schedule according to which the fuel delivery regulator 306 is configured to switch between delivery of fuel from the first fuel source 302 and the second fuel source 304; [0786] iii) an onboard fuel blending ratio according to which the fuel delivery regulator 306 is configured to blend fuel from the sources on board the aircraft; [0787] iv) an allocation of fuel tanks 53, 55 provided in the aircraft to form the first fuel source 302 and the second fuel source 304. This may allow the allocation of fuel tanks to be configured so that the amount of fuel of each type required for the flight can be stored on board the aircraft; and/or [0788] ii) an isolation valve setting for fuel tanks 53, 55 forming the first fuel source 302 and the second fuel source 304. This may allow the configuration of the fuel tanks to be configured by determining which tanks on board the aircraft are isolated or in fluidic communication with each other.” Swann teaches the concept of executing blending of fuel according to a preplanned schedule onboard the vehicle.)
Claim(s) 2-7, 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fox et al. (US 20210108937 A1) (hereafter Fox), in view of Russo et al. (US 20250238813 A1) (hereafter Russo), in view of Swann et al. (US 20230323820 A1) (hereafter Swann), in view of Zhang et al. (US 20240409238 A1) (hereafter Zhang).
As per claim 2:
Although the combination of Fox, Russo, and Swann discloses the above-enclosed invention including utilizing fuel blends, the combination fails to explicitly disclose determining particular fuel blends wherein the individual fuels produce different levels of greenhouse gases.
However Zhang as shown, which talks about tracking sustainable aviation fuel usage and emissions, teaches the concept of determining particular fuel blends wherein the individual fuels produce different levels of greenhouse gases.
The method of claim 1, further comprising, in execution of the fueling plan:
provisioning to a maintainer of the vehicle a mixing ratio between the first fuel type and the second fuel type to cause fueling of the vehicle based at least in part on the mixing ratio, wherein the second fuel type is associated with reduced greenhouse emission compared to the first fuel type. (See Zhang ¶0036, “After SAF production 74, the produced SAF can be certified with certification 76. Certification 76 can include analysis, measurement, or review such that the produced SAF meets certain standards or expectations, often purity or concentration standards. Different SAFs, and production pathways thereof, can be certified in different ways, or based upon different standards, such as different maximum values or thresholds, or differing blend ratios, for example. Certification can vary based upon locale or region, such as government or business. Variations in certification can lead to variations among SAF fuels based upon local certification requirements. The system 50 can consider these variations in determining an emission score, resultant of different certification requirements. Certification of the SAF can be performed by an independent business or entity in order to determine if the fuel meets required industry, government, or environmental standards. Many governments or businesses require SAFs to meet specific standards, and require certification 76 prior to transportation and logistics 80. Certification 76 of the SAFs can contribute to the emission score. There is a cost required with such certification, as measurement, testing, storage during testing, or any other requirement utilized in such certification. Such an additional cost can be monetary and/or emissions, and contributes to the emission score. In one non-limiting example, the SAF may be transported to a certification site where the transportation and movement of fuels contributes to the emission score. Furthermore, while certification 76 is shown positioned between SAF production 74 and transportation and logistics 80, it should be appreciated that the decision tool 52 can perform certification at any point within the system 50, as may be desirable or required to ensure the SAF is meeting standards or requirements throughout production, transport, and use.” Zhang teaches the concept of acquiring mixture values of a first and second fuel type including a reduced emission fuel.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Zhang with the combination of Fox, Russo, and Swann. As shown, the combination discloses the concept of generating and executing optimize fueling plans including accounting for blended fuels. Zhang further teaches the concept of fuels to be blended including acquiring/determining different fuel blending requirements including industry based requirements. Zhang teaches this concept as sustainable aircraft fuels (SAF) to be utilized to reduce emissions while still maintaining minimum requirements to safely operate the vehicle (See Zhang ¶0017 and ¶0036). Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Zhang to further utilize improve emissions while maintaining aviation safety standards.
As per claim 3:
The method of claim 2, wherein: the at least one fuel availability parameter comprises at least one of an available volume of the second fuel type or a current cost of the second fuel type. (See Zhang ¶0052, “FIG. 4 is an example flow chart diagram of demonstrating a method 100 of selecting a sustainable aircraft fuel (SAF) for use in a flight between a departing airport and a destination airport, such as the airport 32 of FIG. 1, in accordance with various aspects described herein. Non-limiting aspects of the method 100 can include, at 102, determining, by a controller module 54 of the decision tool 52, available SAFs at or near the departing airport. Near can be within a threshold proximity, such that the SAF can be transported to the aircraft for use in the flight.” Zhang teaches the concept of determining availability of second fuel type.)
