DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-9 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/04/2024 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 1, 8 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites:
A risk evaluation device comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute the instructions to:
acquire input information which includes location information indicating a location, airframe information of the flying object, weather information, and environmental information; and
evaluate a risk in a case where the flying object is at the location, using a risk evaluation model which has been trained, based on the input information.
Claim 8 recites:
A risk evaluation method comprising:
acquiring input information which includes location information indicating a location of a flying object, airframe information of the flying object, weather information, and environmental information; and
evaluating a risk in a case where the flying object is at the location, using a risk evaluation model which has been trained, based on the input information.
Claim 9 recites:
A non-transitory computer readable recording medium storing a program, the program causing a computer to perform a process comprising:
acquiring input information which includes location information indicating a location of a flying object, airframe information of the flying object, weather information, and environmental information; and
evaluating a risk in a case where the flying object is at the location, using a risk evaluation model which has been trained, based on the input information.
Step 1: Statutory category – Yes
Claim 1 is directed to a risk evaluation device (i.e., an apparatus). Therefore, claim 1 is within at least one of the four statutory categories.
Claim 8 recites a risk evaluation method. Therefore, claim 8 is within at least one of the four statutory categories.
Claim 9 recites a non-transitory computer readable recording medium (i.e., an apparatus). Therefore, claim 9 is within at least one of the four statutory categories. .
Step 2A Prong 1: Mental process – Yes
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 following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
The examiner submits that the foregoing bolded limitation constitute a “mental process” because under its broadest reasonable interpretation the claim covers performance of the limitation in the human mind. For example, “evaluate a risk ...” in the context of the claims encompasses a person observing data collected (i.e., input data) and forming simple judgement. According, the claim recites at least one abstract idea.
Step 2A Prong 2: Practical application – No
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 idea 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 the underlined portions while the bolded portions continue to represent the “abstract idea”.
For the following reasons, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “acquire input information which includes location information indicating a location, airframe information of the flying object, weather information, and environmental information,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (i.e., a processor) to perform the process. In particular, the step of acquiring input information is recited at a high level of generality (i.e., a general means of gathering flight conditions for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Claim 1 further recites additional limitations of “one memory” and “a processor”, and claim 9 further recites an additional limitation of “a computer.” According to the specification, the memory and the processor constitute a general-purpose computer such that it represents no more than mere instructions to apply the judicial exception. The memory and the processor are recited at a high level of generality and merely automates the limitations of “acquire input information ...” and “evaluate a risk ...” The generally recited processor and memory merely describe how to generally “apply” the otherwise mental process using a generic or general-purpose computer.
Claims 1, 8 and 9 further recite an additional limitation of “using a risk evaluation model which has been trained.” According to par. [0015] of the specification, the evaluation model is trained using the processor, which is recited at a high level of generality and merely automates the evaluating step. The generally recited evaluation model merely describes how to generally “apply” the otherwise mental process using a generic or general-purpose computer.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limit on practicing the abstract idea.
Step 2B: Inventive Concept – No
Regarding Step 2B of the 2019 PEG, representative independent claims 1, 8, and 9 do 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 abstract idea into a practical application, the additional element of using a processor to perform the evaluating and training the risk evaluation model amounts to nothing more than applying the exception using a generic computer. Generally applying an exception using a general computer cannot provide an inventive concept. And as discussed above, the additional limitation of “acquire input information ...” is considered an insignificant extra-solution activity.
Further, a conclusion that an additional element is insignificant extra-solution activity 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 limitation of “acquire input information ...” is well-understood, routine, and conventional activity because the specification does not provide any indication that the processor used for performing the acquiring is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases recited 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. Hence, the claim is not patent eligible.
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.
Claims 1-2 and 5-9 are rejected under 35 U.S.C. 103 as being unpatentable over Salentiny et al. (US 2016/0260331 A1), in view of Zhang et al. (US 2022/0297726 A1).
