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 .
Status of Claims
This action is in response to the claims filed 02/27/2026. Wherein, claims 1, 2, 5, 11, 13, and 15 are amended, claims 2, 4, 8, 12, 14, 18 are cancelled, and claims 21-24 are new. Claims 1, 3, 5, 9, 10, 11, 13, 15-17, 19, 20, 23, and 24 are rejected. Claims 6, 7, 16, 17, and 22 are objected to. Claim 21 is allowed.
Response to Arguments
With respect to the rejection of claim 4, now incorporated into claims 1 and 11, the Applicant argues:
The Office Action (pages 11-12), addressing claim 4, alleged that Ragothman disclosed these recitations. But Ragothman does not teach or suggest at least to "determine for each segment which of multiple predetermined ranges the GNSS error prediction corresponding to the segment is located" and to "multiply the distance of each segment by a weighting factor corresponding to the determined range to calculate a functionality weighted distance." At most, Ragothman (page 1565) discloses that "signal reliability maps are used to calculate the position MSE [means square error] at each location, which in turn is used to generate an optimal path for the [autonomous vehicle] to follow. This path is generated by minimizing the total distance traveled and MSE." At a minimum, there is no teaching or suggestion of multiplying a distance of each segment by a weighting factor corresponding to a determined range, much less where the determined range is a range of a GNSS error prediction corresponding to a segment.
Ragothaman discloses “The path planning metric f(β,α) assigns a non-negative real number corresponding to the weight of the edge from nodes β to α in G. Based on the objective function in (20), the weight is given by the position MSE at all points from nodes β and α, denoted P(β,α), multiplied by dist(p)
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” (pg. 1574) Here, the MSE is the GNSS error prediction corresponding to the segment. This is multiplied by a weighting factor associated with the edges between the nodes of the section. Therefore, the Examiner finds the above arguments unpersuasive.
The rejection of independent claim 1, and also the rejection of independent claim 11, should be withdrawn at least for the foregoing reasons. The claims depending from claims 1 or 11 are likewise patentable over the references.
This argument is unpersuasive for the reasons stated above.
New Claims
Independent claim 21 incorporates subject matter from claim 6 that was indicated to be allowable. For at least this reason, independent claim 21, as well as claims 22-24 depending therefrom, are believed to be in condition for allowance.
Independent claim 21 is indicated as allowed because it incorporates the allowable subject matter of claim 6. However, claims 22 depends from claim 6 which is objected to for depending from a rejected claim. While claims 23 and 24 depend from claim 11 and therefore do not depend from an allowed claim.
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.
Claim(s) 1, 9, 10, 11, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. (KR 20250058512 A, “Jin”, machine translated) in view of Pheiffer et al. (US 20200256686 A1, “Pheiffer”) and in further view of Ragothman et al. (Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Models and Algorithms, “Ragothaman”).
Regarding claims 1 and 11, Jin discloses apparatus for guiding a flight route of air mobility and method thereof and teaches:
A system, comprising: (The present invention relates to a flight path guidance device, i.e., a system, and method for flight mobility, and more specifically, to a technology for guiding an optimal flight path in real time – See at least ¶ [0001])
a computer that includes a processor and a memory, the memory including instructions executable by the processor to: (Referring to FIG. 11, a computing system (1000) may include at least one processor (1100), memory (1300), user interface input device (1400), user interface output device (1500), storage (1600), and network interface (1700) connected via a bus (1200) The processor (1100) may be a central processing unit (CPU) or a semiconductor device that executes processing on instructions stored in a memory (1300) and/or storage (1600)... Therefore, the steps of the method or algorithm described in connection with the embodiments disclosed herein may be directly implemented by hardware, software modules, or a combination of the two executed by the processor (1100) – See at least ¶ [0140]-[0142])
receive Global Navigation Satellite System (GNSS) error predictions corresponding to respective potential paths between a vehicle location and a specified destination; (The server (SV) can determine an area where the Dilution of Precision (DOP) between the source node and the neighboring node is greater than a preset threshold as a GNSS unavailable area... DOP can be a numerical representation of the error due to the relative geometric positions of satellites used in the GNSS system – See at least ¶ [0072]-[0076])
identify a lowest-cost path from the potential paths based on path costs (At step S330, the server (SV) can determine the flight path of the second flight mobility (AM2) so that the sum of the costs between the transit nodes connecting the departure node and the arrival node is minimized – See at least ¶ [0083]) determined based on the respective GNSS error predictions for the potential paths; and (The server (SV) can determine an area where the DOP between the source node and the neighboring node is greater than a preset threshold as a GNSS unavailable area – See at least ¶ [0075])
control a [] vehicle to operate the vehicle along the lowest-cost path. (At step S330, the server (SV) can determine the flight path of the second flight mobility (AM2) so that the sum of the costs between the transit nodes connecting the departure node and the arrival node is minimized, i.e., the lowest-cost – See at least ¶ [0083])
