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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/15/2025 has been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a processing unit as in SPEC (paras 47) in claims 6, 8, 16 and 18.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6-7, 11-13 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable in view of Elgersma et al (US 2022/0390237 A1) in view of Hung et al (US 2024/0346928 A1).
Regarding claim 1, Elgersma discloses a system comprising: one or more aiding sensors onboard a vehicle (see Elgersma paras “0045” and “0107-0108” “The navigation filter 340 fuses the extracted statistics from position PDF filter 310 with measurement statistics from other sensors, to estimate the vehicle's position and angular orientation relative to the terrain map. These other sensors include an IMU 350 and one or more aiding sensors 360”),
an inertial measurement unit (IMU) onboard the vehicle and operative to produce inertial measurements (see Elgersma paras “0045” and “0107-0108” “The IMU 350 is operative to produce inertial measurements that are sent to an inertial navigation system (INS) 352, such as a strapdown INS, which is operatively coupled to navigation filter 340” and “Example 6 includes the navigation system of any of Examples 1-5, wherein the sensors further include: an inertial measurement unit (IMU) operative to produce inertial measurements for the vehicle; and one or more aiding sensors”),
a strapdown inertial navigation system (INS) onboard the vehicle and configured to receive the inertial measurements from the IMU, the strapdown INS configured to compute estimated kinematic state statistics of the vehicle; at least one processor operatively coupled to the one or more aiding sensors and the strapdown INS, (see Elgersma paras “0045” and “0107-0108” “The IMU 350 is operative to produce inertial measurements that are sent to an inertial navigation system (INS) 352, such as a strapdown INS, which is operatively coupled to navigation filter 340” and “Example 6 includes the navigation system of any of Examples 1-5, wherein the sensors further include: an inertial measurement unit (IMU) operative to produce inertial measurements for the vehicle; and one or more aiding sensors”),
the at least one processor hosting a feature extraction module, an association module in operative communication with the feature extraction module, and a map features module in operative communication with the association module (see Elgersma paras “0039-0042”, “0090-0095” and “0102-0104” “The position PDF filter also receives map data from a map database, such as map database 132, with the map data corresponding to one or more terrain maps of the given area (block 220). The method 200 generates a first PDF of one or more image features available in the image data (block 230), and generates a second PDF of one or more map features available in the map data (block 240)”, “The simulation of FIG. 9 used a camera image 910 of a rural area, and a map image 920 of the rural area, where k=measurement times (e.g., 1 second for frame rate of camera) for each step along the path. In particular, for k=2 to k=8, the first row of plots 912, 913, 914, 915, 916, 917, 918 show features extracted from camera image 910, and the second row of plots 922, 923, 924, 925, 926, 927, 928 show features extracted from map image 920” and “wherein the position PDF filter performs a correlation between the one or more image features in the image data and the one or more map features in the map data, to estimate position vector statistics of the vehicle relative to the terrain map”),
an onboard navigation filter in operative communication with the association module and the strapdown INS (see Elgersma paras “0045-0048” and “0109-0113” “The navigation filter 340 fuses the extracted statistics from position PDF filter 310 with measurement statistics from other sensors, to estimate the vehicle's position and angular orientation relative to the terrain map”),
and an onboard transmitter/receiver in operative communication with the strapdown INS (see Elgersma at least para “0098”),
wherein the feature extraction module is operative to extract sensor-based features from sensor measurements provided by the one or more aiding sensors (see Elgersma paras “0090-0095” and “0109-0113” “the updated state vector PDF computed by the Fokker-Planck filter. The plots of the updated state vector PDF start with multiple peaks (plot 842)”),
wherein the map features module is operative to store map-based features of a local map (see Elgersma paras “0036-0042” and “0090-0095” “The system 100 includes a position PDF filter 110, which is implemented in a processor 112 onboard a vehicle, such as an aircraft. The processor 112 is operatively coupled to at least one vision sensor 120, such as a camera, which is mounted on the vehicle. The processor 112 is also operatively coupled to a data storage unit 130 onboard the vehicle. The data storage unit 130 includes a map database 132 that stores various terrain maps”, “the extracted statistics are sent to a navigation filter to fuse with measurements from other sensors onboard the vehicle, to estimate the position and angular orientation of the vehicle relative to the terrain map” and “A data storage unit 330 onboard vehicle 302 includes a map database 332 that stores various terrain maps”),
