DETAILED ACTION
Status of Claims
This action is in reply to the amendments filed on 31 December 2025.
Claims 1, 11, 21 & 29 have been amended.
Claims 1-30 are currently pending and have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s arguments, filed 31 December 2025, with respect to the rejection(s) of claim(s) 1-30 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Mayberry et al. US 2022/0107183 A1.
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.
Morita + Watanabe
Claims 1 – 7, 10 – 17, 20 – 25, and 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over patent US 20240263949 A1, hereinafter Morita in view of Mayberry et al. US 2022/0107183 A1, hereafter Mayberry.
Regarding Claim 1, Morita teaches A method of determining location of a vehicle, the method comprising: obtaining a first location of the vehicle corresponding to a first time (see at least Morita P0037: the current position according to macro information and Fig. 5 S11 showing the current position determined by macro information); determining a dead reckoning location of the vehicle corresponding to a second time (see at least Morita P0026: The dead-reckoning navigation processing portion 3 may manage the calculated path as well as the various sensor information as calculated information and may send it, for example, to the current position managing portion 4 and P0027: macro-matching processing portion 2 may use a road map in a database 7 and a conventional calculated path obtained by the dead-reckoning navigation processing portion 3 as a base and P0026: The dead-reckoning navigation processing portion 3 may obtain a calculated path by calculating the direction and distance of a host vehicle from various sensor data including, for example, vehicle speed data the examiner interprets datapoints of direction, distance, and speed as examples of second time determinations as they require relative calculations between at least two times); obtaining feature information corresponding to the second time (see at least Morita P0029: feature information may include information about various structures relating to the road and Fig. 5 S12); obtaining location measurements for one or more features of an environment containing the vehicle and that are identifiable based on the feature information (see at least Morita P0029: the feature information has feature types, feature positions, their update times and Fig. 5 S16); and controlling at least one drive system of the vehicle to implement at least one autonomous driving operation based on the corrected current dead reckoning location of the vehicle (see at least Morita P0031: The vehicle control unit 5 may perform vehicle running control such as, for example, speed control and brake control when cornering based on the current position information obtained by the current position managing portion 4).
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Morita Fig. 5 depicting a repeated process of location detection, feature recognition and orientation calculations, and position correction
Morita teaches lateral component calculations but does not explicitly teach using the projections to determine both longitudinal and lateral corrections. However, Mayberry teaches determining a dead reckoning location of the vehicle corresponding to a second time (see at least Mayberry P0030: The method can include a step 504 of navigating a drone along the line using dead reckoning. During dead reckoning navigation and movement of the drone, the method can include a step 506 of detecting a fiduciary object of the fiduciary objects using an optical sensor of the drone. This can be accomplished using one or more optical sensors mounted on the drone. The method can include a step 508 of realigning a line of travel of the drone with the line when the drone encounters the fiduciary object to compensate for drift that occurs during navigation using dead reckoning); obtaining feature information corresponding to the second time (see at least Mayberry P0030: The method can include a step 504 of navigating a drone along the line using dead reckoning. During dead reckoning navigation and movement of the drone, the method can include a step 506 of detecting a fiduciary object of the fiduciary objects using an optical sensor of the drone. This can be accomplished using one or more optical sensors mounted on the drone. The method can include a step 508 of realigning a line of travel of the drone with the line when the drone encounters the fiduciary object to compensate for drift that occurs during navigation using dead reckoning);; obtaining location measurements for one or more features of an environment containing the vehicle and that are identifiable based on the feature information, each of the one or more features comprising a respective physical object in the environment and external to the vehicle (see at least Mayberry P0015: For example, the projected pattern 104 includes a line 122 having a plurality of fiduciary objects 124A-124I. A projected pattern can have fewer or more fiduciary objects as needed. The fiduciary objects 124A-124I can each have a particular geometrical configuration such as circular, bulbous, but other geometrical shapes can be used. In some cases, the fiduciary objects can be spaced a certain distance D apart (one millimeter, one centimeter, etc.). P0021: FIG. 4 illustrates another example a projected pattern 400 that can be used to accomplish dead reckoning correction in accordance with the present disclosure. The projected pattern 400 includes a plurality of lines 402 that can be oriented perpendicularly to a line of travel L.sub.T along which a drone can traverse. Adjacent ones of the plurality of lines 402 can be spaced apart to define a distance measurement D.); correcting the dead reckoning location of the vehicle based on the location measurements and based on projections of at least one of the one or more features onto a longitudinal-Lateral axis of the vehicle to determine longitudinal and lateral corrections (see at least Mayberry P0021: FIG. 4 illustrates another example a projected pattern 400 that can be used to accomplish dead reckoning correction in accordance with the present disclosure. The projected pattern 400 includes a plurality of lines 402 that can be oriented perpendicularly to a line of travel L.sub.T along which a drone can traverse. Adjacent ones of the plurality of lines 402 can be spaced apart to define a distance measurement D. Each of the plurality of lines 402 can also comprise a height value H. Further, each of the plurality of lines 402 can have a gradient that is similar to the gradient of the projected pattern 300 of FIG. 3. This gradient allows the drone to travel in two axes rather than a single axis where the drone would travel perpendicularly to the plurality of lines 402, as with the projected pattern 200 of FIG. 2. That is, more dead reckoning corrective information can be embedded in this line compared to the projected pattern of FIGS. 1-3.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Mayberry in the dead reckoning navigation field of invention to decrease the drift of a drone caused by the inherent limitations of an inertial measurement unit (IMU)/accelerometer or remove it entirely. (see at least Mayberry P0008).