Jin discloses controlling a vehicle to operate autonomously along the lowest-cost path. Jin does not explicitly teach that this control includes control of a propulsion subsystem and/or a steering subsystem of the vehicle. However, Pheiffer discloses method and apparatus for determining a vehicle path and teaches:
control a propulsion subsystem and/or a steering subsystem of the vehicle to operate the vehicle along the lowest-cost path. (The aspects of the present disclosure provide a system 100 for controlling a vehicle and methods 900, 1000 (see FIGS . 9 and 10) that generate a vehicle path 180, i.e., paths that change rotational orientation of the vehicle in roll, pitch, and yaw (and that are distinct from movement of the vehicle 110 along a spatial path in, e.g., an x, y, z coordinate system), through an operational space 190 in situations where vehicle constraints are unknown and/or unpredictable. The operational space 190 is at least a three dimensional space 191 and in some aspects the operational space is a four dimensional space 192, where the vehicle 110 has six-degrees of freedom for navigating the operational space 190. The aspects of the present disclosure determine a reduced, (e.g., a lowest) cost path depending on the vehicle constraints by converting the operational space 190 to a constraint space (e.g., by employing attitude constraint masks 124), analyzing a vehicle path through the constraint space, and then implementing movement of the vehicle 110 along the vehicle path in the operational space 190 to change the rotational orientation of the vehicle – See at least ¶ [0024])
In summary, Jin discloses apparatus for guiding a flight route of air mobility and method thereof and teaches autonomously controlling a vehicle along a lowest cost path. Jin does not explicitly teach that the control includes control a propulsion subsystem and/or a steering subsystem. However, Pheiffer discloses method and apparatus for determining a vehicle path and teaches autonomously controlling a vehicle along the lowest cost path by manipulating both the x, y, z movement, i.e., propulsion, and the roll, pitch, and yaw of the vehicle, i.e., steering.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the apparatus for guiding a flight route of air mobility and method thereof of Jin to provide for the method and apparatus for determining a vehicle path, as taught in Pheiffer, to optimize vehicle movement paths changing orientation of the vehicle according to constraints of the vehicle. (At Pheiffer ¶ [0001])
The combination of Jin and Pheiffer does not explicitly teach wherein calculating the path costs includes calculating an operating domain cost, a functionality cost, and a margin cost, and summing at least the calculated operating domain cost, the calculated functionality cost, and the calculated margin cost, and wherein calculating the functionality cost includes: dividing the potential path into multiple segments; determining for each segment which of multiple predetermined ranges the GNSS error prediction corresponding to the segment is located; multiplying the distance of each segment by a weighting factor corresponding to the determined range to calculate a functionality weighted distance; and summing the functionality weighted distances of the multiple segments. However, Ragothman discloses Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Models and Algorithms and teaches:
wherein calculating the path costs includes calculating an operating domain cost, (The path planning metric f (β,α) assigns a nonnegative real number corresponding to the weight of the edge from nodes β to α in G. Based on the objective function in (20), the weight is given by the position MSE at all points from nodes β and α, denoted P(β,α), multiplied by dist(p), i.e.,
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- See at least pg. 1574) a functionality cost, (The optimization problem is expressed as the sum of the multiplication of dist(p) by MSE (p,t)
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– See at least pg. 1573) and a margin cost, (This subsection describes the calculation of the path planning constraint on the largest eigenvalue of the position estimation error covariance. The purpose of this constraint is to restrict the AGV’s path to be within the maximum position uncertainty. To this end, the largest eigenvalue of the position-estimation error covariance will be used, which specifies the length of the largest axis of the uncertainty ellipsoid. The largest eigenvalue at a particular position p and at time t, denoted λmax(p,t), is found from the upper 3 × 3 matrix block of (HTR−1H) −1, where H is calculated according to the method discussed in Section VI-A – See at least pg. 1571) and summing at least the calculated operating domain cost, the calculated functionality cost, and the calculated margin cost, and (To account for both position error and path length, the optimization cost function is chosen to be the sum of the position MSE along the path, multiplied by the distance between two adjacent points. The distance is explicitly considered in the cost function because only including position MSE could result in lengthy paths, e.g., paths that require the AGV to leave and reenter the urban environment. The optimization function constraints account for the positionbias due to cellular multipath as well as uncertainty about the AGV’s position estimate. The user-specified constraints are: threshold for position bias r¯max and threshold for position uncertainty λ¯ max. The threshold λ¯ max is used as a constraint for all points p and time t along the AGV’s path, i.e., λmax(p,t) ≤ λ¯ max. The threshold λ¯ max is also used along with r¯max to calculate a threshold on the pseudorange bias ηmax. The calculation of ηmax can be achieved from (18) by substituting the user-specified r¯max, and using λ¯ max in place of λmax[(G˜ TG˜ ) −1]. Since M¯ ≤ M, M¯ is replaced with M to calculate an upper bound that is independent of a particular location and is valid for the entire environment. The path planning generation block solves a constrained optimization problem, discussed next, and returns the AGV’s prescribed path along with a list of reliable GNSS satellites and cellular base stations to use along the path. As the AGV traverses this optimal path, it only uses signals from these reliable GNSS satellites and cellular base stations – See at least pg. 1573)