wherein the association module is operative to associate the sensor-based features from the feature extraction module with the map-based features from the map features module to identify position vectors in the local map that are sent to the navigation filter for further processing to provide aided navigation of the vehicle (see Elgersma paras “0090-0095” and “0109-0113” “the updated state vector PDF computed by the Fokker-Planck filter. The plots of the updated state vector PDF start with multiple peaks (plot 842), but converge to a single-peak updated state vector PDF (plot 847)”, “The plot 842 (output of Fokker-Planck filter) is the updated position vector PDF based on the measurement vector PDF from plot 832. This process repeats itself for each measurement time (k=3, k=4, etc.) to provide an updated position vector PDF at each time. As the process proceeds, more information is obtained at each measurement time, so many of the earlier correlation peaks are reduced”, “the convolution of plot 912 and plot 922 at k=2. The plot 942 (output of Fokker-Planck filter) is the updated position vector PDF based on the measurement vector PDF from plot 932. This process repeats itself for each measurement time (k=3, k=4, etc.) to provide an updated position vector PDF at each time” and “an inertial navigation system (INS) operatively coupled to the navigation filter; wherein the INS is operative to receive the inertial measurements from the IMU, and generate estimated kinematic state statistics for the vehicle.”),
But Elgersma fails to explicitly teach received by the vehicle from a remote command center.
However, Hung teaches received by the vehicle from a remote command center (see Hung paras “0044-0045”, “0054”, “0067” and “0124-0126” “The data collection platform 300 may include the front end user console 202, the back end command center 204, and the onboard in-vehicle navigation device 206. The user console 202 may be implemented as a web application (“web app”) 302 with a local memory 304 configured to store local (e.g., “client-side”) state information. The command center 204 may be implemented as one or more backend services 310 that stores states as data in a database 312, which serve as a single source of truth in the platform 300. The in-vehicle navigation device 206 may be implemented as one or more onboard modules 320 with a local memory 322 configured to store onboard runtime state information” and “when the alert and the reason for the vehicle not following the planned route are uploaded to the command center 204 (step 528), this may trigger the command center 204 to generate the additional navigation instructions (e.g., step 530 may follow step 528). After receiving the alert, the command center 204 may access the planned route and, based on the current vehicle location, may determine the additional navigation instructions needed to return the vehicle to the planned route as quickly as possible (step 530). The command center 204 may then send the additional navigation instructions to the in-vehicle navigation device 206 for display (step 532)”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to receive navigation information from a remote command center” as taught by Hung (paras. [0045]- [0067]) in order to enable updated navigation information to be communicated to the vehicle based on remotely maintained route information to facilitate navigation of the vehicle along a desired route.
Regarding claim 2, Elgersma discloses wherein the one or more aiding sensors comprise one or more: cameras, light detection and ranging (LiDAR) devices, radars, acoustic sensors, magnetometers, gravitometers, radar altimeters, star trackers, or combinations thereof (see Elgersma at least paras “0030-0035” and “0045”).
Regarding claim 3, Elgersma discloses wherein the vehicle comprises an uncrewed aircraft systems (UAS), a crewed aircraft, a ground vehicle, an underground vehicle, a water vehicle, an underwater vehicle, or a spacecraft (see Elgersma at least para “0030”).
Regarding claim 6, Elgersma fails to explicitly teach wherein the remote command center comprises: a processing unit that is operative to compute the map-based features based on one or more maps stored in a local database, and periodic information received from the vehicle; and a local transmitter/receiver in operative communication with the processing unit; wherein the command center is configured to communicate with the vehicle through the local transmitter/receiver.