Regarding Claim 2, the combination of Morita and Mayberry teaches the limitations of Claim 1 and setting the first location to the corrected dead reckoning location (see at least Morita P0037: If [the positions] do not match up, the position checking and correcting portion 11 may then correct the current position according to macro information to a current position calculated based on the feature recognition information and Fig. 5 S18); determining a second dead reckoning location of the vehicle at a third time (see at least Morita P0026: The dead-reckoning navigation processing portion 3 may manage the calculated path as well as the various sensor information as calculated information and may send it, for example, to the current position managing portion 4 and P0027: macro-matching processing portion 2 may use a road map in a database 7 and a conventional calculated path obtained by the dead-reckoning navigation processing portion 3 as a base); obtaining second feature information at the third time (see at least Morita P0029: feature information may include information about various structures relating to the road and Fig. 5 S12); obtaining second location measurements for one or more features that are identifiable based on the second feature information (see at least Morita P0029: the feature information has feature types, feature positions, their update times and Fig. 5 S16); and correcting the second dead reckoning location of the vehicle based on the second location measurements (see at least Morita P0029: if a feature is recognized as a result of image recognition, the current position can be corrected with high accuracy based on the known position of that feature and further specificity given in P0037: If [the positions] do not match up, the position checking and correcting portion 11 may then correct the current position according to macro information to a current position calculated based on the feature recognition information and Fig. 5 S18).
Morita does not explicitly teach a second and third time for data capture of the position and then the dead reckoning and feature recognition. However, Morita does depict in Fig. 5 a cycle of movement return from S18 to S11 (from position correction to obtaining the location). A person having ordinary skill in the art would recognize that this indicates the process repeats from a first and second location to a second and third, and beyond in increasing iteration counts.
Regarding Claim 3, the combination of Morita and Mayberry teaches the limitations of Claim 1 and obtaining the first location includes obtaining a location computed by a satellite position system (see at least Morita P0026: dead-reckoning navigation processing portion 3 may obtain a calculated path by calculating the direction and distance of a host vehicle from various sensor data including, for example, vehicle speed data, G data, gyro data, GPS data. Based on the direction and distance the dead-reckoning navigation processing portion 3 may then calculate the host vehicle's current position and Fig. 3 GPS 54).
Regarding Claim 4, the combination of Morita and Mayberry teaches the limitations of Claim 1 and the vehicle includes one or more sensors, and wherein obtaining location measurements for the one or more features includes obtaining sensor information from the one or more sensors (see at least Morita P0028: feature determination based on image recognition and P0032: image recognition device 8 may scan images in front (e.g. in the direction of travel) of the vehicle with, for example, a camera).
Regarding Claim 5, the combination of Morita and Mayberry teaches the limitations of Claim 4 and the one or more sensors includes a LIDAR device, a camera device, a radar device, or combinations thereof (see at least Morita P0032: image recognition device 8 may scan images in front (e.g. in the direction of travel) of the vehicle with, for example, a camera).
Regarding Claim 6, the combination of Morita and Mayberry teaches the limitations of Claim 1 and where the one or more features includes an intersection, a crosswalk, a geographic landmark, a building, width of a road, a road sign, a traffic light, a telephone post, a lamp post , or combinations thereof (see at least Morita P0029: The feature information may include information about various structures relating to the road, such as, for example, traffic signals, overpasses, road signs, streetlights, poles, electrical poles, guard rails, road shoulders, sidewalk steps, medians, manholes in the road, and/or painted features ( e.g., that indicating center lines, vehicle lanes, left/right turns, proceeding straight ahead, stop lines, bicycle crossings, crosswalks)).
Regarding Claim 7, the combination of Morita and Mayberry teaches the limitations of Claim 1 and obtaining the feature information further comprises obtaining a range or bearing, relative to the vehicle, of each of at least one of the one or more features (see at least Morita P0038: When the recognition information of a manhole is obtained as a feature, for example, the position of the manhole and the distance to it are specified from the recognition data).
Regarding Claim 11, Morita teaches A system for determining location of a vehicle, the system comprising: memory (see at least Morita P0063: each component may be implemented using a controller and or a memory, such as, for example, a CPU or by a program stored in a storage medium); at least one processor communicatively coupled to the memory (see at least Morita P0063: each component may be implemented using a controller and or a memory, such as, for example, a CPU), and configured to: obtain a first location of the vehicle corresponding to a first time (see at least Morita P0037: the current position according to macro information and Fig. 5 S11 showing the current position determined by macro information; determine a dead reckoning location of the vehicle corresponding to a second time (see at least Morita P0026: The dead-reckoning navigation processing portion 3 may manage the calculated path as well as the various sensor information as calculated information and may send it, for example, to the current position managing portion 4 and P0027: macro-matching processing portion 2 may use a road map in a database 7 and a conventional calculated path obtained by the dead-reckoning navigation processing portion 3 as a base and P0026: The dead-reckoning navigation processing portion 3 may obtain a calculated path by calculating the direction and distance of a host vehicle from various sensor data including, for example, vehicle speed data the examiner interprets datapoints of direction, distance, and speed as examples of second time determinations as they require relative calculations between at least two times); and control at least one drive system of the vehicle to implement at least one autonomous driving operation based on the corrected dead reckoning location of the vehicle (see at least Morita P0031: The vehicle control unit 5 may perform vehicle running control such as, for example, speed control and brake control when cornering based on the current position information obtained by the current position managing portion 4).