wherein calculating the functionality cost includes:
dividing the potential path into multiple segments; (the system divides the region of travel into a graph of nodes and edges, i.e., multiple segments – See at least pg. 1574)
determining for each segment which of multiple predetermined ranges the GNSS error prediction corresponding to the segment is located; (For GNSS signals, the signal reliability map is spatiotemporal, and specifies the GNSS satellites to which the AGV would have a blocked LOS for different locations at different times in the environment…The signal reliability maps are used to calculate the position MSE at each location, which in turn is used to generate an optimal path for the AGV to follow. This path is generated by minimizing the total distance traveled and MSE, while guaranteeing that the bias in the position estimate due to multipath is below a desired threshold as well as ensuring that the maximum position uncertainty is below a desired limit – See at least pg. 1565)
multiplying the distance of each segment by a weighting factor corresponding to the determined range to calculate a functionality weighted distance; and (The path planning metric f(β,α) assigns a non-negative real number corresponding to the weight of the edge from nodes β to α in G. Based on the objective function in (20), the weight is given by the position MSE at all points from nodes β and α, denoted P(β,α), multiplied by dist(p)
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” - See at least pg. 1574)
summing the functionality weighted distances of the multiple segments (The optimization problem is expressed as the sum of the multiplication of dist(p) by MSE (p,t) – See at least pg. 1573 and equation below)
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Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the apparatus for guiding a flight route of air mobility and method thereof of Jin and Pheiffer to provide for the Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Models and Algorithms, as taught in Ragothman, to guarantee a desired level of accuracy, by choosing a path that yields acceptable DOP-based uncertainty and multipath-induced biases through the position MSE metric. (At Ragothman pg. 1565)
Regarding claims 3 and 13, the combination of Jin and Pheiffer does not explicitly teach, but Ragothman further teaches:
wherein the instructions to calculate the operating domain cost include instructions to: (Ragothman is directed towards an algorithm, i.e., instructions, to perform the functions of the invention – See at least 1565)
divide the potential path into multiple segments; (the system divides the region of travel into a graph of nodes and edges, i.e., multiple segments – See at least pg. 1574)
multiply the distance of each segment by a domain weight and a GNSS error prediction corresponding to the segment to determine an operating weighted distance; and (The path planning metric f (β,α) assigns a nonnegative real number corresponding to the weight of the edge from nodes β to α in G. Based on the objective function in (20), the weight is given by the position MSE at all points from nodes β and α, denoted P(β,α), multiplied by dist(p), i.e.,
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- See at least pg. 1574)
sum the operating weighted distances of the multiple segments. (As shown in the above equation, the sum of the operated weighted distances of the multiple segments is determined.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the apparatus for guiding a flight route of air mobility and method thereof of Jin and Pheiffer to provide for the Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Models and Algorithms, as taught in Ragothman, to guarantee a desired level of accuracy, by choosing a path that yields acceptable DOP-based uncertainty and multipath-induced biases through the position MSE metric. (At Ragothman pg. 1565)
Regarding claims 5 and 15, the combination of Jin and Pheiffer does not explicitly teach, but Ragothman further teaches:
wherein the instructions to calculate the margin cost includes instructions to: (Ragothman is directed towards an algorithm, i.e., instructions, to perform the functions of the invention – See at least 1565)
divide the potential path into multiple segments; (the system divides the region of travel into a graph of nodes and edges, i.e., multiple segments – See at least pg. 1574)
multiply the distance of each segment by a margin factor calculated based on a GNSS error prediction corresponding to the segment to determine a margin weighted distance; and (This subsection describes the calculation of the path planning constraint on the largest eigenvalue of the position estimation error covariance. The purpose of this constraint is to restrict the AGV’s path to be within the maximum position uncertainty. To this end, the largest eigenvalue of the position-estimation error covariance will be used, which specifies the length of the largest axis of the uncertainty ellipsoid. The largest eigenvalue at a particular position p and at time t, denoted λmax(p,t), is found from the upper 3 × 3 matrix block of (HTR−1H) −1, where H is calculated according to the method discussed in Section VI-A – See at least pg. 1571)
sum the margin weighted distances of the multiple segments. (The second constraint in (19) can be relaxed using (18) to yield the optimization problem
