DETAILED ACTION
Claims 1-20 are pending.
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 .
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to European Patent Application No. 23382924.1, filed on 9/12/2023.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to the abstract idea (mental process) of retrieving data based on other data and determining when a transition confidence metric indicates that the state transition is valid.
Claim 1 recites a system, i.e. a machine, which is a statutory category of invention. The claim recites:
retrieve a transition confidence metric associated with the state transition from a transition confidence matrix for the dynamic system based on the first state and the second state… when the transition confidence metric indicates that the state transition is valid that may be performed in the human mind, or by a human using a pen and paper, i.e. retrieve data based on data and determining when a transition confidence metric indicates that the state transition is valid. Thus the claim recites an abstract idea (mental processes), see MPEP 2106.04(a).
This judicial exception is not integrated into a practical application because the additional elements, i.e. one or more processors; and a memory comprising instructions executable by the one or more processors (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C), obtain first input from a transition state model, wherein the first input indicates a first state of a dynamic system; obtain second input from the transition state model, wherein the second input indicates a state transition from the first state to a second state (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d))) and provide the second state as output (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)) do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, one or more processors; and a memory comprising instructions executable by the one or more processors (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C), obtain first input… obtain a second input… (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d))) and provide the second state as output (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)) are not considered significantly more. Considering the additionally elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Thus the claim is not patent eligible.
Note that aircraft and flight phases are well-understood, routine and conventional, see for example, Debenbusch et al. U.S. Patent No. 11702191 [col. 10 lines 34-61] and the other references cited below. Also note that automatic dependent surveillance broadcast (ADS-B) systems are well-understood, routine and conventional, see for example Shen-Feng et al. U.S. Patent Publication No. 20040044463 [0004] or Sanders U.S. Patent Publication No. 20180026707 [0018].
Claim 2 recites wait for additional input from the transition state model without provision of the output when the transition confidence metric indicates that the state transition is invalid (mental process). Thus this claim recites an abstract idea.
Claim 3 recites a control system, wherein the control system generates one or more control signals for an instance of the dynamic system based on a model of the second state used in response to receipt by the control system of the output, and wherein one or more controllers associated with the instance of the dynamic system implement the one or more control signals (insignificant extra-solution activity — instructions to apply the exception using a technique recited at a high level of generality, see MPEP 2106.05(f) — based on an abstract analysis). Thus this claim recites an abstract idea.
Claim 4 recites a notification system, wherein the notification system generates updated information associated with an instance of the dynamic system based on receipt of the output and provides the updated information to one or more output devices (insignificant extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 5 recites a method, i.e. a process, which is a statutory category of invention. However, the process is similar to that recited in claim 1 and is rejected under the same rationale.
Claims 6-7 recite similar limitations to claims 3-4 and are rejected under the same respective rationales.
Claim 8 recites ‘he first device or a second device is configured to generate the transition confidence matrix, and wherein generating the transition confidence matrix includes: generating a first dataset of transitions for the dynamic system based on historical data that includes, for each state transition that was detected by the transition state model for the historical data: an identifier of a case of the dynamic system that experienced a state transition, a time of the state transition, and a final state associated with the state transition; generating a second dataset of data including a single entry for each case for each distinct type of identified final state; processing the second dataset to identify first cases that satisfy one or more criterion; processing the first dataset to identify, for each state transition of the first cases and based on the time of the state transition, an initial state associated with the state transition that preceded the final state; determining, for each type of state, a first sum of occurrences of the first cases in the first dataset where the state is identified as the initial state; determining, for each initial state and final state pairing, a second sum of occurrences of transitions from the initial state to the final state in the first dataset for the first cases; and generating entries of the transition confidence matrix, wherein an entry associated with a particular initial state and a particular final state is based on the second sum for the particular initial state and the particular final state divided by the first sum associated with the particular initial state’ (mental process). Thus this claim recites an abstract idea.
Claim 9 recites setting each entry of the transition confidence matrix that is less than a confidence threshold to zero (mental process). Thus this claim recites an abstract idea.
Claim 10 recites setting each entry of the transition confidence matrix that is greater than a confidence threshold to one (mental process). Thus this claim recites an abstract idea.
Claim 11 recites changing one or more entries to zero for entries associated with transitions that are known to not occur (mental process). Thus this claim recites an abstract idea.
