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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/18/2026 has been entered.
Status of Claims
This non-final action is in response to Applicant’s amended filing of 08/18/2026.
Claims 1-20 are currently pending and have been examined. Applicant has amended claims 1-6 and 11-16.
Response to Arguments
Applicant's arguments with respect to claims 1-7, 10-17, and 20 rejected under 35 USC § 101 have been fully considered and are persuasive. The rejection under 35 USC § 101 against claims 1-7, 10-17, and 20 is withdrawn.
Applicant’s arguments with respect to claims 1-20 rejected under 35 USC § 103 have been considered but are not persuasive. The Applicant argues that Seo and Vacek does not teach or suggest the amended limitation of “generating, by the computing system, a set of data structures based on the labels created for the data points, wherein each data structure represents an excursion and includes one or more consecutive data points of the telemetry data that have labels indicating the data points are non-conformant.” The Examiner respectfully disagrees.
Regarding Seo, its deviation processing uses total deviation number, distance, and time of the drone from its planned flight path in discrete areas bounded by consecutive time intervals and are summed to determine flight path error scores (see at least ¶ [0058], [0061-0066], [0071], and [0088] and Figs. 4A-4D). These scores are rated according to Tables 1 and 2 that convey a change of air space and associated fees (see ¶ [0078-0080] and [0109] and TABLE-US-0001 and 0002) – therefore, the effect and cost of air space departures is correlated directly to the evaluated error score according to consecutive telemetry information, and are therefore presented in a data structure. Even if, arguendo, the Tables do not represent a data structure, the claimed data structure “represents an excursion and includes one or more consecutive data points of the telemetry data,” which Seo explicitly recites as part of its data collection and error analysis processing (see at least ¶ [0055-0068]). Such flight data is stored (¶ [0056]) and corresponding deviation numbers of each drone in the fleet (¶ [0071]) to a database, and therefore must at least suggest a means of structuring the data to correspond flight data and error scores to each drone. Seo’s only deficiency in the amended limitation is the data having labels that indicate that the data points are non-conformant.
Regarding Vacek, it explicitly categorizes and labels an unmanned aerial vehicle detection data set as compliant flight behavior, unintentional noncompliant flight behavior, or intentional noncompliant flight behavior to determine flight risk level and compliance (see at least ¶ [0037] and [0054-0057]). This would be reasonable to combine with the deviation processing of Seo because both inventions are directed toward using UAV flight data to determine if a UAV is complying with flight operations. One of ordinary skill in the art would be motivated to modify Seo with the feature of Vacek to help the UAV avoid restricted airspace and remain in designated areas according to regulations (see at least Vacek ¶ [0003]). Therefore, the rejection under 35 USC § 103 is maintained and reiterated below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 8-13, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Seo (US 20230035682 A1; reference provided in IDS filed 07/23/2025) in view of Vacek (US 20200043346 A1), Gu et al. (US 20220076583 A1; reference provided in IDS filed 07/23/2025), and Zhang et al. (US 20180362158 A1).
Regarding claims 1 and 11, Seo discloses a non-transitory computer-readable medium having logic stored thereon (claim 11; see at least ¶ [0115]) that, in response to execution by one or more processors of a computing system, causes the computing system to perform a computer-implemented method of efficiently and accurately monitoring aggregate conformance with operational intents for a fleet of unmanned aerial vehicles (UAVs) (claim 1; see at least ¶ [0005-0007], [0033], and [0112] discloses observing and controlling drones’ flight paths and times to avoid drone collisions), the method comprising:
receiving, by a computing system, telemetry data and operational intents for a plurality of flights during a monitoring period (see at least ¶ [0053] and [0056-0058] disclosing a path setting device including a data collection unit and an error analysis unit that collects and processes total deviation number, distance, and time of the drone from its planned flight path);
for each flight of the plurality of flights:
comparing, by the computing system, the telemetry data associated with the flight to the operational intent associated with the flight (see at least ¶ [0056-0059] disclosing an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path by comparing planned GPS coordinates against data received from the drone);
and determining, by the computing system, a level of aggregate conformance based on the set of data structures (Application’s specification ¶ [0005] defines ‘excursions’ as departures from a reserved airspace – without reading the specification into the claims, see at least ¶ [0058], [0061-0066], [0071], and [0088] disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path, where number and distance are calculated in discrete areas bounded by time intervals that are summed to determine the totals and flight path error scores).