Regarding claim 1 and similarly cited claims 8 and 9, Salentiny teaches:
A risk evaluation device (Fig. 1; [0019] “weather risk analysis and flight advisory framework 100”) comprising:
at least one memory configured to store instructions ([0069] “The systems and methods of the present invention may also be partially implemented in software that can be stored on a storage medium,”); and
at least one processor (Fig. 1; [0019] “processor 122”) configured to execute the instructions to:
acquire input information (Fig. 1; [0019] “input data 130”) which includes location information indicating a location ([0057] “the locations of the takeoff, flight path, payload operation, and landing” – These locations indicate the locations along a flight path of the UAV) of a flying object ([0011] “UAVs/RPVs”), airframe information of the flying object ([0027] “Flight and mission data 132 ... may further include many other items of information, such as craft type ...”; [0028] “Craft type may include information related to the aircraft used for the mission, and its configuration and performance characteristics. For example, the UAV/RPV may be a fixed wing aircraft or a rotary aircraft or a hybrid. Craft configuration and performance characteristics may include the number of rotors, whether it is capable of hovering, whether it is autonomous, partially autonomous, or fully/remotely piloted,—the maximum flight time on full battery capacity for minimum and maximum payload weight values assuming no payload energy draw, and minimum and maximum values for flight speed, flight speed with payload, and payload weight.”), weather information ([0021] “assess meteorological and climatological data 131 comprised of historical, recent, current or forecasted weather conditions for location(s) of the takeoff, flight path, payload dispersal area, and landing”), and environmental information ([0029] “Imagery data 137 may include satellite data that may be used to focus a mission on particular geographical areas. Imagery data 137 may be used to focus on a stressed area of a field”); and
evaluate a risk in a case where the flying object is at the location, using a risk evaluation model, based on the input information ([0025] “The present invention contemplates that many types of input data 130 may incorporated into the modeling and analytics described herein, and are within the scope of the present invention.”; [0030] “the weather risk analysis and flight advisory framework 100 of the present invention generates one or more outputs 140 from the flight condition evaluation 115 that are used by other systems, such as software, devices, services, and application programming interfaces 170 , to perform several functions. Generally, the outputs 140 can be configured to communicate signals indicative of or resultant from a flight or mission compliance status 142 , such as “fly” or “no-fly”.”; [0057] “The rules processing and weather risk analysis system and module processes a request within the weather risk analysis and flight advisory framework 100 by examining current, historical, predicted or forecasted weather conditions for the locations of the takeoff, flight path, payload operation, and landing, and retrieving the appropriate operational rules 133 pertaining to but not limited to the operator, aircraft, aircraft/flight mission, and payload. The rules processing and weather risk analysis system and module then applies one or more statistical analyses 212 to evaluate a flight condition using the meteorological and climatological data 131 and based upon the appropriate operational rules 133, and returns a mission compliance status 142 with justifications as needed, and one or more instructions 144, if needed. The statistical analyses are probability calculations to assess mission risk, and enable one or more further risk analyses that, in conjunction with the other input data 130, can be processed via one or more logic units to confirm or deny compliance at step 214 by examining the rule components against the appropriate input data 130.”).
Salentiny does not explicitly teach the risk evaluation model has been trained.
However, in the same field of endeavor, Zhang teaches a risk evaluation model has been trained ([0027] “The trained machine learning model may output a safety score in different simulations.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the risk evaluation model of Salentiny, to train the risk evaluation model, as taught by Zhang. Such modification improves and/or expands detection of unsafe and/or dangerous situations, so that the flying object may be able to better react to such situations, as stated in par. [0024] of Zhang.
Regarding claim 2, Salentiny does not specifically teach wherein the risk evaluation model is a model which has been trained using the input information and a risk value corresponding to a behavior of the flying object acquired by a simulation based on the input information.
However, Zhang teaches:
wherein the risk evaluation model is a model which has been trained using input information ([0042] “In some embodiments, the training engine 136 may feed, into a machine learning model, a first training dataset 821 that includes one or more frames, or series of frames, having conditions identified as safe and a second training dataset 822 that includes one or more frames, or series of frames, conditions identified as unsafe.”) and a risk value corresponding to a behavior of an object acquired by a simulation based on the input information ([0048] “Each of the plurality of simulations 1142, 1144, 1146 can be inputted into embedded logic 1150, which may include a machine learning model such as the machine learning model as trained by the training engine 136. The embedded logic 1150 can determine a safety score for each of the plurality of simulations 1142, 1144, 1146 ... In some embodiments, simulations that have a safety score less than a threshold value can be used to test and/or revise embedded algorithms and/or logic of autonomous vehicles. For example, in some embodiments, a threshold value for selecting simulation scenarios is 5. In this example, any simulation scenario having a safety score of 5 or less can be selected for testing embedded algorithms and/or logic of autonomous vehicles.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Salentiny, in view of Zhang, to train the risk evaluation model using input information and a risk value corresponding to a behavior of the flying object acquired by a simulation based on the input information, as taught by Zhang, in order to improve and/or expands detection of unsafe and/or dangerous situations, so that the flying object may be able to better react to such situations, as stated in par. [0024] of Zhang.
Regarding claim 5, Salentiny further teaches wherein the risk value ([0059] “The output(s) of the logic unit(s), as mentioned, is further processed in the rules processing model and may, for example, generate a final “fly” or “no-fly” rating or score for the rule being processed. This processing may be a mathematical, logical, or textual comparison or formulation to calculate the mission risk probabilities.”) corresponding to the behavior of the flying object is calculated based on a plurality of troubles related to the flying object ([0060] “risk analyses (e.g., flying this make/model with an air temperature in 40-45 degrees F. for 20 minutes has a 32.17% probability of an incident occurring, or the operator has a 98.23% success rate at 20-25 minute flight lengths) to compute a final probability and risk rating”).