Claim 12 recites comparing the transition confidence matrix to one or more expected transitions for the dynamic system (mental process). Thus this claim recites an abstract idea.
Claim 13 recites changing one or more entries in the transition confidence matrix based on said comparing the transition confidence matrix to the one or more expected transitions for the dynamic system (mental process). Thus this claim recites an abstract idea.
Claim 14 recites causing the transition state model to be updated based on said comparing the transition confidence matrix to the one or more expected transitions for the dynamic system (mental process). Thus this claim recites an abstract idea.
Claim 15 recites the one or more criterion include presence of a start state and an end state (specifies abstract data). Thus this claim recites an abstract idea.
Claim 16 recites the dynamic system comprises flights of aircraft, and wherein the state transition from the first state to the second state includes a transition from a first phase of flight to a second phase of flight for a particular aircraft (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)). Thus this claim recites an abstract idea.
Claim 17 recites the transition state model is configured to generate the first input and the second input based on automatic dependent surveillance broadcast (ADS-B) data (mental process and generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)). Thus this claim recites an abstract idea.
Claim 18 recites a non-transitory computer-readable medium, i.e. an article of manufacture, which is a statutory category of invention. However, the process performed by the instructions is similar to that recited in claim 1 and is rejected under the same rationale. Note that a non-transitory computer-readable medium considered merely applying the exception with generic computer technology – see MPEP 2106.04(a)(2) III C.
Claim 19 recites the transition confidence matrix is generated by a device configured to: generate a first dataset of transitions for the dynamic system based on historical data that includes, for each state transition that was detected by the transition state model for the historical data: an identifier of a case of the dynamic system that experienced a state transition, a time of the state transition, and a final state associated with the state transition; generate a second dataset of data including a single entry for each case for each distinct type of identified final state; process the second dataset to identify first cases that satisfy one or more criterion; process the first dataset to identify, for each state transition of the first cases and based on the time of the state transition, an initial state associated with the state transition that preceded the final state; determine, for each type of state, a first sum of occurrences of the first cases of instances in the first dataset where the state is identified as the initial state; determine, for each initial state and final state pairing, a second sum of occurrences of transitions from the initial state to the final state in the first dataset for the first cases; and generate entries of the transition confidence matrix, wherein an entry associated with a particular initial state and a particular final state is based on the second sum for the particular initial state and the particular final state divided by the first sum associated with the particular initial state (mental process). Thus this claim recites an abstract idea.
Claim 20 recites the device includes the one or more processors (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C). Thus this claim recites an abstract idea.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 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) 1-2, 4-5, 7 and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gopalakrishnan et al. U.S. Patent Publication No. 20240312350 (hereinafter Gopalakrishnan) in view of Colligan et al. U.S. Patent Publication No. 20190213894 (hereinafter Colligan).
Regarding claim 1, Gopalakrishnan teaches a system [0055-0058, Fig. 9 — a computer system 900 for implementing the methods of forecasting operation data of an aircraft, according to an embodiment of the present disclosure] comprising:
one or more processors; and a memory comprising instructions executable by the one or more processors [0055-0058, Fig. 9 — system bus 910 may be any of several types of bus structures including a memory bus or a memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. A basic input/output (BIOS) stored in memory ROM 940, or the like… the system can use a processor and a computer-readable storage medium to store instructions that, when executed by a processor (e.g., one or more processors), cause the processor to perform a method or other specific actions.] to:
obtain first input from a transition state model, wherein the first input indicates a first state of a dynamic system [0022-0028, Figs. 1-2 — The method includes using a Hidden Markov Model (HMM)… forecasted sensor parameters can then be used, for example, in models to predict aircraft engine removals for shop visit/maintenance activities… an example of data routes used by an aircraft and an example of a transition probability matrix determined based on the data routes used by the aircraft, according to an embodiment. For example, an aircraft A1 takes the following route and flies from departure airport A to arrival airport B. The same aircraft A1 then uses airport B as its new departure airport to land in new arrival airport C. The aircraft then uses airport C as its new departure airport to land back in arrival airport B. The routes of the aircraft A1 are shown at box 200. The bolded letters correspond to the departure airports for the routes taken by the aircraft A1, in this instance. Therefore, the departure airports are listed, at box 202, as follows: A, B, C, B, A, D, and A. The unique departure airports are identified, at box 204, as being, A, B, C, D (first and second states)];