While Seo discloses based on the comparing, determining, by the computing system, each data point of the telemetry data as conformant or non-conformant (see at least ¶ [0058-0059] and [0075] and Table 1 disclosing an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path to calculate a flight path error score and determines the change in air space required);
and generating, by the computing system, a set of data structures based on the data points, wherein each data structure represents an excursion and includes one or more consecutive data points of the telemetry data that are non-conformant (Application’s specification ¶ [0005] defines ‘excursions’ as departures from a reserved airspace – without reading the specification into the claims, see at least ¶ [0058], [0061-0066], [0071], and [0088] and TABLES 00001-00002 and Figs. 4A-4D disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path, where number and distance are calculated in discrete areas bounded by time intervals that are summed to determine the totals and flight path error scores), Seo does not explicitly disclose based on the comparing, creating, by the computing system, a label for each data point of the telemetry data that indicates whether the data point is conformant or non-conformant;
and generating, by the computing system, a set of excursions based on the labels created for the data points, wherein each excursion includes one or more consecutive data points that have labels indicating the data points are non-conformant.
However, Vacek suggests creating, by the computing system, a label for each data point of the telemetry data that indicates whether the data point is conformant or non-conformant (see at least ¶ [0037] and [0054-0057] disclosing an unmanned aerial vehicle detection and mitigation system taking a detection data set for the UAV determines flight risk level and compliance and labels it as compliant flight behavior, unintentional noncompliant flight behavior, or intentional noncompliant flight behavior);
and generating, by the computing system, a set of excursions based on the labels created for the data points, wherein each excursion includes one or more consecutive data points that have labels indicating the data points are non-conformant (see at least ¶ [0037] and [0054-0057] disclosing an unmanned aerial vehicle detection and mitigation system taking a detection data set for the UAV determines flight risk level and compliance and labels it as compliant flight behavior, unintentional noncompliant flight behavior, or intentional noncompliant flight behavior).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the labeled compliance classification of Vacek into the deviation processing of Seo with a reasonable expectation of success because both inventions are directed toward using UAV flight data to determine if a UAV is complying with flight operations. This would help the UAV avoid restricted airspace and remain in designated areas according to regulations (see at least Vacek ¶ [0003]).
While Seo discloses performing, by the computing system, one or more actions in response to the level of aggregate conformance (see at least ¶ [0080] and Table 1 disclosing the flight path error score calculated determines a corresponding adjustment in airspace); the combination of Seo and Vacek does not explicitly disclose wherein the one or more actions include at least one of:
presenting, by the computing system, a notification of the determined level of aggregate conformance to an operator;
or automatically preventing further flights by the fleet of UAVs.
However, Gu suggests presenting, by the computing system, a notification of the determined level of aggregate conformance to an operator (see at least ¶ [0060-0061] and Figs. 8-9 disclosing a user interface displaying flight information for an aircraft, including flight path deviation from a route).
Additionally, Zhang suggests automatically preventing further flights by the fleet of UAVs (see at least ¶ [0100] and [0121] disclosing preventing modifications to a UAV’s flight path when it deviates from the flight path for more than a threshold amount of time, including disrupting the autonomous flight of the UAV by making it land).
While Zhang is directed toward operating one UAV as opposed to a fleet, the features for directing a single UAV’s flight path and monitoring deviations from that path can be applied and scaled up to a fleet of UAVs without undue experimentation by merely replicating the control and monitor operations to multiple UAVs. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the user interface display of Gu and the flight disruption response of Zhang into the combination of Seo and Vacek with a reasonable expectation of success because all inventions are directed toward monitoring and ensuring an aircraft maintains a flight path with minimal deviation. This will help prevent the UAV from deviating too far from the flight path if it deviates for an extended period of time, and allow the drone operators to be aware of deviations in the drone’s flight and allow them to respond accordingly.