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- See at least pg. 1573; the examiner notes that this equation is subject to the uncertainty bias, i.e., the margin weighted distances, of the path.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the apparatus for guiding a flight route of air mobility and method thereof of Jin and Pheiffer to provide for the Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Models and Algorithms, as taught in Ragothman, to guarantee a desired level of accuracy, by choosing a path that yields acceptable DOP-based uncertainty and multipath-induced biases through the position MSE metric. (At Ragothman pg. 1565)
Regarding claims 8 and 18, the combination of Jin and Pheiffer does not explicitly teach, but Ragothman further teaches:
wherein the instructions to calculate the functionality cost and the margin cost includes instructions to: (Ragothman is directed towards an algorithm, i.e., instructions, to perform the functions of the invention – See at least 1565)
divide the potential path into multiple segments; (the system divides the region of travel into a graph of nodes and edges, i.e., multiple segments – See at least pg. 1574)
determine for each segment which of multiple predetermined ranges the GNSS error prediction corresponding to the segment is located; (The purpose of signal reliability maps is to find the reliable measurements that can be used by the AGV, and inform the path planning generator (discussed in Section VI) of these measurements. Therefore, signal reliability maps are generated only using information that is known a priori, i.e., before the path planning generator prescribes a path to the AGV. Information that is known a priori includes 3-D building maps and other static objects in the environment. Information that is not known a priori includes pedestrians or other vehicles – See at least pg. 1567)
multiply the distance of each segment by a weighting factor corresponding to the determined range to determine a functionality weighted distance; (Formally, a path from the start to the target location is denoted π ∈ P, where P is the set of all paths. The pathπ is composed of a sequence of position indices between the start position index ps and the target pg, namely π = {ps, p1, p2,..., pg}. The optimization problem is expressed as minimize π∈P p∈π dist(p) · MSE(p,t) subject to λmax(p,t) ≤ λ¯ max rr,err 2 ≤ r¯max – See at least pg. 1573)
multiply the distance of each segment by a margin factor calculated based on a GNSS error prediction corresponding to the segment to determine a margin weighted distance; and (This subsection describes the calculation of the path planning constraint on the largest eigenvalue of the position estimation error covariance. The purpose of this constraint is to restrict the AGV’s path to be within the maximum position uncertainty. To this end, the largest eigenvalue of the position-estimation error covariance will be used, which specifies the length of the largest axis of the uncertainty ellipsoid. The largest eigenvalue at a particular position p and at time t, denoted λmax(p,t), is found from the upper 3 × 3 matrix block of (HTR−1H) −1, where H is calculated according to the method discussed in Section VI-A – See at least pg. 1571)
sum the functionality weighted distances and the margin weighted distances of the multiple segments. (Approach B is implemented as follows. The road network is modeled as a graph G similar to Approach A, except the edge weights correspond to Euclidean distance, i.e.,
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)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the apparatus for guiding a flight route of air mobility and method thereof of Jin and Pheiffer to provide for the Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Models and Algorithms, as taught in Ragothman, to guarantee a desired level of accuracy, by choosing a path that yields acceptable DOP-based uncertainty and multipath-induced biases through the position MSE metric. (At Ragothman pg. 1565)
Regarding claims 9, 19, and 23, Jin further teaches:
wherein the instructions to calculate the path costs include instructions to calculate the cost based on at least a potential path distance and a vehicle speed. (The cost can be calculated to increase in proportion to the expected flight time. That is, the cost can be calculated based on the distance between the source node and the neighboring node and the airspeed of the second flight mobility (AM2) – See at least ¶ [0068])
Regarding claims 10, 20, and 24, Jin further teaches:
wherein the instructions further comprise instructions to determine the GNSS error predictions based on historical data for particular locations and times. (The system uses an aircraft, e.g., a pathfinder, that flies prior to a second aircraft – See at least ¶ [0041]; Based on the information from this first aircraft, i.e., historical data, the system determines if the second aircraft can navigate using the GNSS or if the GNSS error is too high to use. The first and second aircraft are traveling from the same node to the same node, i.e., particular locations and times – See at least [0041]-[0044])
Allowable Subject Matter
Claims 6, 7, 16, and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
With respect to claim 1, Jin taken either individually or in combination other prior art of record fails to teach or suggest: “…calculate a probability that a GNSS error distance will exceed a selected distance threshold based on the GNSS error prediction; divide the probability by a desired probability to determine a probability weight; and exponentiate the probability weight to a selected power” in combination with the remaining elements and features of the claimed invention. It is for those reasons that the Applicant’s invention defines over the prior art of record.
Claims 7 and 17 are indicated as allowable because they depend from an allowed claim.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.L.C./Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662
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