However, Hung teaches wherein the remote command center comprises: a processing unit that is operative to compute the map-based features based on one or more maps stored in a local database, and periodic information received from the vehicle; and a local transmitter/receiver in operative communication with the processing unit; wherein the command center is configured to communicate with the vehicle through the local transmitter/receiver (see Hung paras “0034-0036”, “0059-0061”, “0080-0081-0082” and “0151-0160” “the remaining data gathered on the mapping trip may be stored in the in-vehicle navigation device 206 and then uploaded to the command center 204 once the mapping trip is completed. Other combinations of storing the gathered data in the in-vehicle navigation device 206 and the uploading data to the command center 204 are contemplated to be within the scope of the present disclosure”, “the sensor data is gathered (step 518). In some embodiments, the sensor data may be stored in the in-vehicle navigation device 206, transmitted to the command center 204 (e.g., via wireless communication between the in-vehicle navigation device 206 and the command center 204), or a combination thereof” and “receiving additional navigation instructions from the command center, wherein the additional navigation instructions return the vehicle to the planned route”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to receive navigation information from a remote command center” as taught by Hung (paras. [0034-0036]- [0061]) in order to enable updated navigation information to be communicated to the vehicle based on remotely maintained route information to facilitate navigation of the vehicle along a desired route.
Regarding claim 7, Elgersma discloses wherein the periodic information received from the vehicle comprises the estimated kinematic state statistics of the vehicle, and sensor metadata from the one or more aiding sensors (see Elgersma paras “0030”, “0045” and “0107-0108” “allows computation of the vehicle's kinematic state statistics, including position and angular orientation with respect to the map, for use in a navigation filter”, “The IMU 350 is operative to produce inertial measurements that are sent to an inertial navigation system (INS) 352, such as a strapdown INS, which is operatively coupled to navigation filter 340” and “Example 6 includes the navigation system of any of Examples 1-5, wherein the sensors further include: an inertial measurement unit (IMU) operative to produce inertial measurements for the vehicle; and one or more aiding sensors”).
Regarding claim 11, Elgersma discloses a method comprising: providing one or more aiding sensors onboard a vehicle (see Elgersma paras “0045” and “0107-0108” “The navigation filter 340 fuses the extracted statistics from position PDF filter 310 with measurement statistics from other sensors, to estimate the vehicle's position and angular orientation relative to the terrain map. These other sensors include an IMU 350 and one or more aiding sensors 360”),
an onboard strapdown inertial navigation system (INS) that computes estimated kinematic state statistics of the vehicle, and an onboard navigation filter in operative communication with the strapdown INS (see Elgersma paras “0045” and “0107-0108” “The IMU 350 is operative to produce inertial measurements that are sent to an inertial navigation system (INS) 352, such as a strapdown INS, which is operatively coupled to navigation filter 340” and “Example 6 includes the navigation system of any of Examples 1-5, wherein the sensors further include: an inertial measurement unit (IMU) operative to produce inertial measurements for the vehicle; and one or more aiding sensors”),
and providing at least one onboard processor that hosts a feature extraction module, an association module, and a map features module, wherein the at least one onboard processor performs a process comprising: extracting sensor-based features from sensor measurements, obtained by the one or more aiding sensors, in the feature extraction module (see Elgersma paras “0039-0042”, “0090-0095” and “0102-0104” “The position PDF filter also receives map data from a map database, such as map database 132, with the map data corresponding to one or more terrain maps of the given area (block 220). The method 200 generates a first PDF of one or more image features available in the image data (block 230), and generates a second PDF of one or more map features available in the map data (block 240)”, “The simulation of FIG. 9 used a camera image 910 of a rural area, and a map image 920 of the rural area, where k=measurement times (e.g., 1 second for frame rate of camera) for each step along the path. In particular, for k=2 to k=8, the first row of plots 912, 913, 914, 915, 916, 917, 918 show features extracted from camera image 910, and the second row of plots 922, 923, 924, 925, 926, 927, 928 show features extracted from map image 920” and “wherein the position PDF filter performs a correlation between the one or more image features in the image data and the one or more map features in the map data, to estimate position vector statistics of the vehicle relative to the terrain map”),
