DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/14/2025 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 10-17 the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because transitory storage medium includes signals. Signals are not patent eligible. Examiner recommends amending to say “non-transitory storage medium” as supported in Applicant’s specification.
Alice type rejection – Abstract Idea Mental Process
As to claim 1-20 the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
101 Analysis – Step 1
Claim(s) 1-20 is/are directed to a mental process of determining a motion trajectory (Process claims 1-9 and apparatus for claim 10-20).
101 Analysis – Step 2A, Prong 1
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 18 includes limitations that recite an abstract idea – mental process (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 18 recites:
A cloud-based computing system comprising:
a data storage element to maintain historical data associated with prior operation of one or more vehicles, the historical data comprising at least a first set of one or more historical vehicle states associated with prior occurrence of an anomalous condition and prior operator actions associated with a second set of one or more historical vehicle states; and
a processing system coupled to the data storage element to provide an assistance service, wherein the assistance service is configurable to:
obtain, via one or more systems onboard a vehicle, status information indicative of a current state of the vehicle;
forecast the anomalous condition is likely for the vehicle based on a relationship between the current state of the vehicle and the first set of one or more historical vehicle states associated with the prior occurrence of the anomalous condition;
identify a remedial operator action correlative to avoidance of the anomalous condition based on the prior operator actions associated with the second set of one or more historical vehicle states; and
provide an indication of the remedial operator action to an operator of the vehicle. (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”)
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “forecast” and “identify” in the context of this claim encompasses a person (navigator/atc) looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea – mental process.
101 Analysis – Step 2A, Prong 2
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”) See above.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Claim 18 includes a processing apparatus. Regarding the additional limitations of “processor” that merely describes how to generally “apply” the otherwise mental judgements in a generic or general-purpose processing environment. The processing is recited at a high level of generality and merely automates the determining process steps.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the mental process into a practical application, the additional element of using a processor and storage to perform the determining amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept.
Further, a conclusion that an additional element is insignificant extra-solution activity (obtaining data and transmitting solution) in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field. The additional limitations of processing with a processing apparatus are well-understood, routine, and conventional activities because the specification does not provide any indication that the processing apparatus is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner.
Dependent claim(s) 2-9, 11-17, and 19-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application because they merely add to the mental processing. Therefore, dependent claims 2-9, 11-17, and 19-20 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1, 10, and 18.
Therefore, claim(s) 1-20 is/are ineligible under 35 USC §101. Examiner recommends a controlling step.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by US 20140195077 A1 hereinafter Johnsen.
As to claim 1 and 10, Johnsen discloses a method of assisting operation of a vehicle, [Johnsen: abstract] the method comprising:
obtaining, via one or more systems onboard the vehicle, status information indicative of a current state of the vehicle; [Johnsen: 0016 “Aircraft telemetry data 110 can be collected by one or more known measurement systems 115 disposed on one or more aircraft 112.”]
forecasting an anomalous condition is likely for the vehicle based on a relationship between the current state of the vehicle and a first set of one or more historical vehicle states associated with prior occurrence of the anomalous condition; [Johnsen: 0200 “It has been discovered that future braking action values can be predicted from aircraft telemetry data 110 and/or aircraft braking action values from aircraft that have already landed on a runway by utilizing double exponential smoothing techniques, at least one method of which is described below.”]
analyzing a second set of one or more historical vehicle states similar to the current state of the vehicle to identify a remedial operator action correlative to avoidance of the anomalous condition based on prior operator actions associated with the second set of one or more historical vehicle states; [Johnsen: 0044-0045 Fig. 4 selecting an airport that allows avoidance of the anomalous condition] and
providing an indication of the remedial operator action to an operator of the vehicle. [Johnsen: 0044-0045, 0050, Fig. 4 selecting an airport that allows avoidance of the anomalous condition. Giving the operator directions to land in another airport.]
As to 2 and 11, Johnsen discloses wherein forecasting the anomalous condition comprises detecting when a probability of occurrence of the anomalous condition is greater than a minimum detection threshold using a forecast model to calculate the probability of occurrence as a function of input time series data streams comprising the current state of the vehicle. [Johnsen: 0007 “The computer can be adapted to utilize the telemetry inputs to predict an expected future braking action value for a third aircraft scheduled to utilize the airport runway and to selectively transmit, to a communication device, an alert notification if at least one alert threshold is met and a warning notification if at least one warning threshold is met. The computer can also be adapted to utilize a first data smoothing factor and a first trend smoothing factor to predict the expected future braking action value.”]