obtain second input from the transition state model, wherein the second input indicates a state transition from the first state to a second state [0022-0028, Figs. 1-2 — The method includes using a Hidden Markov Model (HMM)… forecasted sensor parameters can then be used, for example, in models to predict aircraft engine removals for shop visit/maintenance activities… an example of data routes used by an aircraft and an example of a transition probability matrix determined based on the data routes used by the aircraft, according to an embodiment. For example, an aircraft A1 takes the following route and flies from departure airport A to arrival airport B. The same aircraft A1 then uses airport B as its new departure airport to land in new arrival airport C. The aircraft then uses airport C as its new departure airport to land back in arrival airport B. The routes of the aircraft A1 are shown at box 200. The bolded letters correspond to the departure airports for the routes taken by the aircraft A1, in this instance. Therefore, the departure airports are listed, at box 202, as follows: A, B, C, B, A, D, and A. The unique departure airports are identified, at box 204, as being, A, B, C, D (first and second states)];
retrieve a transition confidence metric associated with the state transition from a transition confidence matrix for the dynamic system based on the first state and the second state [0022-0028, Figs. 1-2 — The method includes calculating a transition probability matrix 106 based on the historical flight data (transition probabilities/ confidence metrics are shown in the figures). For example, a pattern of departure airports and arrival airports used by the aircraft is captured in the form of a transition probability matrix 106, where the transition probability of moving from one airport to another is captured in four matrices, each computed for a selected period of time (e.g., each quarter for four quarters) during a one-year period… The method includes determining forecasted departure airports, forecasted arrival airports and time of arrival of the aircraft to the arrival airports to build a forecasted sequence of future route structure 110 — cf. paragraphs 0021-0023, 0048 of the instant specification/PGPub]; and
provide the second state as output [0022-0028, Figs. 1-2 — The method includes calculating a transition probability matrix 106 based on the historical flight data (transition probabilities/ confidence metrics are shown in the figures). For example, a pattern of departure airports and arrival airports used by the aircraft is captured in the form of a transition probability matrix 106, where the transition probability of moving from one airport to another is captured in four matrices, each computed for a selected period of time (e.g., each quarter for four quarters) during a one-year period… The method includes determining forecasted departure airports, forecasted arrival airports (second states) and time of arrival of the aircraft to the arrival airports to build a forecasted sequence of future route structure 110].
But Gopalakrishnan fails to clearly specify providing the second state as output when a transition confidence metric indicates that the state transition is valid.
However, Colligan teaches providing the second state as output when a transition confidence metric indicates that the state transition is valid [0059, 0085-0086 — service 150 confirms an aircraft state has been reached, and sends a corresponding message or alert to Local and center flight management. The service repeats block 360 until all aircraft surface states have been confirmed and reported. In parallel with block 360, the service 150 executes one or more statistical or probability routines. The executed statistical and probability routines provide statistical data (e.g., confidence intervals and levels) and or probability data (Bayesian probability) that an upcoming aircraft surface state will occur at an expected (scheduled) time. The service 150 provides the statistical/probability information with the message or alert.]
Gopalakrishnan and Colligan are analogous art. They relate to aircraft control/management systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by Gopalakrishnan, by incorporating the above limitations, as taught by Colligan.
One of ordinary skill in the art would have been motivated to do this modification in order to only provide the second state as output when the transition to the second state is certain, as suggested by Colligan [0085-0086].
Regarding claim 2, the combination of Gopalakrishnan and Colligan all the limitations of the base claims as outlined above.
Further, teaches Gopalakrishnan the instructions are further executable by the one or more processors [0055-0058, Fig. 9 — system bus 910 may be any of several types of bus structures including a memory bus or a memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. A basic input/output (BIOS) stored in memory ROM 940, or the like… the system can use a processor and a computer-readable storage medium to store instructions that, when executed by a processor (e.g., one or more processors), cause the processor to perform a method or other specific actions.].
Further, Colligan teaches waiting for additional input from the transition state model without provision of the output when the transition confidence metric indicates that the state transition is invalid [0059, 0085-0086 — service 150 confirms an aircraft state has been reached, and sends a corresponding message or alert to Local and center flight management. The service repeats block 360 until all aircraft surface states have been confirmed and reported (until valid). In parallel with block 360, the service 150 executes one or more statistical or probability routines. The executed statistical and probability routines provide statistical data (e.g., confidence intervals and levels) and or probability data (Bayesian probability) that an upcoming aircraft surface state will occur at an expected (scheduled) time. The service 150 provides the statistical/probability information with the message or alert.]