storing map-based features of a local map (see Elgersma paras “0036-0042” and “0090-0095” “The system 100 includes a position PDF filter 110, which is implemented in a processor 112 onboard a vehicle, such as an aircraft. The processor 112 is operatively coupled to at least one vision sensor 120, such as a camera, which is mounted on the vehicle. The processor 112 is also operatively coupled to a data storage unit 130 onboard the vehicle. The data storage unit 130 includes a map database 132 that stores various terrain maps”, “the extracted statistics are sent to a navigation filter to fuse with measurements from other sensors onboard the vehicle, to estimate the position and angular orientation of the vehicle relative to the terrain map” and “A data storage unit 330 onboard vehicle 302 includes a map database 332 that stores various terrain maps”),
associating the sensor-based features with the map-based features, in the association module, to identify position vectors in the local map; and4sending the position vectors to the navigation filter for further processing to provide aided navigation for the vehicle (see Elgersma paras “0090-0095” and “0109-0113” “the updated state vector PDF computed by the Fokker-Planck filter. The plots of the updated state vector PDF start with multiple peaks (plot 842), but converge to a single-peak updated state vector PDF (plot 847)”, “The plot 842 (output of Fokker-Planck filter) is the updated position vector PDF based on the measurement vector PDF from plot 832. This process repeats itself for each measurement time (k=3, k=4, etc.) to provide an updated position vector PDF at each time. As the process proceeds, more information is obtained at each measurement time, so many of the earlier correlation peaks are reduced”, “the convolution of plot 912 and plot 922 at k=2. The plot 942 (output of Fokker-Planck filter) is the updated position vector PDF based on the measurement vector PDF from plot 932. This process repeats itself for each measurement time (k=3, k=4, etc.) to provide an updated position vector PDF at each time” and “an inertial navigation system (INS) operatively coupled to the navigation filter; wherein the INS is operative to receive the inertial measurements from the IMU, and generate estimated kinematic state statistics for the vehicle.”),
But Elgersma fails to explicitly teach received by the vehicle from a remote command center, in the map features module.
However, Hung teaches received by the vehicle from a remote command center, in the map features module (see Hung paras “0044-0045”, “0054”, “0067” and “0124-0126” “The data collection platform 300 may include the front end user console 202, the back end command center 204, and the onboard in-vehicle navigation device 206. The user console 202 may be implemented as a web application (“web app”) 302 with a local memory 304 configured to store local (e.g., “client-side”) state information. The command center 204 may be implemented as one or more backend services 310 that stores states as data in a database 312, which serve as a single source of truth in the platform 300. The in-vehicle navigation device 206 may be implemented as one or more onboard modules 320 with a local memory 322 configured to store onboard runtime state information” and “when the alert and the reason for the vehicle not following the planned route are uploaded to the command center 204 (step 528), this may trigger the command center 204 to generate the additional navigation instructions (e.g., step 530 may follow step 528). After receiving the alert, the command center 204 may access the planned route and, based on the current vehicle location, may determine the additional navigation instructions needed to return the vehicle to the planned route as quickly as possible (step 530). The command center 204 may then send the additional navigation instructions to the in-vehicle navigation device 206 for display (step 532)”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to receive navigation information from a remote command center” as taught by Hung (paras. [0045]- [0067]) in order to enable updated navigation information to be communicated to the vehicle based on remotely maintained route information to facilitate navigation of the vehicle along a desired route.
Regarding claim 12, Elgersma discloses wherein the one or more aiding sensors comprise one or more: cameras, light detection and ranging (LiDAR) devices, radars, acoustic sensors, magnetometers, gravitometers, radar altimeters, star trackers, or combinations thereof (see Elgersma at least paras “0030-0035” and “0045”).
Regarding claim 13, Elgersma discloses wherein the vehicle comprises an uncrewed aircraft systems (UAS), a crewed aircraft, a ground vehicle, an underground vehicle, a water vehicle, an underwater vehicle, or a spacecraft (see Elgersma at least para “0030”).
Regarding claim 16, Elgersma fails to explicitly teach wherein the remote command center comprises: a processing unit that computes the map-based features based on one or more maps stored in a local database, and periodic information received from the vehicle; and a local transmitter/receiver in operative communication with the processing unit; wherein the command center communicates with the vehicle through the local transmitter/receiver.