As per claim 4:
The method of claim 2, wherein: the generative Al model is configured to:
predict a respective greenhouse gas (GHG) impact over the travel pathway pursuant to a plurality of mixing ratio candidates; (See Zhang ¶0038, “At blending or mixing 82, the distributed SAF can be blended or mixed. For example, SAFs can be blended with fossil-based jet fuels or other fuels to form new fuels, which can result an overall reduction in emissions or increased efficiency resultant of the addition of the SAF. Additionally, blending or mixing the fuels can change engine operation through fuel burn, such as burn speed or temperature. Blending or mixing SAFs contributes to the emission score. For example, a SAF blenders tax credit (BTC) can provide for a per-gallon credit or value for SAF fuels blended with fossil-based fuels or other jet fuels. Therefore, it is contemplated that the system 50 considers credits for fuel mixes, and can consider cost offsets based upon credits received for using a SAF BTC qualified fuel mixture. For example, jet fuels can be blended with a SAF to form a SAF blend. Alternatively, the SAF may be a 100% drop-in, which may be utilized without blending with another fuel. In another example, all fuels, including SAFs may be eligible for credits for greenhouse gas reduction performance against a baseline value or factor as a clean fuel production credit.” Zhang teaches the concept of determining different emission reductions for different mixtures of fuels.)
determine, as the mixing ratio between the first fuel type and the second fuel type, one of the plurality of mixing ratio candidates that is associated with a minimum predicted GHG impact. (See Zhang ¶0038, “The system 50 can further consider what mixes or amounts of SAFs can be utilized to meet such standards, in order to receive benefits associated with such standards. Furthermore, it should be appreciated that the blending or mixing of the fuels can also contribute to the emission score. There is an energy cost required to mix fuels, as well as any additional processing or production which can be required after mixing or blending. All of these factors contributed to the emission score of blending or mixing SAFs.” See also Zhang ¶0046, “In this way, it should be understood that within the system 50, a product such as crude oil or corn can vary the emission score, defining an individual fuel pathway, or portion thereof, for the system 50. In another example, the SAF can be blended or mixed 82 with a jet fuel, thereby defining an emission score considering the combination between the two, and that the new SAF created as the mixture of the two defines a new fuel pathway that incorporates both fuels contributing to the blending or mixing 82 of the SAFs. Therefore, it should be understood that multiple different fuel pathways are contemplated, depending on the particulars of the individual elements of the system 50. With such an understanding, it should be appreciated that each individual fuel pathway can include an individual emission score.” Zhang teaches the concept of determining blends with optimal emission reduction.)
As per claim 5:
The method of claim 4, wherein: the generative Al model is further configured to:
predict, in accordance with a respective mixing ratio candidate, a set of GHG impact permutations pursuant to a plurality of condition scenarios along the travel pathway; and (See Zhang ¶0065, “The emission score can further include determining the availability of emission credits, such as carbon credits. The emission credits can be specific to a SAF, government, or region, for example, where the potential to utilize or sell offsetting emission credits can impact the emission score. The emission score can further model emissions for a flight, or fleet of aircraft, in real time, and the decision tool 52 can provide updated decision information for purchase, sale, storage, or other use of the SAF. In another example, the emission score can include a carbon intensity. The emission score can include potential carbon offsets, such as carbon dioxide capture or forestry planting. In yet another example, the emission score can model supply and demand for a SAF over a time period, in order to determine the emission score for fuel pathways based upon anticipated demand for the SAF, or resultant availability thereof during the time period. Such a time period can be tailored to an emissions credit, for example. Additionally, the emission score can consider emission credits which are outside of the fuel pathway for the particular SAF. In yet another example, modeling the emission score in real time can further consider aircraft route, where such a route can determine an emission score for the fuel pathway, such as environment, weather, or the availability of emission credits locally. In another non-limiting example, modelling can include an off-line determination, utilizing historic flight data or SAF data used to determine an emission score expected based upon the flight mission, flight path, weather, or other commonalities based upon the historic data. Additionally, real time modeling of the emission score can include the operational state of the engine 22 burning the SAF, such as at takeoff, climb, cruise, descend, or land. Further still, it is contemplated that the emission score can be periodically updated during the flight of the aircraft 20, as flight requirements can vary fuel needs, and therefore, the emission score.” Zhang teaches the concept of determining emissions impact based on a plurality of factors.)
determine, as the mixing ratio between the first fuel type and the second fuel type, one of the plurality of mixing ratio candidates that is associated with a minimal set of GHG impact permutations.