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Gopalakrishnan and Colligan, by incorporating the above limitations, as taught by Colligan.
One of ordinary skill in the art would have been motivated to do this modification in order to only provide the second state as output when the transition to the second state is certain, as suggested by Colligan [0085-0086].
Regarding claim 4, the combination of Gopalakrishnan and Colligan teaches all the limitations of the base claims as outlined above.
Further, Colligan teaches comprising a notification system, wherein the notification system generates updated information associated with an instance of the dynamic system based on receipt of the output and provides the updated information to one or more output devices [0059, 0085-0086 — service 150 confirms an aircraft state has been reached, and sends a corresponding message or alert to Local and center flight management; 0028 — Automated airport surveillance system refers to a radar system used at airports to detect and display the position of aircraft in the terminal area and the airspace around the airport, and may constitute the main air traffic control system for the airspace around airports — It would, at least, be obvious to one having ordinary skill in the art to show the output on a management display in order to help manage air traffic.].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Gopalakrishnan and Colligan, by incorporating the above limitations, as taught by Colligan.
One of ordinary skill in the art would have been motivated to do this modification in order to help manage air traffic, as suggested by Colligan [0028].
Regarding claim 5, Gopalakrishnan teaches a method [0022-0028, Figs. 1-2 — a method of forecasting aircraft operational data] comprising:
obtaining, at a first device, first input from a transition state model for a dynamic system, wherein the first input indicates a first state of the dynamic system [0022-0028, Figs. 1-2 — The method includes using a Hidden Markov Model (HMM)… forecasted sensor parameters can then be used, for example, in models to predict aircraft engine removals for shop visit/maintenance activities… an example of data routes used by an aircraft and an example of a transition probability matrix determined based on the data routes used by the aircraft, according to an embodiment. For example, an aircraft A1 takes the following route and flies from departure airport A to arrival airport B. The same aircraft A1 then uses airport B as its new departure airport to land in new arrival airport C. The aircraft then uses airport C as its new departure airport to land back in arrival airport B. The routes of the aircraft A1 are shown at box 200. The bolded letters correspond to the departure airports for the routes taken by the aircraft A1, in this instance. Therefore, the departure airports are listed, at box 202, as follows: A, B, C, B, A, D, and A. The unique departure airports are identified, at box 204, as being, A, B, C, D (first and second states); 0055-0058, Fig. 9 — a computer system 900 for implementing the methods of forecasting operation data of an aircraft];
obtaining, at the first device, second input from the transition state model, wherein the second input indicates a state transition from the first state to a second state [0022-0028, Figs. 1-2 — The method includes using a Hidden Markov Model (HMM)… forecasted sensor parameters can then be used, for example, in models to predict aircraft engine removals for shop visit/maintenance activities… an example of data routes used by an aircraft and an example of a transition probability matrix determined based on the data routes used by the aircraft, according to an embodiment. For example, an aircraft A1 takes the following route and flies from departure airport A to arrival airport B. The same aircraft A1 then uses airport B as its new departure airport to land in new arrival airport C. The aircraft then uses airport C as its new departure airport to land back in arrival airport B. The routes of the aircraft A1 are shown at box 200. The bolded letters correspond to the departure airports for the routes taken by the aircraft A1, in this instance. Therefore, the departure airports are listed, at box 202, as follows: A, B, C, B, A, D, and A. The unique departure airports are identified, at box 204, as being, A, B, C, D (first and second states); 0055-0058, Fig. 9 — a computer system 900 for implementing the methods of forecasting operation data of an aircraft];
retrieving, at the first device, a transition confidence metric associated with the state transition from the first state to the second state from a transition confidence matrix [0022-0028, Figs. 1-2 — The method includes calculating a transition probability matrix 106 based on the historical flight data (transition probabilities/ confidence metrics are shown in the figures). For example, a pattern of departure airports and arrival airports used by the aircraft is captured in the form of a transition probability matrix 106, where the transition probability of moving from one airport to another is captured in four matrices, each computed for a selected period of time (e.g., each quarter for four quarters) during a one-year period… The method includes determining forecasted departure airports, forecasted arrival airports and time of arrival of the aircraft to the arrival airports to build a forecasted sequence of future route structure 110 — cf. paragraphs 0021-0023, 0048 of the instant specification/PGPub]; and
providing, via the first device, the second state as output [0022-0028, Figs. 1-2 — The method includes calculating a transition probability matrix 106 based on the historical flight data (transition probabilities/ confidence metrics are shown in the figures). For example, a pattern of departure airports and arrival airports used by the aircraft is captured in the form of a transition probability matrix 106, where the transition probability of moving from one airport to another is captured in four matrices, each computed for a selected period of time (e.g., each quarter for four quarters) during a one-year period… The method includes determining forecasted departure airports, forecasted arrival airports (second states) and time of arrival of the aircraft to the arrival airports to build a forecasted sequence of future route structure 110] in response to the transition confidence metric indicating that the state transition is valid.