Regarding claims 2 and 12, Seo discloses generating the set of data structures based on the labels created for the data points includes:
adding, by the computing system, a first new data structure to the set of data structures (Application’s specification ¶ [0005] defines ‘excursions’ as departures from a reserved airspace – without reading the specification into the claims, see at least ¶ [0058], [0061-0066], [0071], and [0088] and Equation 1 disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path, where number and distance are calculated in discrete areas bounded by time intervals that are summed to determine the totals and flight path error scores);
adding, by the computing system, the first non-conformant data point to the first new data structure (Application’s specification ¶ [0005] defines ‘excursions’ as departures from a reserved airspace – without reading the specification into the claims, see at least ¶ [0058], [0061-0066], [0071], and [0088] and Equation 1 disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path, where number and distance are calculated in discrete areas bounded by time intervals that are summed to determine the totals and flight path error scores);
finding, by the computing system, one or more consecutive non-conformant data points that consecutively follow the first non-conformant data point, such that the first non-conformant data point and the one or more consecutive non-conformant data points are together less than or equal to a maximum excursion size (Application’s specification ¶ [0005] defines ‘excursions’ as departures from a reserved airspace – without reading the specification into the claims, see at least ¶ [0058], [0061-0066], [0071], [0082], and [0088] and Fig. 6 disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path, where number and distance are calculated in discrete areas bounded by time intervals that are summed to determine the totals and flight path error scores; total deviations then are compared to allocated airspaces for the drone, including a preset upper limit of airspace allocated to the drone);
and adding, by the computing system, the first non-conformant data point and the one or more consecutive non-conformant data points to the first new data structure (Application’s specification ¶ [0005] defines ‘excursions’ as departures from a reserved airspace – without reading the specification into the claims, see at least ¶ [0058], [0061-0066], [0071], and [0088] and Equation 1 disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path, where number and distance are calculated in discrete areas bounded by time intervals that are summed to determine the totals and flight path error scores).
While Seo discloses detecting, by the computing system, a first non-conformant data point of the telemetry data (Application’s specification ¶ [0005] defines ‘excursions’ as departures from a reserved airspace – without reading the specification into the claims, see at least ¶ [0058], [0061-0066], [0071], and [0088] disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path, where number and distance are calculated in discrete areas bounded by time intervals that are summed to determine the totals and flight path error scores), it does not do so based on the labels.
However, Vacek suggests detecting, by the computing system, a first non-conformant data point of the telemetry data based on the labels (see at least ¶ [0037] and [0054-0057] disclosing an unmanned aerial vehicle detection and mitigation system taking a detection data set for the UAV determines flight risk level and compliance and labels it as compliant flight behavior, unintentional noncompliant flight behavior, or intentional noncompliant flight behavior).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the labeled compliance classification of Vacek into the deviation processing of Seo with a reasonable expectation of success because both inventions are directed toward using UAV flight data to determine if a UAV is complying with flight operations. This would help the UAV avoid restricted airspace and remain in designated areas according to regulations (see at least Vacek ¶ [0003]).
Regarding claims 3 and 13, Seo discloses generating the set of data structures based on the labels created for the data points further includes:
searching, by the computing system, for a next non-conformant data point of the data points starting with a data point that consecutively follows the last non-conformant data point added to the new data structure (Application’s specification ¶ [0005] defines ‘excursions’ as departures from a reserved airspace – without reading the specification into the claims, see at least ¶ [0058], [0061-0066], [0071], and [0088] disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path, where number and distance are calculated in discrete areas bounded by time intervals that are summed to determine the totals and flight path error scores);
and in response to finding a next non-conformant data point of the data points, adding, by the computing system, a second new data structure to the set of data structures that starts with the next non-conformant data point (Application’s specification ¶ [0005] defines ‘excursions’ as departures from a reserved airspace – without reading the specification into the claims, see at least ¶ [0058], [0061-0066], [0071], and [0088] and Equation 1 disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path, where number and distance are calculated in discrete areas bounded by time intervals that are summed to determine the totals and flight path error scores).
Seo does not explicitly disclose searching and finding a non-conformant data point of the labeled data points based on the labels.
However, Vacek suggests searching and finding a non-conformant data point of the labeled data points based on the labels (see at least ¶ [0037] and [0054-0057] disclosing an unmanned aerial vehicle detection and mitigation system taking a detection data set for the UAV determines flight risk level and compliance and labels it as compliant flight behavior, unintentional noncompliant flight behavior, or intentional noncompliant flight behavior).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the labeled compliance classification of Vacek into the deviation processing of Seo with a reasonable expectation of success because both inventions are directed toward using UAV flight data to determine if a UAV is complying with flight operations. This would help the UAV avoid restricted airspace and remain in designated areas according to regulations (see at least Vacek ¶ [0003]).
Regarding claims 8 and 18, Seo suggests the determined level of aggregate conformance is the near non-conformance level or the non-conformance level (see at least ¶ [0078-0080] and Table 1 depicting the path error score mapping different score values to different airspace adjustments).
The combination of Seo and Vacek does not explicitly disclose wherein the one or more actions include:
presenting, by the computing system, a notification of the determined level of aggregate conformance to an operator.