As to 3 and 12, Johnsen discloses further comprising training the forecast model to calculate the probability of occurrence as a function of input time series data streams comprising the current state of the vehicle using historical time series data streams comprising the first set of one or more historical vehicle states associated with the prior occurrence of the anomalous condition. [Johnsen: 0007 “The computer can be adapted to utilize the telemetry inputs to predict an expected future braking action value for a third aircraft scheduled to utilize the airport runway and to selectively transmit, to a communication device, an alert notification if at least one alert threshold is met and a warning notification if at least one warning threshold is met. The computer can also be adapted to utilize a first data smoothing factor and a first trend smoothing factor to predict the expected future braking action value.”]
As to claim 4 and 13, Johnsen discloses wherein analyzing the second set of one or more historical vehicle states similar to the current state of the vehicle comprises inputting the current state of the vehicle to a recommendation model configured to output indication of the remedial operator action, wherein the recommendation model is trained using the second set of one or more historical vehicle states. [Johnsen: 0044-0045, 0050, Fig. 4 selecting an airport that allows avoidance of the anomalous condition. Giving the operator directions to land in another airport.]
As to claim 5 and 14, Johnsen discloses further comprising obtaining, via one or more external data sources, contextual information associated with a route of the vehicle, [Johnsen: the airport landing conditions gathered from prior landings] wherein forecasting the anomalous condition comprises detecting when a probability of occurrence of the anomalous condition is greater than a minimum detection threshold using a forecast model to calculate the probability of occurrence as a function of input time series data streams comprising the current state of the vehicle and the contextual information. [Johnsen: Fig. 3 and text]
As to claim 6 and 15, Johnsen discloses wherein: the vehicle comprises an aircraft; the contextual information comprises runway condition information associated with a destination runway of a flight plan for the aircraft; [Johnsen: 0019 runway condition of POOR] the anomalous condition comprises a runway overrun; [Johnsen: 0019 impacts on stopping distance has the implication of an overrun risk] and the remedial operator action comprises a recommended pilot action to reduce likelihood of the runway overrun. [Johnsen: 0044-0045, 0050, Fig. 4 selecting an airport that allows avoidance of the anomalous condition. Giving the operator directions to land in another airport.]
As to claim 7, Johnsen discloses further comprising obtaining, via one or more external data sources, contextual information associated with a route of the vehicle, [Johnsen: 0037 the airport landing conditions gathered from prior landings] wherein analyzing the second set of one or more historical vehicle states similar to the current state of the vehicle comprises inputting the current state of the vehicle and the contextual information to a recommendation model configured to output indication of the remedial operator action, [Johnsen: fig. 4 change airport] wherein the recommendation model is trained using the second set of one or more historical vehicle states. [Johnsen: 0043 uses same process to determine the Good airport rating]
As to claim 8 and 17, Johnsen discloses wherein: the vehicle comprises an aircraft; [Johnsen: Abstract] the contextual information comprises runway condition information associated with a destination runway of a flight plan for the aircraft; [Johnsen: 0037 the airport landing conditions gathered from prior landings] and the second set of one or more historical vehicle states includes respective runway condition information for the respective historical vehicle states [Johnsen: Fig. 4. 0044-0045 the process is applied to multiple airports and flight plans are changed to accommodate landing at a safe airport.].
As to claim 9, Johnsen discloses wherein: the anomalous condition comprises a runway overrun; [Johnsen: 0019 impacts on stopping distance has the implication of an overrun risk] and the remedial operator action comprises a recommended pilot action to reduce likelihood of the runway overrun [Johnsen: Fig. 4. 0044-0045 the process is applied to multiple airports and flight plans are changed to accommodate landing at a safe airport.].
As to claim 16, Johnsen discloses wherein the instructions are configurable to cause the processing system to: obtain, via one or more external data sources, contextual information associated with a route of the vehicle; [Johnsen: 0037 the airport landing conditions gathered from prior landings] and input the current state of the vehicle and the contextual information to a recommendation model configured to output indication of the remedial operator action, [Johnsen: fig. 4 change airport] wherein the recommendation model is trained using the second set of one or more historical vehicle states. [Johnsen: 0043 uses same process to determine the Good airport rating]
As to claim 18, Johnsen discloses a cloud-based computing system [Johnsen: 0018, Fig. 1] comprising: a data storage element [Johnsen: 0018 “he one or more data handling and processing systems 120 can receive, store”] to maintain historical data associated with prior operation of one or more vehicles, the historical data comprising at least a first set of one or more historical vehicle states associated with prior occurrence of an anomalous condition and prior operator actions associated with a second set of one or more historical vehicle states; [Johnsen: 0200 “It has been discovered that future braking action values can be predicted from aircraft telemetry data 110 and/or aircraft braking action values from aircraft that have already landed on a runway by utilizing double exponential smoothing techniques, at least one method of which is described below.”] and
a processing system coupled to the data storage element to provide an assistance service, wherein the assistance service is configurable to: [Johnsen: 0018]
obtain, via one or more systems onboard a vehicle, status information indicative of a current state of the vehicle; [Johnsen: 0016 “Aircraft telemetry data 110 can be collected by one or more known measurement systems 115 disposed on one or more aircraft 112.”]