But Gopalakrishnan fails to clearly specify providing the second state as output when a transition confidence metric indicates that the state transition is valid.
However, Colligan teaches providing the second state as output when a transition confidence metric indicates that the state transition is valid [0059, 0085-0086 — service 150 confirms an aircraft state has been reached, and sends a corresponding message or alert to Local and center flight management. The service repeats block 360 until all aircraft surface states have been confirmed and reported. In parallel with block 360, the service 150 executes one or more statistical or probability routines. The executed statistical and probability routines provide statistical data (e.g., confidence intervals and levels) and or probability data (Bayesian probability) that an upcoming aircraft surface state will occur at an expected (scheduled) time. The service 150 provides the statistical/probability information with the message or alert.]
Gopalakrishnan and Colligan are analogous art. They relate to aircraft control/management systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Gopalakrishnan, by incorporating the above limitations, as taught by Colligan.
One of ordinary skill in the art would have been motivated to do this modification in order to only provide the second state as output when the transition to the second state is certain, as suggested by Colligan [0085-0086].
Regarding claim 7, the combination of Gopalakrishnan and Colligan teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 4.
Regarding claim 16, the combination of Gopalakrishnan and Colligan teaches all the limitations of the base claims as outlined above.
Further, Gopalakrishnan teaches the dynamic system comprises flights of aircraft, and wherein the state transition from the first state to the second state includes a transition from a first phase of flight to a second phase of flight for a particular aircraft [0022-0028, Figs. 1-2 — The same aircraft A1 then uses airport B as its new departure (flight phase) airport to land in new arrival airport C].
Regarding claim 17, the combination of Gopalakrishnan and Colligan teaches all the limitations of the base claims as outlined above.
Further, Gopalakrishnan teaches the transition state model [0022-0028, Figs. 1-2 — The method includes using a Hidden Markov Model (HMM)… forecasted sensor parameters can then be used, for example, in models to predict aircraft engine removals for shop visit/maintenance activities… an example of data routes used by an aircraft and an example of a transition probability matrix determined based on the data routes used by the aircraft, according to an embodiment. For example, an aircraft A1 takes the following route and flies from departure airport A to arrival airport B. The same aircraft A1 then uses airport B as its new departure airport to land in new arrival airport C. The aircraft then uses airport C as its new departure airport to land back in arrival airport B. The routes of the aircraft A1 are shown at box 200. The bolded letters correspond to the departure airports for the routes taken by the aircraft A1, in this instance. Therefore, the departure airports are listed, at box 202, as follows: A, B, C, B, A, D, and A. The unique departure airports are identified, at box 204, as being, A, B, C, D]
Further, Colligan teaches the transition state model is configured to generate the first input and the second input [0072-0073, Fig. 2 —The moving map module 175 (in service 150)may receive aircraft positional data from a moving map system installed in the EFB 101. The state estimation module 180 receives a constant stream of inputs from other modules of the service 150]. And Colligan teaches a position input based on automatic dependent surveillance broadcast (ADS-B) data [0023 — Automatic dependent surveillance-broadcast (ADS-B) refers to a surveillance technology in which an aircraft determines its position via satellite navigation and periodically broadcasts the position, enabling the aircraft to be tracked. The aircraft position information can be received by air traffic control ground stations as well as by other aircraft to provide situational awareness and allow self-separation between and among aircraft. ADS-B is “automatic” in that it requires no pilot or external input. ADS-B is “dependent” in that it depends on data from the aircraft's onboard equipment].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Gopalakrishnan, such that the transition state model is configured to generate the first input and the second input based on automatic dependent surveillance broadcast (ADS-B) data in order to improve tracking of the aircraft, as suggested by Colligan [0023].