Regarding claim 6, Salentiny further teaches wherein the risk value ([0059] “The output(s) of the logic unit(s), as mentioned, is further processed in the rules processing model and may, for example, generate a final “fly” or “no-fly” rating or score for the rule being processed. This processing may be a mathematical, logical, or textual comparison or formulation to calculate the mission risk probabilities.”) corresponding to the behavior corresponding to the behavior of the flying object is calculated by using a probability of occurrence of the plurality of troubles ([0060] “probability of incident occurring”) related to the flying object ([0060] “risk analyses (e.g., flying this make/model with an air temperature in 40-45 degrees F. for 20 minutes has a 32.17% probability of an incident occurring, or the operator has a 98.23% success rate at 20-25 minute flight lengths) to compute a final probability and risk rating”; [0062]).
Regarding claim 7,Salentiny further teaches wherein the risk value corresponding to the behavior of the flying object is calculated using the risk value which is set in advance based on topographical information of an area corresponding to the location of the flying object ([0029] “Imagery data 137 may include satellite data that may be used to focus a mission on particular geographical areas. Imagery data 137 may be used to focus on a stressed area of a field, for example to apply appropriate chemicals, to take higher resolution imagery of the area than a satellite can do, or to avoid spraying expensive chemical on areas where it is not needed. A flight condition evaluation 115 may generate an output that instructs the mission to proceed for that constrained geographical area. Field and farm equipment data 136 includes data collected and transmitted by machines, such as a combine in a field to which a payload is to be applied. In such an example, the combine may transmit its location, and this information would enable the rules processing and weather risk analysis initialization system and module of the weather risk analysis and flight advisory framework 100 to issue an instruction 144 to avoid the combine's area of operation. This may avoid collisions, as well as any atmospheric interference due to significant particulate matter being discharged from the combine, for example.”).
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Salentiny et al. (US 2016/0260331 A1), in view of Zhang et al. (US 2022/0297726 A1), and further in view of Sachs et al. (US 2021/0358310 A1).
Regarding claim 3, neither Salentiny nor Zhang specifically teaches wherein the risk value corresponding to the behavior of the flying object is calculated based on several risks related to the flying object.
However, in the same field of endeavor, Sachs teaches:
wherein the risk value ([0077] “total risk score”) corresponding to the behavior of the flying object ([0020] “a risk management system may take, as input, detailed flight plans, and/or other inputs relating to vehicle health and history, e.g., fuel or battery state, and may transform or process these inputs to obtain a numerical ground risk score and/or a numerical air risk score corresponding to a respective likelihood of ground collision and/or air collision.”) is calculated based on several risks related to the flying object ([0077] “In some embodiments, a total risk score can be found by averaging (or otherwise aggregating) the percentage risks of each of the output risk scores from models 252-258 in FIG. 2”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Salentiny, in view of Zhang, to determine the risk value based on several risks, as taught by Sachs. Such modification reduces or eliminates the need for human review and oversight of each aspect of flight plan, and may thereby shorten the time for risk assessment and/or pre-flight approval to hours, minutes, or even with in real time, rather than days or weeks, as stated by Sachs in [0090].
Regarding claim 4, neither Salentiny nor Zhang specifically teaches wherein the risk value corresponding to the behavior of the flying object is calculated using a weight set for each of the several risks.
However, Sachs teaches:
wherein the risk value corresponding to the behavior of the flying object is calculated using a weight set for each of the several risks ([0077] “In other embodiments, a weighted application of the different output risk scores from models 252-258 may be applied, with different types of risk being weighted more heavily or lightly than others. Another embodiment may apply a machine learning algorithm, where the particular weights applied to different risks are determined based on learned flight, geospatial, and/or temporal conditions. In one example, at certain times of the year where the probability of storms is higher, the output of a weather risk model may be weighted comparatively higher.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Salentiny, in view of Zhang, to calculate the risk value corresponding to the behavior of the flying object using a weight set for each of the several risks, as taught by Sachs. Such modification reduces or eliminates the need for human review and oversight of each aspect of flight plan, and may thereby shorten the time for risk assessment and/or pre-flight approval to hours, minutes, or even with in real time, rather than days or weeks, as stated by Sachs in [0090].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chi-Johnston et al. (US 2023/0339459 A1) teaches training and the use of a machine learning model to measure the safety of an autonomous vehicle (AV) driving in simulation.
White et al. (US 2023/0408288 A1) teaches a machine learning model for calculating risks associated with a given geographic or three-dimensional region.
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/NHI Q BUI/ Examiner, Art Unit 3656