However, Gu suggests presenting, by the computing system, a notification of the determined level of aggregate conformance to an operator (see at least ¶ [0060-0061] and Figs. 8-9 disclosing a user interface displaying flight information for an aircraft, including flight path deviation from a route).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the user interface display of Gu into the combination of Seo and Vacek with a reasonable expectation of success because all inventions are directed toward monitoring deviations in flight parameters of airborne vehicles along a designated path. This would allow the drone operators to be aware of deviations in the drone’s flight and allow them to respond accordingly.
Regarding claims 9 and 19, Seo discloses the determined level of aggregate conformance is the non-conformance level;
and wherein the actions include:
determining, by the computing system, an amount of time for which the non-conformance level has persisted (see at least ¶ [0053] and [0056-0058] disclosing a path setting device including a data collection unit and an error analysis unit that collects and processes total deviation number, distance, and time of the drone from its planned flight path).
The combination of Seo and Vacek does not explicitly disclose, in response to determining, by the computing system, that the non-conformance level has persisted for more than a threshold amount of time, automatically preventing further flights by the fleet of UAVs.
However, Zhang suggests , in response to determining, by the computing system, that the non-conformance level has persisted for more than a threshold amount of time, automatically preventing further flights by the fleet of UAVs (see at least ¶ [0100] and [0121] disclosing preventing modifications to a UAV’s flight path when it deviates from the flight path for more than a threshold amount of time, including disrupting the autonomous flight of the UAV by making it land).
While Zhang is directed toward operating one UAV as opposed to a fleet, the features for directing a single UAV’s flight path and monitoring deviations from that path can be applied and scaled up to a fleet of UAVs without undue experimentation by merely replicating the control and monitor operations to multiple UAVs. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the flight disruption response of Zhang into the combination of Seo and Vacek with a reasonable expectation of success because all inventions are directed toward monitoring and ensuring an aircraft maintains a flight path with minimal deviation. This will help prevent the UAV from deviating too far from the flight path if it deviates for an extended period of time.
Regarding claims 10 and 20, the combination of Seo and Vacek does not explicitly disclose the actions include presenting a dashboard of historical determinations of levels of aggregate conformance.
However, Gu suggests presenting a dashboard of historical determinations of levels of aggregate conformance (see at least ¶ [0060-0061] and Figs. 8-9 disclosing a user interface displaying flight information for an aircraft, including flight path deviation from a route).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the user interface display of Gu into the combination of Seo and Vacek with a reasonable expectation of success because both inventions are directed toward monitoring deviations in flight parameters of airborne vehicles along a designated path. This would allow the drone operators to be aware of deviations in the drone’s flight and allow them to respond accordingly.
Claims 4-7 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Seo in view of Vacek, Gu, and Zhang, as applied to claims 1 and 11 above, and in view of rationale of obviousness.
Regarding claims 4 and 14, Seo discloses determining the level of aggregate conformance based on the set of data structures includes:
determining, by the computing system, a total number of flight hours indicated by at least one of the operational intents or the telemetry data (see at least ¶ [0058], [0061-0066], [0071], and [0088] disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path).
The combination of Seo, Vacek, Gu, and Zhang does not explicitly disclose determining, by the computing system, a flight time conformance percentage and a per-flight-hour excursion number;
and determining, by the computing system, the level of aggregate conformance based on the flight time conformance percentage and the per-flight-hour excursion number.
However, Seo discloses a path setting device including a data collection unit and an error analysis unit that collects and processes total deviation number, distance, and time of the drone from its planned flight path (see at least ¶ [0053] and [0056-0058]). Applicant’s specification defines “characteristics” flight time conformance percentage as “divid[ing] the total duration of excursions by the total number of flight hours”, and the per-flight-hour excursion number as “divid[ing the] number of excursions by the total number of flight hours” (see ¶ [0062-0063]). These “characteristics” are composed of data that is readily gathered according to Seo, and one of ordinary skill in the art could manipulate these values mathematically into the desired characteristics. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to derive desired calculations from collected data with a reasonable expectation of success because the necessary information for performing the calculations are readily collected in Seo. One of ordinary skill may then use this gathered information or derive metrics according to design choice to characterize a drone’s deviated flight path.
Regarding claims 5 and 15, Seo discloses determining the flight time conformance percentage includes:
determining, by the computing system, a total duration of excursions based on the set data structures (see at least ¶ [0058], [0061-0066], [0071], and [0088] disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path).
The combination of Seo, Vacek, Gu, and Zhang does not explicitly disclose dividing, by the computing system, the total duration of the excursions by the total number of flight hours to determine the flight time conformance percentage.