Regarding claim 18, Gopalakrishnan teaches a non-transitory computer-readable medium comprising instructions executable by one or more processors [0055-0058, Fig. 9 — system bus 910 may be any of several types of bus structures including a memory bus or a memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. A basic input/output (BIOS) stored in memory ROM 940, or the like… the system can use a processor and a computer-readable storage medium to store instructions that, when executed by a processor (e.g., one or more processors), cause the processor to perform a method or other specific actions.] to:
obtain first input from a transition state model for a dynamic system, wherein the first input indicates a first state of the dynamic system; obtain second input from the transition state model, wherein the second output indicates a state transition from the first state to a second state [0022-0028, Figs. 1-2 — The method includes using a Hidden Markov Model (HMM)… forecasted sensor parameters can then be used, for example, in models to predict aircraft engine removals for shop visit/maintenance activities… an example of data routes used by an aircraft and an example of a transition probability matrix determined based on the data routes used by the aircraft, according to an embodiment. For example, an aircraft A1 takes the following route and flies from departure airport A to arrival airport B. The same aircraft A1 then uses airport B as its new departure airport to land in new arrival airport C. The aircraft then uses airport C as its new departure airport to land back in arrival airport B. The routes of the aircraft A1 are shown at box 200. The bolded letters correspond to the departure airports for the routes taken by the aircraft A1, in this instance. Therefore, the departure airports are listed, at box 202, as follows: A, B, C, B, A, D, and A. The unique departure airports are identified, at box 204, as being, A, B, C, D (first and second states); 0055-0058, Fig. 9 — a computer system 900 for implementing the methods of forecasting operation data of an aircraft];
obtain second input from the transition state model, wherein the second output indicates a state transition from the first state to a second state [0022-0028, Figs. 1-2 — The method includes using a Hidden Markov Model (HMM)… forecasted sensor parameters can then be used, for example, in models to predict aircraft engine removals for shop visit/maintenance activities… an example of data routes used by an aircraft and an example of a transition probability matrix determined based on the data routes used by the aircraft, according to an embodiment. For example, an aircraft A1 takes the following route and flies from departure airport A to arrival airport B. The same aircraft A1 then uses airport B as its new departure airport to land in new arrival airport C. The aircraft then uses airport C as its new departure airport to land back in arrival airport B. The routes of the aircraft A1 are shown at box 200. The bolded letters correspond to the departure airports for the routes taken by the aircraft A1, in this instance. Therefore, the departure airports are listed, at box 202, as follows: A, B, C, B, A, D, and A. The unique departure airports are identified, at box 204, as being, A, B, C, D (first and second states); 0055-0058, Fig. 9 — a computer system 900 for implementing the methods of forecasting operation data of an aircraft];
retrieve a transition confidence metric associated with the state transition from the first state to the second state from a transition confidence matrix [0022-0028, Figs. 1-2 — The method includes calculating a transition probability matrix 106 based on the historical flight data (transition probabilities/ confidence metrics are shown in the figures). For example, a pattern of departure airports and arrival airports used by the aircraft is captured in the form of a transition probability matrix 106, where the transition probability of moving from one airport to another is captured in four matrices, each computed for a selected period of time (e.g., each quarter for four quarters) during a one-year period… The method includes determining forecasted departure airports, forecasted arrival airports and time of arrival of the aircraft to the arrival airports to build a forecasted sequence of future route structure 110 — cf. paragraphs 0021-0023, 0048 of the instant specification/PGPub]; and
provide the second state as output [0022-0028, Figs. 1-2 — The method includes calculating a transition probability matrix 106 based on the historical flight data (transition probabilities/ confidence metrics are shown in the figures). For example, a pattern of departure airports and arrival airports used by the aircraft is captured in the form of a transition probability matrix 106, where the transition probability of moving from one airport to another is captured in four matrices, each computed for a selected period of time (e.g., each quarter for four quarters) during a one-year period… The method includes determining forecasted departure airports, forecasted arrival airports (second states) and time of arrival of the aircraft to the arrival airports to build a forecasted sequence of future route structure 110] in response to the transition confidence metric indicating that the state transition is valid.
But Gopalakrishnan fails to clearly specify providing the second state as output when a transition confidence metric indicates that the state transition is valid.