Morita teaches lateral component calculations but does not explicitly teach using the projections to determine both longitudinal and lateral corrections. However, Mayberry teaches determine a dead reckoning location of the vehicle corresponding to a second time (see at least Mayberry P0030: The method can include a step 504 of navigating a drone along the line using dead reckoning. During dead reckoning navigation and movement of the drone, the method can include a step 506 of detecting a fiduciary object of the fiduciary objects using an optical sensor of the drone. This can be accomplished using one or more optical sensors mounted on the drone. The method can include a step 508 of realigning a line of travel of the drone with the line when the drone encounters the fiduciary object to compensate for drift that occurs during navigation using dead reckoning); obtain feature information corresponding to the second time (see at least Mayberry P0030: The method can include a step 504 of navigating a drone along the line using dead reckoning. During dead reckoning navigation and movement of the drone, the method can include a step 506 of detecting a fiduciary object of the fiduciary objects using an optical sensor of the drone. This can be accomplished using one or more optical sensors mounted on the drone. The method can include a step 508 of realigning a line of travel of the drone with the line when the drone encounters the fiduciary object to compensate for drift that occurs during navigation using dead reckoning);; obtain location measurements for one or more features of an environment containing the vehicle and that are identifiable based on the feature information, each of the one or more features comprising a respective physical object in the environment and external to the vehicle (see at least Mayberry P0015: For example, the projected pattern 104 includes a line 122 having a plurality of fiduciary objects 124A-124I. A projected pattern can have fewer or more fiduciary objects as needed. The fiduciary objects 124A-124I can each have a particular geometrical configuration such as circular, bulbous, but other geometrical shapes can be used. In some cases, the fiduciary objects can be spaced a certain distance D apart (one millimeter, one centimeter, etc.). P0021: FIG. 4 illustrates another example a projected pattern 400 that can be used to accomplish dead reckoning correction in accordance with the present disclosure. The projected pattern 400 includes a plurality of lines 402 that can be oriented perpendicularly to a line of travel L.sub.T along which a drone can traverse. Adjacent ones of the plurality of lines 402 can be spaced apart to define a distance measurement D.); correct the dead reckoning location of the vehicle based on the location measurements and based on projections of at least one of the one or more features onto a longitudinal-Lateral axis of the vehicle to determine longitudinal and lateral corrections (see at least Mayberry P0021: FIG. 4 illustrates another example a projected pattern 400 that can be used to accomplish dead reckoning correction in accordance with the present disclosure. The projected pattern 400 includes a plurality of lines 402 that can be oriented perpendicularly to a line of travel L.sub.T along which a drone can traverse. Adjacent ones of the plurality of lines 402 can be spaced apart to define a distance measurement D. Each of the plurality of lines 402 can also comprise a height value H. Further, each of the plurality of lines 402 can have a gradient that is similar to the gradient of the projected pattern 300 of FIG. 3. This gradient allows the drone to travel in two axes rather than a single axis where the drone would travel perpendicularly to the plurality of lines 402, as with the projected pattern 200 of FIG. 2. That is, more dead reckoning corrective information can be embedded in this line compared to the projected pattern of FIGS. 1-3.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Mayberry in the dead reckoning navigation field of invention to decrease the drift of a drone caused by the inherent limitations of an inertial measurement unit (IMU)/accelerometer or remove it entirely. (see at least Mayberry P0008).
Regarding Claim 12, the combination of Morita and Mayberry teaches the limitations of Claim 11 and the at least one processor is further configured to: set the first location to the corrected dead reckoning location (see at least Morita P0037: If [the positions] do not match up, the position checking and correcting portion 11 may then correct the current position according to macro information to a current position calculated based on the feature recognition information and Fig. 5 S18); determine a second dead reckoning location of the vehicle at a third time (see at least Morita P0026: The dead-reckoning navigation processing portion 3 may manage the calculated path as well as the various sensor information as calculated information and may send it, for example, to the current position managing portion 4 and P0027: macro-matching processing portion 2 may use a road map in a database 7 and a conventional calculated path obtained by the dead-reckoning navigation processing portion 3 as a base); obtain second feature information at the third time (see at least Morita P0029: feature information may include information about various structures relating to the road and Fig. 5 S12); obtain second location measurements for one or more features that are identifiable based on the second feature information (see at least Morita P0029: the feature information has feature types, feature positions, their update times and Fig. 5 S16); and correct the second dead reckoning location of the vehicle based on the second location measurements (see at least Morita P0029: if a feature is recognized as a result of image recognition, the current position can be corrected with high accuracy based on the known position of that feature and further specificity given in P0037: If [the positions] do not match up, the position checking and correcting portion 11 may then correct the current position according to macro information to a current position calculated based on the feature recognition information and Fig. 5 S18).