However, Hung teaches wherein the remote command center comprises: a processing unit that computes the map-based features based on one or more maps stored in a local database, and periodic information received from the vehicle; and a local transmitter/receiver in operative communication with the processing unit; wherein the command center communicates with the vehicle through the local transmitter/receiver (see Hung paras “0034-0036”, “0059-0061”, “0080-0081-0082” and “0151-0160” “the remaining data gathered on the mapping trip may be stored in the in-vehicle navigation device 206 and then uploaded to the command center 204 once the mapping trip is completed. Other combinations of storing the gathered data in the in-vehicle navigation device 206 and the uploading data to the command center 204 are contemplated to be within the scope of the present disclosure”, “the sensor data is gathered (step 518). In some embodiments, the sensor data may be stored in the in-vehicle navigation device 206, transmitted to the command center 204 (e.g., via wireless communication between the in-vehicle navigation device 206 and the command center 204), or a combination thereof” and “receiving additional navigation instructions from the command center, wherein the additional navigation instructions return the vehicle to the planned route”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to receive navigation information from a remote command center” as taught by Hung (paras. [0034-0036]- [0061]) in order to enable updated navigation information to be communicated to the vehicle based on remotely maintained route information to facilitate navigation of the vehicle along a desired route.
Regarding claim 17, Elgersma discloses wherein the periodic information received from the vehicle comprises the estimated kinematic state statistics of the vehicle, and sensor metadata from the one or more aiding sensors (see Elgersma paras “0030”, “0045” and “0107-0108” “allows computation of the vehicle's kinematic state statistics, including position and angular orientation with respect to the map, for use in a navigation filter”, “The IMU 350 is operative to produce inertial measurements that are sent to an inertial navigation system (INS) 352, such as a strapdown INS, which is operatively coupled to navigation filter 340” and “Example 6 includes the navigation system of any of Examples 1-5, wherein the sensors further include: an inertial measurement unit (IMU) operative to produce inertial measurements for the vehicle; and one or more aiding sensors”).
Claims 4-5 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable in view of Elgersma et al (US 2022/0390237 A1) in view of Hung et al (US 2024/0346928 A1) in further view of Dobs et al (US 2022/0058969 A1).
Regarding claim 4, modified Elgersma fails to explicitly teach wherein the navigation filter comprises: a measurement prediction module; a measurement error module in operative communication with the measurement prediction module; a measurement update module in operative communication with the measurement error module; and a time update module that is configured to receive initial estimated vehicle kinematic state statistics from the strapdown INS at a first time window.
However, Dobs teaches wherein the navigation filter comprises: a measurement prediction module; a measurement error module in operative communication with the measurement prediction module; a measurement update module in operative communication with the measurement error module; and a time update module that is configured to receive initial estimated vehicle kinematic state statistics from the strapdown INS at a first time window (see Dobs paras “0025-0026”, “0030-0035” and “0042-0044” “The state vector to be estimated by the filter can include combinations of the vehicle's kinematic states and sensor parameters that capture the measurement model of the IMU and at least one aiding sensor. Although embodiments of the invention apply to statistical filters generally, embodiments of the invention will be illustrated herein, for pedagogical purposes, using Kalman filter(s)” and “prediction step (or prediction block), the statistics of a state vector are propagated forward in time. In the second, or update step (or update block), the predicted statistics of a state vector are corrected or updated using the statistics of a measurement vector. The Kalman filter is based on a time propagation of the mean vector and covariance matrix of the state vector in the prediction step and on corrections of the predicted mean vector and covariance matrix of the state vector using the measurements vector and covariance matrix from the one or more aiding sensors (i.e., at least the imager) in the update step. During the update step, the state mean vector and the state covariance matrix are corrected using the measurement vectors and their covariance matrix”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to include the prediction, measurement error and measurement update” as taught by Dobs (paras. [0030-0035]) in order to provide accurate estimates of the vehicle kinematic state by propagating the vehicle state forward in time and correcting the predicted state using subsequently received sensor measurements.
Regarding claim 5, modified Elgersma fails to explicitly teach wherein: the measurement prediction module is operative to predict measurement vectors after receiving updated estimated vehicle kinematic state statistics from the time update module at a second time window; the measurement error module is operative to compute measurement error vectors based on sensor measurement statistics from one or more aiding sensor measurements, and the predicted measurement vectors from the measurement prediction module; the measurement update module is operative to compute a filter gain based on the measurement error vectors from the measurement error module and the updated estimated vehicle kinematic state statistics; and the measurement update module is configured to output filter updates for use by the strapdown INS.