However, Colligan teaches providing the second state as output when a transition confidence metric indicates that the state transition is valid [0059, 0085-0086 — service 150 confirms an aircraft state has been reached, and sends a corresponding message or alert to Local and center flight management. The service repeats block 360 until all aircraft surface states have been confirmed and reported. In parallel with block 360, the service 150 executes one or more statistical or probability routines. The executed statistical and probability routines provide statistical data (e.g., confidence intervals and levels) and or probability data (Bayesian probability) that an upcoming aircraft surface state will occur at an expected (scheduled) time. The service 150 provides the statistical/probability information with the message or alert.]
Gopalakrishnan and Colligan are analogous art. They relate to aircraft control/management systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above non-transitory computer-readable medium, as taught by Gopalakrishnan, by incorporating the above limitations, as taught by Colligan.
One of ordinary skill in the art would have been motivated to do this modification in order to only provide the second state as output when the transition to the second state is certain, as suggested by Colligan [0085-0086].
Claim(s) 3 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Gopalakrishnan and Colligan in view of Zaccaria et al. U.S. Patent No. 6088632 (hereinafter Zaccaria).
Regarding claim 3, the combination of Gopalakrishnan and Colligan teaches all the limitations of the base claims as outlined above.
Further, Gopalakrishnan teaches a model of the second state [0022-0028, Figs. 1-2 — The method includes using a Hidden Markov Model (HMM)… forecasted sensor parameters can then be used, for example, in models to predict aircraft engine removals for shop visit/maintenance activities… an example of data routes used by an aircraft and an example of a transition probability matrix determined based on the data routes used by the aircraft, according to an embodiment. For example, an aircraft A1 takes the following route and flies from departure airport A to arrival airport B. The same aircraft A1 then uses airport B as its new departure airport to land in new arrival airport C. The aircraft then uses airport C as its new departure airport to land back in arrival airport B. The routes of the aircraft A1 are shown at box 200. The bolded letters correspond to the departure airports for the routes taken by the aircraft A1, in this instance. Therefore, the departure airports are listed, at box 202, as follows: A, B, C, B, A, D, and A. The unique departure airports are identified, at box 204, as being, A, B, C, D (first and second states)].
the control system generates one or more control signals for an instance of the dynamic system based on a model of the second state used in response to receipt by the control system of the output, and wherein one or more controllers associated with the instance of the dynamic system implement the one or more control signals.
But the combination of Gopalakrishnan and Colligan fails to clearly specify the control system generates one or more control signals for an instance of the dynamic system based on the second state used in response to receipt by the control system of the output, and wherein one or more controllers associated with the instance of the dynamic system implement the one or more control signals.
However, Zaccaria teaches the control system generates one or more control signals for an instance of the dynamic system based on the second state used in response to receipt by the control system of the output, and wherein one or more controllers associated with the instance of the dynamic system implement the one or more control signals [col. 3 lines 27-42 — the engine control system for a first aircraft 1 belonging to a family of aircraft which furthermore includes at least a second aircraft, the said first aircraft having a maximum mass which is greater, within a predetermined limit, than that of the second aircraft, which has a thrust command law for each engine, generally provides for each engine of the said first aircraft, a first command law for the thrust of the said engine, specific to the said first aircraft 1 during the take-off phase T, and a second command law for the thrust of the engine, corresponding to the part of the said thrust command law applicable to the second aircraft during all the other flight phases R, means 2,22 for detecting the transition from one flight phase to another being provided in order to supply the engine with at least one control signal corresponding to one or other of the first and second thrust command laws.]
Gopalakrishnan, Colligan and Zaccaria are analogous art. They relate to aircraft control/management systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above system, as taught by the combination of Gopalakrishnan and Colligan, by incorporating the above limitations, as taught by Zaccaria.
One of ordinary skill in the art would have been motivated to do this modification in order to only provide the appropriate thrust for each aircraft/engine/flight phase combination and to avoid the need for research and development of a new thrust management law or use of the thrust command management law of the aeroplane with a higher mass, as suggested by Zaccaria [col. 1 lines 10-39, col. 3 lines 27-42].
Regarding claim 6, the combination of Gopalakrishnan and Colligan teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected under the same rationale as claim 4.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sundareswara et al. U.S. Patent Publication No. 20190096145 discloses a system and method for aircraft fault detection that employs a transition matrix with a plurality of test states.
Note that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
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/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119
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