(See Zhang ¶0047, “The decision tool 52 can provide information specific to the SAF, which permits users to compare SAFs by emission score or another factor. The decision tool 52 further permits the user to make decisions in order to reduce the cost by selecting a SAF, permitting the user to consider costs along the entire lifecycle of the SAF. This way, the decision tool 52 permits the user to reduce monetary cost, thereby maximizing value based upon emissions, determining service risk associated with dynamic changes in policy or cost.” Zhang teaches the concept of predicting emission impact of different fuel blends and determining optimal blends.)
As per claim 6:
Although the combination of Fox, Russo, and Swann discloses the above-enclosed invention including utilizing fuel blends, the combination fails to explicitly disclose determining particular fuel blends comprising specific mixing ratios.
However Zhang as shown, which talks about tracking sustainable aviation fuel usage and emissions, teaches the concept of determining particular fuel blends comprising specific mixing ratios.
The method of claim 1, wherein: the fueling plan comprises a mixing ratio between a first fuel type and a second fuel type; and the method further comprises, in execution of the fueling plan: (See Zhang ¶0036, “After SAF production 74, the produced SAF can be certified with certification 76. Certification 76 can include analysis, measurement, or review such that the produced SAF meets certain standards or expectations, often purity or concentration standards. Different SAFs, and production pathways thereof, can be certified in different ways, or based upon different standards, such as different maximum values or thresholds, or differing blend ratios, for example. Certification can vary based upon locale or region, such as government or business. Variations in certification can lead to variations among SAF fuels based upon local certification requirements. The system 50 can consider these variations in determining an emission score, resultant of different certification requirements. Certification of the SAF can be performed by an independent business or entity in order to determine if the fuel meets required industry, government, or environmental standards. Many governments or businesses require SAFs to meet specific standards, and require certification 76 prior to transportation and logistics 80. Certification 76 of the SAFs can contribute to the emission score. There is a cost required with such certification, as measurement, testing, storage during testing, or any other requirement utilized in such certification. Such an additional cost can be monetary and/or emissions, and contributes to the emission score. In one non-limiting example, the SAF may be transported to a certification site where the transportation and movement of fuels contributes to the emission score. Furthermore, while certification 76 is shown positioned between SAF production 74 and transportation and logistics 80, it should be appreciated that the decision tool 52 can perform certification at any point within the system 50, as may be desirable or required to ensure the SAF is meeting standards or requirements throughout production, transport, and use.” Zhang teaches the concept of acquiring mixture values of a first and second fuel type including a reduced emission fuel.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Zhang with the combination of Fox, Russo, and Swann. As shown, the combination discloses the concept of generating and executing optimize fueling plans including accounting for blended fuels. Zhang further teaches the concept of fuels to be blended including acquiring/determining different fuel blending requirements including industry based requirements. Zhang teaches this concept as sustainable aircraft fuels (SAF) to be utilized to reduce emissions while still maintaining minimum requirements to safely operate the vehicle (See Zhang ¶0017 and ¶0036). Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Zhang to further utilize improve emissions while maintaining aviation safety standards.
in response to the vehicle moving within a proximity of the at least a portion of the travel pathway, combining, aboard the vehicle, respective volumes of the first fuel type and the second fuel type in accordance with the mixing ratio. (See Swann ¶0675, “In order to supply fuel to the pilot fuel injectors 313 during the first cruise operation that is different from that supplied to the combustor during the second cruise operation the fuel delivery regulator 306 is arranged to supply fuel selectively from the first and second fuel sources 302, 304 as described above (e.g. fuel from only the first fuel source 302, fuel from only the second fuel source 304, or a mixture thereof). In one example, the fuel delivery regulator 306 may comprise a fuel blender 318 as shown in FIG. 17. Similarly to as described above, the fuel blender is arranged to receive a supply of fuel from both the first and second fuel sources 302, 304 and output a fuel consisting of fuel from the first fuel source, fuel from the second fuel source, or a blend thereof. This blended fuel is supplied to the pilot fuel injectors 313, with fuel from a single fuel source (the second fuel source 304 in the example above) being supplied to the main fuel injectors 314.” See also Swann ¶0241-¶0242, “calculating an optimised set of one or more fuel characteristics for each operating point of the proposed mission defined in the mission description based on the nvPM impact parameters; and
determining a fuel allocation based on the optimised set of one or more fuel characteristics.” Swann teaches the concept of blending fuels aboard the vehicle based on optimization of the blend and conditions during the flight path including particular times during the path.)
As per claim 7:
Although the combination of Fox, Russo, and Swann discloses the above-enclosed invention including utilizing fuel blends and alterative fuels, the combination fails to explicitly disclose a second fuel with lower emission.