However, Dobs teaches wherein: the measurement prediction module is operative to predict measurement vectors after receiving updated estimated vehicle kinematic state statistics from the time update module at a second time window; the measurement error module is operative to compute measurement error vectors based on sensor measurement statistics from one or more aiding sensor measurements, and the predicted measurement vectors from the measurement prediction module (see Dobs paras “0025-0026”, “0030-0035”, “0042-0044” and “0061” “The state vector to be estimated by the filter can include combinations of the vehicle's kinematic states and sensor parameters that capture the measurement model of the IMU and at least one aiding sensor. Although embodiments of the invention apply to statistical filters generally, embodiments of the invention will be illustrated herein, for pedagogical purposes, using Kalman filter(s)” and “prediction step (or prediction block), the statistics of a state vector are propagated forward in time. In the second, or update step (or update block), the predicted statistics of a state vector are corrected or updated using the statistics of a measurement vector. The Kalman filter is based on a time propagation of the mean vector and covariance matrix of the state vector in the prediction step and on corrections of the predicted mean vector and covariance matrix of the state vector using the measurements vector and covariance matrix from the one or more aiding sensors (i.e., at least the imager) in the update step. During the update step, the state mean vector and the state covariance matrix are corrected using the measurement vectors and their covariance matrix”),
the measurement update module is operative to compute a filter gain based on the measurement error vectors from the measurement error module and the updated estimated vehicle kinematic state statistics; and the measurement update module is configured to output filter updates for use by the strapdown INS (see Dobs paras “0025-0026”, “0030-0035”, “0042-0044” and “0061” “The term Kalman filter shall include all variations of Kalman filter(s), including for example an extended Kalman filter, a second order Kalman filter, an unscented Kalman filter, constant-gain Kalman filters, or gain scheduled Kalman filters. Other forms of Bayesian filters include particle filters or point mass filters and they can be implemented in a variety of ways to estimate a state vector for global or local applications. The state vector to be estimated by the filter can include combinations of the vehicle's kinematic states and sensor parameters that capture the measurement model of the IMU and at least one aiding sensor. Although embodiments of the invention apply to statistical filters generally” and “The prediction block 220C and the update block 220I (subsequently described) can be implemented in many different ways utilizing, e.g., different types of Kalman filters, frames of reference, aiding data, and/or state vector elements. Optionally, the state vector can include vehicle kinematic states and parameters corresponding to the IMU and at least one aiding sensor (i.e., at least the imager) and their associated measurement models. The Kalman filter can be implemented in either a direct approach or indirect approach using the error state vector”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to include the prediction, measurement error and measurement update” as taught by Dobs (paras. [0030-0035]) in order to provide accurate estimates of the vehicle kinematic state by propagating the vehicle state forward in time and correcting the predicted state using subsequently received sensor measurements.
Regarding claim 14, modified Elgersma fails to explicitly teach wherein the navigation filter comprises: a measurement prediction module; a measurement error module in operative communication with the measurement prediction module; a measurement update module in operative communication with the measurement error module; and a time update module that receives initial estimated vehicle kinematic state statistics from the strapdown INS at a first time window.
However, Dobs teaches wherein the navigation filter comprises: a measurement prediction module; a measurement error module in operative communication with the measurement prediction module; a measurement update module in operative communication with the measurement error module; and a time update module that receives initial estimated vehicle kinematic state statistics from the strapdown INS at a first time window (see Dobs paras “0025-0026”, “0030-0035” and “0042-0044” “The state vector to be estimated by the filter can include combinations of the vehicle's kinematic states and sensor parameters that capture the measurement model of the IMU and at least one aiding sensor. Although embodiments of the invention apply to statistical filters generally, embodiments of the invention will be illustrated herein, for pedagogical purposes, using Kalman filter(s)” and “prediction step (or prediction block), the statistics of a state vector are propagated forward in time. In the second, or update step (or update block), the predicted statistics of a state vector are corrected or updated using the statistics of a measurement vector. The Kalman filter is based on a time propagation of the mean vector and covariance matrix of the state vector in the prediction step and on corrections of the predicted mean vector and covariance matrix of the state vector using the measurements vector and covariance matrix from the one or more aiding sensors (i.e., at least the imager) in the update step. During the update step, the state mean vector and the state covariance matrix are corrected using the measurement vectors and their covariance matrix”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to include the prediction, measurement error and measurement update” as taught by Dobs (paras. [0030-0035]) in order to provide accurate estimates of the vehicle kinematic state by propagating the vehicle state forward in time and correcting the predicted state using subsequently received sensor measurements.
