Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Application
This action is in reply to the Request For Continued Examination and amendments filed August 29, 2025.
Claims 1 – 30 are pending and elected for examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 22, 2025, has been entered.
Response to Arguments
Objections
The amendment to claim 21 correcting the typographical error is acknowledged and recorded.
35 USC § 103
Applicant’s arguments with respect to independent claims 1, 11, 21, and 29 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The examiner recommends reviewing the discussion from the interview summary filed August 22, 2025, for providing additional detail of the limitation for compiling datapoints and the use of DR/delta-z which may overcome the prior art on the record.
Examination According to Amendments
Information Disclosure Statement
The information disclosure statements (IDS) submitted on June 7, 2023, and May 7, 2024, have been considered by the examiner.
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 are rejected under 35 U.S.C. 103 as being unpatentable over patent US 20240263949 A1, hereinafter Morita in view of US 2010/0030471 A1, hereinafter Watanabe.
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); 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 to determine longitudinal and lateral corrections (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 and P0058: an amount of vehicle movement may be obtained (step S32), whereby lateral components may be extracted (step S33), and the extracted lateral components may be added up (step S34)); 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, Watanabe teaches A method of determining location of a vehicle, the method comprising: obtaining a first location of the vehicle corresponding to a first time; determining a dead reckoning location of the vehicle corresponding to a second time (see at least Watanabe Abstract: A position detecting apparatus used in a navigation system for detecting a vehicle position includes… a dead reckoning calculating unit for calculating state quantity inclusive of a current vehicle position… The dead reckoning calculating unit includes: a movement calculating unit for calculating a movement during a period from a previous state quantity calculation time up to a current state quantity calculation time the examiner interprets a previous state quantity calculation time as an example of a first time and a current state quantity calculation time as an example of a second time); obtaining feature information corresponding to the second time; 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 Watanabe Abstract: state quantity inclusive of a current vehicle position, a vehicle speed, and a vehicle attitude angle the examiner interprets a state quantity as an example of one or more features); 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 to determine longitudinal and lateral corrections (see at least Watanabe P0050: the sixth step including a resolving step of resolving the measured moving speed into a vehicle longitudinal component, a vehicle lateral component, and a vehicle vertical component based on the amount of change in vehicle attitude; and a calculating step of determining, by calculation, a corrected state quantity based on each of the speed components, and the state quantity calculated through the dead reckoning calculating process and Fig. 2 showing the relationships of vehicle directions to the coordinate system and projected speed components).
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 Watanabe in the dead reckoning navigation field of invention to determine both longitudinal and lateral corrections for the advantage of correcting the navigation representations for the dead reckoning process (see at least Watanabe P0050).
Regarding Claim 2, the combination of Morita and Watanabe 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 Watanabe 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 Watanabe 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 Watanabe 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 Watanabe 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 Watanabe 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); obtain 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); obtain 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); 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 to determine longitudinal and lateral corrections (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 and P0058: an amount of vehicle movement may be obtained (step S32), whereby lateral components may be extracted (step S33), and the extracted lateral components may be added up (step S34)); 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, Watanabe teaches obtain a first location of the vehicle corresponding to a first time; determine a dead reckoning location of the vehicle corresponding to a second time (see at least Watanabe Abstract: A position detecting apparatus used in a navigation system for detecting a vehicle position includes… a dead reckoning calculating unit for calculating state quantity inclusive of a current vehicle position… The dead reckoning calculating unit includes: a movement calculating unit for calculating a movement during a period from a previous state quantity calculation time up to a current state quantity calculation time the examiner interprets a previous state quantity calculation time as an example of a first time and a current state quantity calculation time as an example of a second time); obtain feature information corresponding to the second time; obtain 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 Watanabe Abstract: state quantity inclusive of a current vehicle position, a vehicle speed, and a vehicle attitude angle the examiner interprets a state quantity as an example of one or more features); 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 to determine longitudinal and lateral corrections (see at least Watanabe P0050: the sixth step including a resolving step of resolving the measured moving speed into a vehicle longitudinal component, a vehicle lateral component, and a vehicle vertical component based on the amount of change in vehicle attitude; and a calculating step of determining, by calculation, a corrected state quantity based on each of the speed components, and the state quantity calculated through the dead reckoning calculating process and Fig. 2 showing the relationships of vehicle directions to the coordinate system and projected speed components).
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 Watanabe in the dead reckoning navigation field of invention to determine both longitudinal and lateral corrections for the advantage of correcting the navigation representations for the dead reckoning process (see at least Watanabe P0050).
Regarding Claim 12, the combination of Morita and Watanabe 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 Watanabe 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 Watanabe 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 Watanabe 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 Watanabe 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 Watanabe 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); 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 to determine longitudinal and lateral corrections (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 and P0058: an amount of vehicle movement may be obtained (step S32), whereby lateral components may be extracted (step S33), and the extracted lateral components may be added up (step S34)); 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, Watanabe 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; means for determining a dead reckoning location of the vehicle corresponding to a second time (see at least Watanabe Abstract: A position detecting apparatus used in a navigation system for detecting a vehicle position includes… a dead reckoning calculating unit for calculating state quantity inclusive of a current vehicle position… The dead reckoning calculating unit includes: a movement calculating unit for calculating a movement during a period from a previous state quantity calculation time up to a current state quantity calculation time the examiner interprets a previous state quantity calculation time as an example of a first time and a current state quantity calculation time as an example of a second time); means for obtaining feature information corresponding to the second time; 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 Watanabe Abstract: state quantity inclusive of a current vehicle position, a vehicle speed, and a vehicle attitude angle the examiner interprets a state quantity as an example of one or more features); 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 to determine longitudinal and lateral corrections (see at least Watanabe P0050: the sixth step including a resolving step of resolving the measured moving speed into a vehicle longitudinal component, a vehicle lateral component, and a vehicle vertical component based on the amount of change in vehicle attitude; and a calculating step of determining, by calculation, a corrected state quantity based on each of the speed components, and the state quantity calculated through the dead reckoning calculating process and Fig. 2 showing the relationships of vehicle directions to the coordinate system and projected speed components).