Morita does not explicitly teach a second and third time for data capture of the position and then the dead reckoning and feature recognition. However, Morita does depict in Fig. 5 a cycle of movement return from S18 to S11 (from position correction to obtaining the location). A person having ordinary skill in the art would recognize that this indicates the process repeats from a first and second location to a second and third, and beyond in increasing iteration counts.
Regarding Claim 13, the combination of Morita and Mayberry teaches the limitations of Claim 11 and the at least one processor is configured to obtain the first location utilizing, in part, a location computed by a satellite position system (see at least Morita P0026: dead-reckoning navigation processing portion 3 may obtain a calculated path by calculating the direction and distance of a host vehicle from various sensor data including, for example, vehicle speed data, G data, gyro data, GPS data. Based on the direction and distance the dead-reckoning navigation processing portion 3 may then calculate the host vehicle's current position and Fig. 3 GPS 54).
Regarding Claim 14, the combination of Morita and Mayberry teaches the limitations of Claim 11 and the vehicle includes one or more sensors, and wherein the at least one processor is configured to obtain the first location utilizing sensor information from the one or more sensors (see at least Morita P0028: feature determination based on image recognition and P0032: image recognition device 8 may scan images in front (e.g. in the direction of travel) of the vehicle with, for example, a camera).
Regarding Claim 15, the combination of Morita and Mayberry teaches the limitations of Claim 14 and the one or more sensors includes a LIDAR device, a camera device, a radar device, or combinations thereof (see at least Morita P0032: image recognition device 8 may scan images in front (e.g. in the direction of travel) of the vehicle with, for example, a camera).
Regarding Claim 16, the combination of Morita and Mayberry teaches the limitations of Claim 11 and the one or more features includes an intersection, a crosswalk, a geographic landmark, a building, width of a road, a road sign, a traffic light, a telephone post, a lamp post, or combinations thereof (see at least Morita P0029: The feature information may include information about various structures relating to the road, such as, for example, traffic signals, overpasses, road signs, streetlights, poles, electrical poles, guard rails, road shoulders, sidewalk steps, medians, manholes in the road, and/or painted features ( e.g., that indicating center lines, vehicle lanes, left/right turns, proceeding straight ahead, stop lines, bicycle crossings, crosswalks)).
Regarding Claim 17, the combination of Morita and Mayberry teaches the limitations of Claim 11 and the at least one processor is configured to obtain feature information including a range or bearing, relative to the vehicle, of each of at least one of the one or more features (see at least Morita P0038: When the recognition information of a manhole is obtained as a feature, for example, the position of the manhole and the distance to it are specified from the recognition data).
Regarding Claim 21, Morita teaches A system for determining location of a vehicle, the system comprising: means for obtaining a first location of the vehicle corresponding to a first time (see at least Morita P0037: the current position according to macro information and Fig. 5 S11 showing the current position determined by macro information. The macro information is collected from various sensors (see also at least Morita P0026 and P0042)); means for determining a dead reckoning location of the vehicle corresponding to a second time (see at least Morita P0026: The dead-reckoning navigation processing portion 3 may manage the calculated path as well as the various sensor information as calculated information and may send it, for example, to the current position managing portion 4 and P0027: macro-matching processing portion 2 may use a road map in a database 7 and a conventional calculated path obtained by the dead-reckoning navigation processing portion 3 as a base and P0026: The dead-reckoning navigation processing portion 3 may obtain a calculated path by calculating the direction and distance of a host vehicle from various sensor data including, for example, vehicle speed data the examiner interprets datapoints of direction, distance, and speed as examples of second time determinations as they require relative calculations between at least two times); means for obtaining feature information corresponding to the second time (see at least Morita P0029: feature information may include information about various structures relating to the road and P0032: image recognition device 8… for example, a camera, may recognize paint information on the road and Fig. 5 S12); means for obtaining location measurements for one or more features of an environment containing the vehicle and that are identifiable based on the feature information (see at least Morita P0029: the feature information has feature types, feature positions, their update times and P0032: the image recognition device 8 may perform recognition processing of features designated in accordance with a demand from the micro-matching processing portion 1 and may send those recognition results, feature types, feature positions and Fig. 5 S16); and means for controlling at least one drive system of the vehicle to implement at least one autonomous driving operation based on the corrected current dead reckoning location of the vehicle (see at least Morita P0031: The vehicle control unit 5 may perform vehicle running control such as, for example, speed control and brake control when cornering based on the current position information obtained by the current position managing portion 4).