forecast the anomalous condition is likely for the vehicle based on a relationship between the current state of the vehicle and the first set of one or more historical vehicle states associated with the prior occurrence of the anomalous condition; [Johnsen: 0200 “It has been discovered that future braking action values can be predicted from aircraft telemetry data 110 and/or aircraft braking action values from aircraft that have already landed on a runway by utilizing double exponential smoothing techniques, at least one method of which is described below.”]
identify a remedial operator action correlative to avoidance of the anomalous condition based on the prior operator actions associated with the second set of one or more historical vehicle states; [Johnsen: 0044-0045, 0050, Fig. 4 selecting an airport that allows avoidance of the anomalous condition. Giving the operator directions to land in another airport.]and
provide an indication of the remedial operator action to an operator of the vehicle. [Johnsen: 0044-0045, 0050, Fig. 4 selecting an airport that allows avoidance of the anomalous condition. Giving the operator directions to land in another airport.]
As to claim 19, Johnsen discloses wherein the assistance service is configurable to: obtain, via one or more external data sources, [Johnsen: Fig. 1] contextual information associated with a route of the vehicle; [Johnsen: 0037 the airport landing conditions gathered from prior landings] and input the current state of the vehicle [Johnsen: 0016 “Aircraft telemetry data 110 can be collected by one or more known measurement systems 115 disposed on one or more aircraft 112.”]and the contextual information to a recommendation model configured to output indication of the remedial operator action, wherein the recommendation model is trained using the second set of one or more historical vehicle states to identify the remedial operator action correlative to nonoccurrence of the anomalous condition. [Johnsen: 0044-0045, 0050, Fig. 4 selecting an airport that allows avoidance of the anomalous condition. Giving the operator directions to land in another airport.]
As to claim 20, Johnsen discloses wherein: the vehicle comprises an aircraft; the contextual information comprises runway condition information associated with a destination runway of a flight plan for the aircraft; [Johnsen: 0037 the airport landing conditions gathered from prior landings] and the second set of one or more historical vehicle states includes respective runway condition information for the respective historical vehicle states. [Johnsen: Fig. 4. 0044-0045 the process is applied to multiple airports and flight plans are changed to accommodate landing at a safe airport.].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220028288 A1 An aircraft-based runway overrun awareness alerting system (ROAAS) for an aircraft primary flight display (PFD) is disclosed. In embodiments, the ROAAS is embodied aboard an aircraft and tracks the position and heading of the aircraft. Further, the ROAAS tracks the energy state of the aircraft as it approaches a runway for landing. Based on the current energy state, as well as the runway parameters, the ROAAS predicts a landing point for the aircraft along the runway. Based on this landing point, as well as the current energy state, the ROAAS determines the current likelihood of runway excursion (e.g., that the aircraft will not have sufficient runway remaining to decelerate or stop) and displays this likelihood as a dynamic graphic element not integrated into any other instruments or components displayed by the PFD.
US 20220348317 A1 An aircraft landing event system including a processor communicatively coupled with memory storing aircraft landing event data. The processor is configured to receive environment information representative of an environmental condition of a runway approached by an aircraft and receive retardation information representative of a retardation demand of the aircraft during an anticipated landing event of the aircraft on the runway. The processor is further configured to select aircraft landing event data from the memory based on the environment information and the retardation information and determine a performance indicator for the landing event based on the aircraft landing event data selected by the processor. The processor is further configured to communicate the performance indicator to a landing system of the aircraft.
US 20110144875 A1 A method and apparatus for calculating the actual runway braking coefficient of friction of an aircraft using data from the aircraft's flight data recorder or flight data management system, transmitting the data in real-time to an off-aircraft high-power computing system, calculating off-aircraft the landing aircraft's actual runway braking coefficient of friction, and reporting the calculated information to individuals and agencies including air traffic control, airport operations and maintenance, and aircraft pilots and ground crews.
The examiner has pointed out particular references contained in the prior art of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. Applicant should consider the entire prior art as applicable as to the limitations of the claims. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDERICK M BRUSHABER whose telephone number is (313)446-4839. The examiner can normally be reached Monday-Friday 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hunter Lonsberry can be reached at (571) 272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FREDERICK M BRUSHABER/
Primary Examiner
Art Unit 3665
/FREDERICK M BRUSHABER/Primary Examiner, Art Unit 3665
Prosecution Insights