Regarding claim 15, modified Elgersma fails to explicitly teach wherein: the measurement prediction module performs a process comprising predicting measurement vectors after receiving updated estimated vehicle kinematic state statistics from the time update module at a second time window; the measurement error module performs a process comprising computing measurement error vectors based on sensor measurement statistics from one or more aiding sensor measurements, and the predicted measurement vectors from the measurement prediction module; and the measurement update module performs a process comprising: computing a filter gain based on the measurement error vectors from the measurement error module and the updated estimated vehicle kinematic state statistics; and outputting filter updates for use by the strapdown INS.
However, Dobs teaches wherein: the measurement prediction module performs a process comprising predicting measurement vectors after receiving updated estimated vehicle kinematic state statistics from the time update module at a second time window; the measurement error module performs a process comprising computing measurement error vectors based on sensor measurement statistics from one or more aiding sensor measurements, and the predicted measurement vectors from the measurement prediction module (see Dobs paras “0025-0026”, “0030-0035”, “0042-0044” and “0061” “The state vector to be estimated by the filter can include combinations of the vehicle's kinematic states and sensor parameters that capture the measurement model of the IMU and at least one aiding sensor. Although embodiments of the invention apply to statistical filters generally, embodiments of the invention will be illustrated herein, for pedagogical purposes, using Kalman filter(s)” and “prediction step (or prediction block), the statistics of a state vector are propagated forward in time. In the second, or update step (or update block), the predicted statistics of a state vector are corrected or updated using the statistics of a measurement vector. The Kalman filter is based on a time propagation of the mean vector and covariance matrix of the state vector in the prediction step and on corrections of the predicted mean vector and covariance matrix of the state vector using the measurements vector and covariance matrix from the one or more aiding sensors (i.e., at least the imager) in the update step. During the update step, the state mean vector and the state covariance matrix are corrected using the measurement vectors and their covariance matrix”),
and the measurement update module performs a process comprising: computing a filter gain based on the measurement error vectors from the measurement error module and the updated estimated vehicle kinematic state statistics; and outputting filter updates for use by the strapdown INS (see Dobs paras “0025-0026”, “0030-0035”, “0042-0044” and “0061” “The term Kalman filter shall include all variations of Kalman filter(s), including for example an extended Kalman filter, a second order Kalman filter, an unscented Kalman filter, constant-gain Kalman filters, or gain scheduled Kalman filters. Other forms of Bayesian filters include particle filters or point mass filters and they can be implemented in a variety of ways to estimate a state vector for global or local applications. The state vector to be estimated by the filter can include combinations of the vehicle's kinematic states and sensor parameters that capture the measurement model of the IMU and at least one aiding sensor. Although embodiments of the invention apply to statistical filters generally” and “The prediction block 220C and the update block 220I (subsequently described) can be implemented in many different ways utilizing, e.g., different types of Kalman filters, frames of reference, aiding data, and/or state vector elements. Optionally, the state vector can include vehicle kinematic states and parameters corresponding to the IMU and at least one aiding sensor (i.e., at least the imager) and their associated measurement models. The Kalman filter can be implemented in either a direct approach or indirect approach using the error state vector”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to include the prediction, measurement error and measurement update” as taught by Dobs (paras. [0030-0035]) in order to provide accurate estimates of the vehicle kinematic state by propagating the vehicle state forward in time and correcting the predicted state using subsequently received sensor measurements.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable in view of Elgersma et al (US 2022/0390237 A1) in view of Hung et al (US 2024/0346928 A1) in further view of Rossi et al (US 2020/0200540 A1).