Morita teaches lateral component calculations but does not explicitly teach using the projections to determine both longitudinal and lateral corrections. However, Mayberry teaches means for determining a dead reckoning location of the vehicle corresponding to a second time (see at least Mayberry P0030: The method can include a step 504 of navigating a drone along the line using dead reckoning. During dead reckoning navigation and movement of the drone, the method can include a step 506 of detecting a fiduciary object of the fiduciary objects using an optical sensor of the drone. This can be accomplished using one or more optical sensors mounted on the drone. The method can include a step 508 of realigning a line of travel of the drone with the line when the drone encounters the fiduciary object to compensate for drift that occurs during navigation using dead reckoning); means for obtaining feature information corresponding to the second time (see at least Mayberry P0030: The method can include a step 504 of navigating a drone along the line using dead reckoning. During dead reckoning navigation and movement of the drone, the method can include a step 506 of detecting a fiduciary object of the fiduciary objects using an optical sensor of the drone. This can be accomplished using one or more optical sensors mounted on the drone. The method can include a step 508 of realigning a line of travel of the drone with the line when the drone encounters the fiduciary object to compensate for drift that occurs during navigation using dead reckoning); means for obtaining location measurements for one or more features of an environment containing the vehicle and that are identifiable based on the feature information, each of the one or more features comprising a respective physical object in the environment and external to the vehicle (see at least Mayberry P0015: For example, the projected pattern 104 includes a line 122 having a plurality of fiduciary objects 124A-124I. A projected pattern can have fewer or more fiduciary objects as needed. The fiduciary objects 124A-124I can each have a particular geometrical configuration such as circular, bulbous, but other geometrical shapes can be used. In some cases, the fiduciary objects can be spaced a certain distance D apart (one millimeter, one centimeter, etc.). P0021: FIG. 4 illustrates another example a projected pattern 400 that can be used to accomplish dead reckoning correction in accordance with the present disclosure. The projected pattern 400 includes a plurality of lines 402 that can be oriented perpendicularly to a line of travel L.sub.T along which a drone can traverse. Adjacent ones of the plurality of lines 402 can be spaced apart to define a distance measurement D.); means for correcting the dead reckoning location of the vehicle based on the location measurements and based on projections of at least one of the one or more features onto a longitudinal-Lateral axis of the vehicle to determine longitudinal and lateral corrections (see at least Mayberry P0021: FIG. 4 illustrates another example a projected pattern 400 that can be used to accomplish dead reckoning correction in accordance with the present disclosure. The projected pattern 400 includes a plurality of lines 402 that can be oriented perpendicularly to a line of travel L.sub.T along which a drone can traverse. Adjacent ones of the plurality of lines 402 can be spaced apart to define a distance measurement D. Each of the plurality of lines 402 can also comprise a height value H. Further, each of the plurality of lines 402 can have a gradient that is similar to the gradient of the projected pattern 300 of FIG. 3. This gradient allows the drone to travel in two axes rather than a single axis where the drone would travel perpendicularly to the plurality of lines 402, as with the projected pattern 200 of FIG. 2. That is, more dead reckoning corrective information can be embedded in this line compared to the projected pattern of FIGS. 1-3.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Mayberry in the dead reckoning navigation field of invention to decrease the drift of a drone caused by the inherent limitations of an inertial measurement unit (IMU)/accelerometer or remove it entirely. (see at least Mayberry P0008).
Regarding Claim 22, the combination of Morita and Mayberry teaches the limitations of Claim 21 and means for setting the first location to the corrected dead reckoning location (see at least Morita P0037: If [the positions] do not match up, the position checking and correcting portion 11 may then correct the current position according to macro information to a current position calculated based on the feature recognition information and Fig. 5 S18); means for determining a second dead reckoning location of the vehicle at a third time (see at least Morita P0026: The dead-reckoning navigation processing portion 3 may manage the calculated path as well as the various sensor information as calculated information and may send it, for example, to the current position managing portion 4 and P0027: macro-matching processing portion 2 may use a road map in a database 7 and a conventional calculated path obtained by the dead-reckoning navigation processing portion 3 as a base); means for obtaining second feature information at the third time (see at least Morita P0029: feature information may include information about various structures relating to the road and P0032: image recognition device 8… for example, a camera, may recognize paint information on the road and Fig. 5 S12);; means for obtaining second location measurements for one or more features that are identifiable based on the second feature information (see at least Morita P0029: the feature information has feature types, feature positions, their update times and P0032: the image recognition device 8 may perform recognition processing of features designated in accordance with a demand from the micro-matching processing portion 1 and may send those recognition results, feature types, feature positions and Fig. 5 S16); and means for correcting the second dead reckoning location of the vehicle based on the second location measurements(see at least Morita P0029: if a feature is recognized as a result of image recognition, the current position can be corrected with high accuracy based on the known position of that feature and further specificity given in P0037: If [the positions] do not match up, the position checking and correcting portion 11 may then correct the current position according to macro information to a current position calculated based on the feature recognition information and Fig. 5 S18).
Morita does not explicitly teach a first and second time for data capture of the position and then the dead reckoning and feature recognition. However, a person having ordinary skill in the art would recognize that position information is gathered and that dead reckoning requires an initial location and a second location, the dead reckoning process capturing the movement between the two locations with dead reckoning technologies (e.g., speed sensors, acceleration meters, gyros, etc. see at least Morita P0005 and P0026) for attaining a direction and distance to calculate a current position for any dead reckoning localization process.
Regarding Claim 23, the combination of Morita and Mayberry teaches the limitations of Claim 21 and the vehicle includes one or more sensors, and wherein obtaining location measurements for the one or more features includes means for obtaining sensor information from the one or more sensors (see at least Morita P0028: feature determination based on image recognition and P0032: image recognition device 8 may scan images in front (e.g. in the direction of travel) of the vehicle with, for example, a camera).