However, Seo discloses a path setting device including a data collection unit and an error analysis unit that collects and processes total deviation number, distance, and time of the drone from its planned flight path (see at least ¶ [0053] and [0056-0058]). Applicant’s specification defines “characteristics” flight time conformance percentage as “divid[ing] the total duration of excursions by the total number of flight hours”, and the per-flight-hour excursion number as “divid[ing the] number of excursions by the total number of flight hours” (see ¶ [0062-0063]). These “characteristics” are composed of data that is readily gathered according to Seo, and one of ordinary skill in the art could manipulate these values mathematically into the desired characteristics. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to derive desired calculations from collected data with a reasonable expectation of success because the necessary information for performing the calculations are readily collected in Seo. One of ordinary skill may then use this gathered information or derive metrics according to design choice to characterize a drone’s deviated flight path.
Regarding claims 6 and 16, Seo discloses determining the per-flight-hour excursion number includes:
counting, by the computing system, a total number of excursions based on the sets of data structures (see at least ¶ [0058], [0061-0066], [0071], and [0088] disclosing a path setting device including an error analysis unit that processes total deviation number, distance, and time of the drone from its planned flight path).
The combination of Seo, Vacek, Gu, and Zhang does not explicitly disclose dividing, by the computing system, the total number of excursions by the total number of flight hours to determine the per-flight hour excursion number.
However, Seo discloses a path setting device including a data collection unit and an error analysis unit that collects and processes total deviation number, distance, and time of the drone from its planned flight path (see at least ¶ [0053] and [0056-0058]). Applicant’s specification defines “characteristics” flight time conformance percentage as “divid[ing] the total duration of excursions by the total number of flight hours”, and the per-flight-hour excursion number as “divid[ing the] number of excursions by the total number of flight hours” (see ¶ [0062-0063]). These “characteristics” are composed of data that is readily gathered according to Seo, and one of ordinary skill in the art could manipulate these values mathematically into the desired characteristics. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to derive desired calculations from collected data with a reasonable expectation of success because the necessary information for performing the calculations are readily collected in Seo. One of ordinary skill may then use this gathered information or derive metrics according to design choice to characterize a drone’s deviated flight path.
Regarding claims 7 and 17, Seo suggests determined level of aggregate conformance is one of an over-conformance level, a conformance level, a near non-conformance level, and a non-conformance level (see at least ¶ [0078-0080] and Table 1 depicting the path error score mapping different score values to different airspace adjustments);
and wherein determining the level of aggregate conformance number includes:
comparing, by the computing system, the number to a set of number thresholds associated with the over-conformance level, the conformance level, the near non-conformance level, and the non-conformance level (see at least ¶ [0078-0080] and Table 1 depicting the path error score mapping different score values to different airspace adjustments);
and determining the level of aggregate conformance to be a lowest level of aggregate conformance indicated by the comparisons (see at least ¶ [0074] and [0078-0080] and Table 1 depicting the path error score mapping different score values to different airspace adjustments, where path error score is weighted to increase the score based on environmental factors, thereby enforcing greater adjustments to airspace).
The combination of Seo, Vacek, Gu, and Zhang does not explicitly disclose wherein determining the level of aggregate conformance based on the flight time conformance percentage and the per-flight-hour excursion number includes:
comparing, by the computing system, the flight time conformance percentage to a set of percentage thresholds associated with the over-conformance level, the conformance level, the near non-conformance level, and the non-conformance level;
and comparing, by the computing system, the per-flight-hour excursion number to a set of number thresholds associated with the over-conformance level, the conformance level, the near non-conformance level, and the non-conformance level.
However, Seo discloses a path setting device including a data collection unit and an error analysis unit that collects and processes total deviation number, distance, and time of the drone from its planned flight path (see at least ¶ [0053] and [0056-0058]). Applicant’s specification defines “characteristics” flight time conformance percentage as “divid[ing] the total duration of excursions by the total number of flight hours”, and the per-flight-hour excursion number as “divid[ing the] number of excursions by the total number of flight hours” (see ¶ [0062-0063]). These “characteristics” are composed of data that is readily gathered according to Seo, and one of ordinary skill in the art could manipulate these values mathematically into the desired characteristics. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to derive desired calculations from collected data with a reasonable expectation of success because the necessary information for performing the calculations are readily collected in Seo. One of ordinary skill may then use this gathered information or derive metrics against thresholds according to design choice to characterize a drone’s deviated flight path.
Conclusion
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/J.C.B./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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