However Zhang as shown, which talks about tracking sustainable aviation fuel usage and emissions, teaches the concept of a second fuel with lower emission.
The method of claim 1, wherein:
the secondary fuel type is associated with reduced greenhouse emission compared to the primary fuel type; and (See Zhang ¶0024, “SAFs utilize sustainable feedstocks, like the biomass feedstock 36, which can utilize or offset carbon emissions 46, or other emission types, in order to form the closed-loop system. The closed loop system provides for sustainability for the fuel, which provides an overall reduction in emissions through utilization of the carbon emissions 46 emitted through use of the fuel.” Zhang teaches the concept of utilizing a second fuel type which is associated with reduced emissions.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Zhang with the combination of Fox, Russo, and Swann. As shown, the combination discloses the concept of generating and executing optimize fueling plans including accounting for blended fuels. Zhang further teaches the concept of fuels to be blended including acquiring/determining different fuel blending requirements including industry based requirements. Zhang teaches this concept as sustainable aircraft fuels (SAF) to be utilized to reduce emissions while still maintaining minimum requirements to safely operate the vehicle (See Zhang ¶0017 and ¶0036). Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Zhang to further utilize improve emissions while maintaining aviation safety standards.
the fueling plan indicates an interval along the travel pathway at which to switch active fuel consumption of the vehicle from the primary fuel type to the secondary fuel type;
the method further comprises, in execution of the fueling plan:
causing the vehicle to switch active fuel consumption from the primary fuel type to the secondary fuel type in response to the vehicle traversing to the interval along the travel pathway.
(See Swann ¶00299-¶0303, The one or more fuel usage parameters may optionally include any one or more of: [0300] i) a blending schedule according to which fuel from the first fuel source and the second fuel source is blended by the fuel delivery regulator; [0301] ii) a switching schedule according to which the fuel delivery regulator is configured to switch between delivery of fuel from the first fuel source and the second fuel source; [0302] iii) an allocation of fuel tanks provided in the aircraft to form the first fuel source and the second fuel source; and/or [0303] iv) an isolation valve setting for fuel tanks forming the first fuel source and the second fuel source.” Swann teaches the concept of switching from a first fuel type to a second fuel type based on a planned schedule.)
As per claim 13:
Although the combination of Fox, Russo, and Swann discloses the above-enclosed invention including utilizing fuel blends, the combination fails to explicitly disclose determining particular fuel blends comprising specific mixing ratios.
However Zhang as shown, which talks about tracking sustainable aviation fuel usage and emissions, teaches the concept of determining particular fuel blends comprising specific mixing ratios.
The apparatus of claim 12, wherein:
the fueling plan comprises a mixing ratio between a first fuel type and a second fuel type; and the computer-code instructions, in execution with the at least one processor, further cause the apparatus to: (See Zhang ¶0036, “After SAF production 74, the produced SAF can be certified with certification 76. Certification 76 can include analysis, measurement, or review such that the produced SAF meets certain standards or expectations, often purity or concentration standards. Different SAFs, and production pathways thereof, can be certified in different ways, or based upon different standards, such as different maximum values or thresholds, or differing blend ratios, for example. Certification can vary based upon locale or region, such as government or business. Variations in certification can lead to variations among SAF fuels based upon local certification requirements. The system 50 can consider these variations in determining an emission score, resultant of different certification requirements. Certification of the SAF can be performed by an independent business or entity in order to determine if the fuel meets required industry, government, or environmental standards. Many governments or businesses require SAFs to meet specific standards, and require certification 76 prior to transportation and logistics 80. Certification 76 of the SAFs can contribute to the emission score. There is a cost required with such certification, as measurement, testing, storage during testing, or any other requirement utilized in such certification. Such an additional cost can be monetary and/or emissions, and contributes to the emission score. In one non-limiting example, the SAF may be transported to a certification site where the transportation and movement of fuels contributes to the emission score. Furthermore, while certification 76 is shown positioned between SAF production 74 and transportation and logistics 80, it should be appreciated that the decision tool 52 can perform certification at any point within the system 50, as may be desirable or required to ensure the SAF is meeting standards or requirements throughout production, transport, and use.” Zhang teaches the concept of acquiring mixture values of a first and second fuel type including a reduced emission fuel.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Zhang with the combination of Fox, Russo, and Swann. As shown, the combination discloses the concept of generating and executing optimize fueling plans including accounting for blended fuels. Zhang further teaches the concept of fuels to be blended including acquiring/determining different fuel blending requirements including industry based requirements. Zhang teaches this concept as sustainable aircraft fuels (SAF) to be utilized to reduce emissions while still maintaining minimum requirements to safely operate the vehicle (See Zhang ¶0017 and ¶0036). Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Zhang to further utilize improve emissions while maintaining aviation safety standards.