Regarding Claim 24, the combination of Morita and Mayberry teaches the limitations of Claim 21 and the one or more features includes an intersection, a crosswalk, a geographic landmark, a building, width of a road, a road sign, a traffic light, a telephone post, a lamp post, or combinations thereof (see at least Morita P0029: The feature information may include information about various structures relating to the road, such as, for example, traffic signals, overpasses, road signs, streetlights, poles, electrical poles, guard rails, road shoulders, sidewalk steps, medians, manholes in the road, and/or painted features ( e.g., that indicating center lines, vehicle lanes, left/right turns, proceeding straight ahead, stop lines, bicycle crossings, crosswalks)).
Regarding Claim 25, the combination of Morita and Mayberry teaches the limitations of Claim 21 and the means for obtaining feature information includes obtaining a range or bearing of at least one of the one or more features relative to the vehicle (see at least Morita P0038: When the recognition information of a manhole is obtained as a feature, for example, the position of the manhole and the distance to it are specified from the recognition data).
Regarding Claim 29, Morita teaches. A non-transitory processor-readable storage medium (see at least Morita P0063: each component may be implemented using a controller and or a memory, such as, for example, a CPU or by a program stored in a storage medium) comprising processor- readable instructions configured to cause one or more processors to determine a location of a vehicle (see at least Morita Claim 20: storing a set of program instructions executable on a data processing device and usable to manage current position information), comprising: code for obtaining a first location of the vehicle corresponding to a first time (see at least Morita P0037: the current position according to macro information and Fig. 5 S11 showing the current position determined by macro information); code for determining a dead reckoning location of the vehicle corresponding to a second time (see at least Morita P0026: The dead-reckoning navigation processing portion 3 may manage the calculated path as well as the various sensor information as calculated information and may send it, for example, to the current position managing portion 4 and P0027: macro-matching processing portion 2 may use a road map in a database 7 and a conventional calculated path obtained by the dead-reckoning navigation processing portion 3 as a base and P0026: The dead-reckoning navigation processing portion 3 may obtain a calculated path by calculating the direction and distance of a host vehicle from various sensor data including, for example, vehicle speed data the examiner interprets datapoints of direction, distance, and speed as examples of second time determinations as they require relative calculations between at least two times); code for obtaining feature information corresponding to the second time (see at least Morita P0029: feature information may include information about various structures relating to the road and Fig. 5 S12); code for obtaining location measurements for one or more features of an environment containing the vehicle that are identifiable based on the feature information (see at least Morita P0029: the feature information has feature types, feature positions, their update times and Fig. 5 S16); and code for controlling at least one drive system of the vehicle to implement at least one autonomous driving operation based on the corrected current dead reckoning location of the vehicle (see at least Morita P0031: The vehicle control unit 5 may perform vehicle running control such as, for example, speed control and brake control when cornering based on the current position information obtained by the current position managing portion 4).
Morita teaches lateral component calculations but does not explicitly teach using the projections to determine both longitudinal and lateral corrections. However, However, Mayberry teaches The non-transitory processor-readable storage medium comprising processor- readable instructions (See Mayberry P0032) code for determining a dead reckoning location of the vehicle corresponding to a second time (see at least Mayberry P0030: The method can include a step 504 of navigating a drone along the line using dead reckoning. During dead reckoning navigation and movement of the drone, the method can include a step 506 of detecting a fiduciary object of the fiduciary objects using an optical sensor of the drone. This can be accomplished using one or more optical sensors mounted on the drone. The method can include a step 508 of realigning a line of travel of the drone with the line when the drone encounters the fiduciary object to compensate for drift that occurs during navigation using dead reckoning); code for obtaining feature information corresponding to the second time (see at least Mayberry P0030: The method can include a step 504 of navigating a drone along the line using dead reckoning. During dead reckoning navigation and movement of the drone, the method can include a step 506 of detecting a fiduciary object of the fiduciary objects using an optical sensor of the drone. This can be accomplished using one or more optical sensors mounted on the drone. The method can include a step 508 of realigning a line of travel of the drone with the line when the drone encounters the fiduciary object to compensate for drift that occurs during navigation using dead reckoning); code for obtaining location measurements for one or more features of an environment containing the vehicle and that are identifiable based on the feature information, each of the one or more features comprising a respective physical object in the environment and external to the vehicle (see at least Mayberry P0015: For example, the projected pattern 104 includes a line 122 having a plurality of fiduciary objects 124A-124I. A projected pattern can have fewer or more fiduciary objects as needed. The fiduciary objects 124A-124I can each have a particular geometrical configuration such as circular, bulbous, but other geometrical shapes can be used. In some cases, the fiduciary objects can be spaced a certain distance D apart (one millimeter, one centimeter, etc.). P0021: FIG. 4 illustrates another example a projected pattern 400 that can be used to accomplish dead reckoning correction in accordance with the present disclosure. The projected pattern 400 includes a plurality of lines 402 that can be oriented perpendicularly to a line of travel L.sub.T along which a drone can traverse. Adjacent ones of the plurality of lines 402 can be spaced apart to define a distance measurement D.); code for correcting the dead reckoning location of the vehicle based on the location measurements and based on projections of at least one of the one or more features onto a longitudinal-Lateral axis of the vehicle to determine longitudinal and lateral corrections (see at least Mayberry P0021: FIG. 4 illustrates another example a projected pattern 400 that can be used to accomplish dead reckoning correction in accordance with the present disclosure. The projected pattern 400 includes a plurality of lines 402 that can be oriented perpendicularly to a line of travel L.sub.T along which a drone can traverse. Adjacent ones of the plurality of lines 402 can be spaced apart to define a distance measurement D. Each of the plurality of lines 402 can also comprise a height value H. Further, each of the plurality of lines 402 can have a gradient that is similar to the gradient of the projected pattern 300 of FIG. 3. This gradient allows the drone to travel in two axes rather than a single axis where the drone would travel perpendicularly to the plurality of lines 402, as with the projected pattern 200 of FIG. 2. That is, more dead reckoning corrective information can be embedded in this line compared to the projected pattern of FIGS. 1-3.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Mayberry in the dead reckoning navigation field of invention to decrease the drift of a drone caused by the inherent limitations of an inertial measurement unit (IMU)/accelerometer or remove it entirely. (see at least Mayberry P0008).