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 Watanabe in the dead reckoning navigation field of invention to determine both longitudinal and lateral corrections for the advantage of correcting the navigation representations for the dead reckoning process (see at least Watanabe P0050).
Regarding Claim 22, the combination of Morita and Watanabe 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 Watanabe 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 Watanabe 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 Watanabe 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).
Morita + Watanabe + Ghadiok
Claims 8 – 10, 18 – 20, and 26 – 28 are rejected under 35 U.S.C. 103 as being unpatentable over Morita and Watanabe in view of patent publication US 20220026232 A1, hereinafter Ghadiok.
Regarding Claim 8, the combination of Morita and Watanabe 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 Watanabe 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 Watanabe 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 Watanabe 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 Watanabe 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 Watanabe 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).
Morita + Watanabe + Beaurepaire
Claims 29 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Morita and Watanabe in view of patent publication EP 2833098 A1, hereinafter Beaurepaire.
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); 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 to determine longitudinal and lateral corrections (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 and P0058: an amount of vehicle movement may be obtained (step S32), whereby lateral components may be extracted (step S33), and the extracted lateral components may be added up (step S34)); 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, Watanabe teaches code for obtaining a first location of the vehicle corresponding to a first time; code for determining a dead reckoning location of the vehicle corresponding to a second time (see at least Watanabe Abstract: A position detecting apparatus used in a navigation system for detecting a vehicle position includes… a dead reckoning calculating unit for calculating state quantity inclusive of a current vehicle position… The dead reckoning calculating unit includes: a movement calculating unit for calculating a movement during a period from a previous state quantity calculation time up to a current state quantity calculation time the examiner interprets a previous state quantity calculation time as an example of a first time and a current state quantity calculation time as an example of a second time); code for obtaining feature information corresponding to the second time; 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 (see at least Watanabe Abstract: state quantity inclusive of a current vehicle position, a vehicle speed, and a vehicle attitude angle the examiner interprets a state quantity as an example of one or more features); 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 to determine longitudinal and lateral corrections (see at least Watanabe P0050: the sixth step including a resolving step of resolving the measured moving speed into a vehicle longitudinal component, a vehicle lateral component, and a vehicle vertical component based on the amount of change in vehicle attitude; and a calculating step of determining, by calculation, a corrected state quantity based on each of the speed components, and the state quantity calculated through the dead reckoning calculating process and Fig. 2 showing the relationships of vehicle directions to the coordinate system and projected speed components).
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 Watanabe in the dead reckoning navigation field of invention to determine both longitudinal and lateral corrections for the advantage of correcting the navigation representations for the dead reckoning process (see at least Watanabe P0050).
The combination of Morita and Watanabe 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. Furthermore, Beaurepaire teaches a system that can be predictably integrated with that of Morita as a dead reckoning location correction method. Beaurepaire teaches (see at least Abstract): A location platform determines a first set of dead reckoning data associated with a vehicle from a first point to a second point… determines a second set of dead reckoning data associated with at least one device of at least one user of the vehicle from the second point to a third point, wherein the one or more non-dead reckoning location technologies is available or used at the third point. The location platform then processes and/or facilitates a processing of the first set of dead reckoning data and the second set of dead reckoning data to determine a location of the second point and (see at least P0042): the calculation module 211 combines the first and second sets of dead reckoning data to retroactively determine the location of the second point once the one or more non-dead reckoning location 20 technologies become available to the at least one device at the third point. The non-transitory processor-readable storage medium comprising processor- readable instructions (see at least Beaurepaire P0078: instructions, also called computer instructions, software and program code, may be read into memory 804 from another computer-readable medium and P0074: The term "computer-readable medium" as used herein refers to any medium that participates in providing information to processor 802, including instructions for execution. Such a medium may take many forms, including, but not limited to computer-readable storage medium (e.g., non-volatile media, volatile media), and transmission media. Non-transitory media, such as non-volatile media, include, for example, optical or magnetic disks, such as storage device 808) configured to cause one or more processors to determine a location of a vehicle (see at least Beaurepaire P0004: comprises at least one processor, and at least one memory including computer program code for one or more computer programs) configured to complete the processes described above.
Thus, 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 the combination of Morita and Watanabe to incorporate the method of Beaurepaire in the location tracking and correction field of invention to use code for the advantage of providing instructions for the operation of the processor and/or the computer system (see at least Beaurepaire P0069).
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. Furthermore, Beaurepaire teaches a location correction system detailed above and that the system which is configured to complete the processes described above (see at least Beaurepaire P0004) comprises at least one processor, and at least one memory including computer program code for one or more computer programs).
Thus, 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 Beaurepaire in the location tracking and correction field of invention to use code for the advantage of providing instructions for the operation of the processor and/or the computer system (see at least Beaurepaire P0069).
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.
Information
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ROSE . RIDDER
Examiner
Art Unit 3664
/R.R./Examiner, Art Unit 3664
/KITO R ROBINSON/Supervisory Patent Examiner, Art Unit 3664
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