Regarding claim 9, modified Elgersma fails to explicitly teach wherein the remote command center comprises an air traffic control system, an uncrewed air traffic management system, or a ground station.
However, Rossi teaches wherein the remote command center comprises an air traffic control system, an uncrewed air traffic management system, or a ground station (see Rossi paras “0025”, “0061” and “0078” “the remote station is chosen from the list consisting of a ground air traffic control center, a ground operation center, an operation center on board an aircraft, and a ground operation station for unmanned aircraft” and “the remote station is a ground air traffic control center, a ground operation center, an operation center on board an aircraft, and a ground operation station for unmanned aircraft”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of modified Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to configure the remote control center as an air traffic control system or ground station” as taught by Rossi (paras. [0025] – [0061]) in order to enable centralized monitoring, communication and control of aircraft operations based on current air traffic conditions and aircraft mission requirements.
Regarding claim 19, modified Elgersma fails to explicitly teach wherein the remote command center comprises an air traffic control system, an uncrewed air traffic management system, or a ground station.
However, Rossi teaches wherein the remote command center comprises an air traffic control system, an uncrewed air traffic management system, or a ground station (see Rossi paras “0025”, “0061” and “0078” “the remote station is chosen from the list consisting of a ground air traffic control center, a ground operation center, an operation center on board an aircraft, and a ground operation station for unmanned aircraft” and “the remote station is a ground air traffic control center, a ground operation center, an operation center on board an aircraft, and a ground operation station for unmanned aircraft”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of modified Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to configure the remote control center as an air traffic control system or ground station” as taught by Rossi (paras. [0025] – [0061]) in order to enable centralized monitoring, communication and control of aircraft operations based on current air traffic conditions and aircraft mission requirements.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable in view of Elgersma et al (US 2022/0390237 A1) in view of Hung et al (US 2024/0346928 A1) in further view of Chelaru (US 2022/0274677 A1).
Regarding claim 10, modified Elgersma fails to explicitly teach wherein the remote command center comprises a high- altitude aircraft, a marine surface vessel, or an Earth satellite.
However, Chelaru teaches wherein the remote command center comprises a high- altitude aircraft, a marine surface vessel, or an Earth satellite (see Chelaru para “0292” “The ultra-large submersible transport boat (100) claimed by the invention is built with its general command and control center (920) configured so that the ultra-large marine submersible transport boat (100), while navigating, functions as an Automatic Underwater Vehicles (AUV) with remote human commands and supervision, and when close to the supply stations (120) and delivery station (130), the ultra-large marine submersible transport boat (100) functions at the surface of the sea under direct human commands and supervision”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to provide the remote command center on a marine vessel” as taught by Chelaru (para. [0292]) in order to provide a mobile command and control platform capable of remotely supervising and controlling the vehicle from locations where a fixed ground-based command center may not be available or practical.
Regarding claim 20, modified Elgersma fails to explicitly teach wherein the remote command center comprises a high- altitude aircraft, a marine surface vessel, or an Earth satellite.
However, Chelaru teaches wherein the remote command center comprises a high- altitude aircraft, a marine surface vessel, or an Earth satellite (see Chelaru para “0292” “The ultra-large submersible transport boat (100) claimed by the invention is built with its general command and control center (920) configured so that the ultra-large marine submersible transport boat (100), while navigating, functions as an Automatic Underwater Vehicles (AUV) with remote human commands and supervision, and when close to the supply stations (120) and delivery station (130), the ultra-large marine submersible transport boat (100) functions at the surface of the sea under direct human commands and supervision”),
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Elgersma for a position probability density function filter to determine real-time measurement errors for map based vision navigation systems “to provide the remote command center on a marine vessel” as taught by Chelaru (para. [0292]) in order to provide a mobile command and control platform capable of remotely supervising and controlling the vehicle from locations where a fixed ground-based command center may not be available or practical.
Allowable Subject Matter
Claims 8 and 18 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.
Conclusion
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/HOSSAM M ABD EL LATIF/Examiner, Art Unit 3664
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