Regarding Claim 30, the combination of Morita, Watanabe, and Beaurepaire teach the limitations of Claim 29 and Morita teaches code for setting the first location to the corrected dead reckoning location (see at least Morita P0037: If [the positions] do not match up, the position checking and correcting portion 11 may then correct the current position according to macro information to a current position calculated based on the feature recognition information and Fig. 5 S18); code for determining a second dead reckoning location of the vehicle at a third time (see at least Morita P0026: The dead-reckoning navigation processing portion 3 may manage the calculated path as well as the various sensor information as calculated information and may send it, for example, to the current position managing portion 4 and P0027: macro-matching processing portion 2 may use a road map in a database 7 and a conventional calculated path obtained by the dead-reckoning navigation processing portion 3 as a base); code for obtaining second feature information at the third time (see at least Morita P0029: feature information may include information about various structures relating to the road and Fig. 5 S12); code for obtaining location measurements for one or more features that are identifiable based on the second feature information (see at least Morita P0029: the feature information has feature types, feature positions, their update times and Fig. 5 S16); and code for correcting the second dead reckoning location of the vehicle based on of the second location measurements (see at least Morita P0029: if a feature is recognized as a result of image recognition, the current position can be corrected with high accuracy based on the known position of that feature and further specificity given in P0037: If [the positions] do not match up, the position checking and correcting portion 11 may then correct the current position according to macro information to a current position calculated based on the feature recognition information and Fig. 5 S18).
Morita does not explicitly teach a second and third time for data capture of the position and then the dead reckoning and feature recognition. However, Morita does depict in Fig. 5 a cycle of movement return from S18 to S11 (from position correction to obtaining the location). A person having ordinary skill in the art would recognize that this indicates the process repeats from a first and second location to a second and third, and beyond in increasing iteration counts.
Morita does not explicitly teach that the processor comprises code, though a person having ordinary skill in the art would recognize code as a predictable embodiment of the method and system disclosed by Morita. However Mayberry does (see at least P0032)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Mayberry in the dead reckoning navigation field of invention to decrease the drift of a drone caused by the inherent limitations of an inertial measurement unit (IMU)/accelerometer or remove it entirely. (see at least Mayberry P0008).
Morita + Mayberry + Ghadiok
Claims 8 – 10, 18 – 20, and 26 – 28 are rejected under 35 U.S.C. 103 as being unpatentable over Morita and Mayberry in view of patent publication US 20220026232 A1, hereinafter Ghadiok.
Regarding Claim 8, the combination of Morita and Mayberry teaches the limitations of claim 1 but does not explicitly teach triangulation or trilateration, however, Ghadiok teaches correcting the dead reckoning location of the vehicle includes triangulation or trilateration (see at least Ghadiok P0059: secondary location systems that can be used include: global navigation systems (e.g., GPS), a cellular tower triangulation system, trilateration system, beacon system, dead-reckoning system (e.g., using the orientation sensors, optical flow, wheel or motor odometry measurements, etc.), or any other suitable location system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Ghadiok in the vehicle location correction field of invention to determine a higher accuracy or precise location (see at least Ghadiok P0041).
Regarding Claim 9, the combination of Morita and Mayberry teaches obtaining location measurements for one or more features includes sensor fusion (see at least Morita P0063: one or more of the components may be further divided and/or combined as necessary the examiner interprets combining components such as sensors as an example of fusion).
Ghadiok further teaches obtaining location measurements for one or more features includes sensor fusion (see at least Ghadiok P0070: information from multiple feature detectors can be fused).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Ghadiok in the vehicle location correction field of invention to combine/fuse sensor data for the advantage of increasing the accuracy of the processing modules (see at least Ghadiok P0070).
Regarding Claim 10, Morita teaches the limitations of claim 1 but does not explicitly teach using statistical bias or neural network to predict error and correct location. However, Ghadiok teaches predicting error in the dead reckoning comprises using statistical bias or a neural network (see at least Ghadiok P0041: When dead-reckoning systems are used, the high-precision location can be used to correct or eliminate drift ( e.g., location error) and/or reset the reference point for the dead-reckoning system (e.g., reset the location error, set the location error to a zero value or another suitable value, etc.)) and P0040: The method can be performed at a predetermined frequency, in response to a localization error (e.g., estimated, calculated) and P0090: the system position relative to the landmark is determined by applying a CNN or other neural network to the recorded signals the examiner interprets calculated localization error as an example of a prediction of upcoming error).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Ghadiok in the vehicle location correction field of invention to use a neural network for the advantage of validation, calibration, and to increase the accuracy of the modules (Ghadiok P0070 and P0071).