generate the fueling plan as the vehicle traverses the travel pathway; and (See Russo ¶0066, “Further, transportation plan templates may be dynamically updated based on new data, insights, or changing conditions as described in detail below. In a non-limiting example, if a particular highway route in the transportation template becomes frequently congested due to construction or increased traffic, processor 104 may be configured to update transportation template to generate new transportation plans that avoid such congested route.” Russo teaches the concept of generating plans dynamically based on up to date information collected.)
in execution of the fueling plan and in response to the vehicle moving within a proximity of the at least one portion of the travel pathway, cause a mixing of respective onboard volumes of the first fuel type and the second fuel type in accordance with the mixing ratio. (See Swann ¶0675, “In order to supply fuel to the pilot fuel injectors 313 during the first cruise operation that is different from that supplied to the combustor during the second cruise operation the fuel delivery regulator 306 is arranged to supply fuel selectively from the first and second fuel sources 302, 304 as described above (e.g. fuel from only the first fuel source 302, fuel from only the second fuel source 304, or a mixture thereof). In one example, the fuel delivery regulator 306 may comprise a fuel blender 318 as shown in FIG. 17. Similarly to as described above, the fuel blender is arranged to receive a supply of fuel from both the first and second fuel sources 302, 304 and output a fuel consisting of fuel from the first fuel source, fuel from the second fuel source, or a blend thereof. This blended fuel is supplied to the pilot fuel injectors 313, with fuel from a single fuel source (the second fuel source 304 in the example above) being supplied to the main fuel injectors 314.” See also Swann ¶0241-¶0242, “calculating an optimised set of one or more fuel characteristics for each operating point of the proposed mission defined in the mission description based on the nvPM impact parameters; and
determining a fuel allocation based on the optimised set of one or more fuel characteristics.” Swann teaches the concept of blending fuels aboard the vehicle based on optimization of the blend and conditions during the flight path including particular times during the path.)
As per claim 14:
The apparatus of claim 13, wherein: the computer-code instructions, in execution with the at least one processor, further cause the apparatus to:
obtain at least one change to the current environmental data as the vehicle traverses the travel pathway;
generate, via the generative Al model, a second fueling plan based at least in part on the at least one change to the current environmental data, the second fueling plan configured to replace the fueling plan and comprising a second mixing ratio between the first fuel type and the second fuel type; and
in execution of the second fueling plan and in response to the vehicle moving within a proximity of the at least one portion of the travel pathway, cause a mixing of respective onboard volumes of the first fuel type and the second fuel type in accordance with the second mixing ratio.
(See Russo ¶0066, “Further, transportation plan templates may be dynamically updated based on new data, insights, or changing conditions as described in detail below. In a non-limiting example, if a particular highway route in the transportation template becomes frequently congested due to construction or increased traffic, processor 104 may be configured to update transportation template to generate new transportation plans that avoid such congested route.” Russo teaches the concept of generating plans dynamically based on up to date information collected.)
Response to Arguments
Applicant’s arguments, see page 12, filed 07/22/2026, with respect to the 35 U.S.C. 101 rejection have been fully considered and are persuasive. Specifically, the rejection in regards to step 2B analysis has been found persuasive as the abstract idea is integrated into a practical application, wherein the claims specifically recites the how the improved fuel plan is executed (through the switching/blending of the fuel in accordance with the generated/determined plan). The 35 U.S.C. 101 rejection of claims 1-15, 17-20 has been withdrawn.
In response to the Applicant’s arguments as directed towards the 35 U.S.C. 103 rejection, the Examiner respectfully disagrees. The Examiner notes that although the combination of Fox and Russo does not explicitly recite a reward function Russo does teach the concept of optimizing fuel usage and path information by analyzing historical and feedback information, including positively reenforcing factors which reduce emissions (See Russo ¶0056-¶0061). As such, although the combination of Fox and Russo does not explicitly recite the term “reward function”, the combination still teaches a generative artificial intelligence which utilizes feedback through positive reinforcement of particular factors which yield positive feedback.
Applicant’s arguments with respect to claim(s) 1-15, 17-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huwyler et al. (US 20150053304 A1), which talks about optimizing the use of multiple fuel types in an engine including dynamically blending fuel types during operation.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT M CAO whose telephone number is (571)270-5598. The examiner can normally be reached Monday - Friday 11-7.
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/VINCENT M CAO/Primary Examiner, Art Unit 3622
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