Regarding Claim 18, the combination of Morita and Mayberry teaches the limitations of claim 11 but does not explicitly teach triangulation or trilateration, however, Ghadiok teaches at least one processor is configured to correct the dead reckoning location using, at least in part, triangulation or trilateration (see at least Ghadiok P0059: secondary location systems that can be used include: global navigation systems (e.g., GPS), a cellular tower triangulation system, trilateration system, beacon system, dead-reckoning system (e.g., using the orientation sensors, optical flow, wheel or motor odometry measurements, etc.), or any other suitable location system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Ghadiok in the vehicle location correction field of invention to determine a higher accuracy or precise location (see at least Ghadiok P0041).
Regarding Claim 19, the combination of Morita and Mayberry teaches the limitations of Claim 11 and at least one processor is configured to obtain location measurements for one or more features using, at least in part, sensor fusion (see at least Morita P0063: one or more of the components may be further divided and/or combined as necessary the examiner interprets combining components such as sensors as an example of fusion).
Ghadiok further teaches obtain location measurements for one or more features using, at least in part, sensor fusion (see at least Ghadiok P0070: information from multiple feature detectors can be fused).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Ghadiok in the vehicle location correction field of invention to combine/fuse sensor data for the advantage of increasing the accuracy of the processing modules (see at least Ghadiok P0070).
Regarding Claim 20, Morita teaches the limitations of claim 1 but does not explicitly teach using statistical bias or neural network to predict error and correct location. However, Ghadiok teaches to predict error in the dead reckoning the at least one processor is configured to: use statistical bias or a neural network (see at least Ghadiok P0041: When dead-reckoning systems are used, the high-precision location can be used to correct or eliminate drift ( e.g., location error) and/or reset the reference point for the dead-reckoning system (e.g., reset the location error, set the location error to a zero value or another suitable value, etc.)) and P0040: The method can be performed at a predetermined frequency, in response to a localization error (e.g., estimated, calculated) and P0090: the system position relative to the landmark is determined by applying a CNN or other neural network to the recorded signals the examiner interprets calculated localization error as an example of a prediction of upcoming error).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Ghadiok in the vehicle location correction field of invention to use a neural network for the advantage of validation, calibration, and to increase the accuracy of the modules (Ghadiok P0070 and P0071).
Regarding Claim 26, the combination of Morita and Mayberry teaches the limitations of claim 21 but does not explicitly teach triangulation or trilateration, however, Ghadiok teaches the means for correcting the dead reckoning location of the vehicle includes triangulation or trilateration (see at least Ghadiok P0059: secondary location systems that can be used include: global navigation systems (e.g., GPS), a cellular tower triangulation system, trilateration system, beacon system, dead-reckoning system (e.g., using the orientation sensors, optical flow, wheel or motor odometry measurements, etc.), or any other suitable location system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Ghadiok in the vehicle location correction field of invention to determine a higher accuracy or precise location (see at least Ghadiok P0041).
Regarding Claim 27, the combination of Morita and Mayberry teaches the limitations of Claim 21 and the means for obtaining location measurements for one or more features includes sensor fusion (see at least Morita P0063: one or more of the components may be further divided and/or combined as necessary the examiner interprets combining components such as sensors as an example of fusion).
Ghadiok further teaches the means for obtaining location measurements for one or more features includes sensor fusion (see at least Ghadiok P0070: information from multiple feature detectors can be fused).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Ghadiok in the vehicle location correction field of invention to combine/fuse sensor data for the advantage of increasing the accuracy of the processing modules (see at least Ghadiok P0070).
Regarding Claim 28, Morita teaches the limitations of claim 1 but does not explicitly teach using statistical bias or neural network to predict error and correct location. However, Ghadiok teaches the means for predicting error in the dead reckoning comprise means for using statistical bias or a neural network (see at least Ghadiok P0041: When dead-reckoning systems are used, the high-precision location can be used to correct or eliminate drift ( e.g., location error) and/or reset the reference point for the dead-reckoning system (e.g., reset the location error, set the location error to a zero value or another suitable value, etc.)) and P0040: The method can be performed at a predetermined frequency, in response to a localization error (e.g., estimated, calculated) and P0090: the system position relative to the landmark is determined by applying a CNN or other neural network to the recorded signals the examiner interprets calculated localization error as an example of a prediction of upcoming error).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morita to incorporate the method of Ghadiok in the vehicle location correction field of invention to use a neural network for the advantage of validation, calibration, and to increase the accuracy of the modules (Ghadiok P0070 and P0071).
Conclusion
Related References
The related art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 5890090 A by Nelson, Jr. teaches the calculations for a dead-reckoning system with particular detail to the axis projections that are used within the calculations system.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KITO R ROBINSON whose telephone number is (571)270-3921. The examiner can normally be reached M-F 8:00am-5:00pm.
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/KITO R ROBINSON/Supervisory Patent Examiner, Art Unit 3664
