DETAILED ACTION
This office action is in response to the amendment filed on 7/13/2026. In the amendment, claims 1-3, 5 and 8 have been amended and claim 7 is now canceled and claims 9-10 are newly added. Overall, claims 1-6 and 8-10 are pending in this application.
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 .
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Pub No. US 2021/0311208 A1 to Ohlarik et. al. (Ohlarik) in view of Pub No. US 2022/0142045 A1 to Kawal (Kawal).
In Reference to Claim 1
Ohlarik teaches (except for the bolded and italic recitations below):
A self-position estimation system for a mobile body, comprising:
A memory (330) that stores instructions and correction information map data; and
a processor (320) that, when executing the instructions stored in the memory (330) comprising:
estimating a first self-position in a first coordinate system by using a first positioning sensor (#105 image device) mounted on the mobile body (110) (see at least Ohlarik Figs. 1-3 and paragraphs 26 and 61, “In some implementations, the location platform 115 may receive, from the vehicle device 105, additional information about the image and/or the vehicle 110. For example, the location platform 115 may receive information associated with camera hardware associated with the image, such as a focal length associated with the image, a lens type associated with the image, and/or the like. In some implementations, the location platform 115 may receive additional information associated with the vehicle 110. For example, the location platform 115 may obtain a sample timestamp, a direction of the vehicle 110, speed information associated with the vehicle 110, acceleration data associated with the vehicle 110, and/or the like” and “Software instructions may be read into memory 330 and/or storage component 340 from another computer-readable medium or from another device via communication interface 370. When executed, software instructions stored in memory 330 and/or storage component 340 may cause processor 320 to perform one or more processes described herein”);
estimating a second self-position in a second coordinate system by using a second positioning sensor (#105 GNSS device) mounted on the mobile body (110) (see at least Ohlarik Figs. 1-3 and paragraphs 14 “As shown in FIG. 1A, and by reference number 120, location platform 115 may receive, from vehicle device 105, a calculated global navigation satellite system (GNSS) location of the vehicle 110. The calculated GNSS location may have been determined through the vehicle device 105 located on the vehicle 110. For example, the vehicle device 105 (e.g., using a vehicle sensor such as a GNSS device and/or the like) may have obtained information from a set of satellites (e.g., satellite 1, satellite 2, satellite 3, satellite 4, and/or the like) indicating satellite coordinates for the vehicle 110. Each satellite, of the set of satellites, may send a respective set of satellite coordinates for the vehicle 110. The vehicle device 105 may combine the multiple sets of satellite coordinates from the set of satellites to determine a GNSS location”);
converting the first self-position in the first coordinate system into an expression format (location coordination) in the second coordinate system (see at least Ohlarik Figs. 1D, 1E and paragraphs 27 and 35 “As shown in FIG. 1D, and by reference number 140, the location platform 115 may process the image identifying the reference points, with a vision positioning system (VPS), to determine a VPS location of the vehicle. For example, in some implementations, the location platform 115 may perform image processing to identify one or more reference points from the images. In some implementations, the location platform 115 may use vehicle device information (e.g., focal length of a lens and/or the like), image information (e.g., height of a reference point in the image, and/or the like) and information known about a reference point (e.g., height of a known reference point) to determine a relative distance of the reference point to the vehicle device 105 and/or the vehicle 110. For example, the location platform 115 may use the equations below to determine the relative position of the one or more reference points to the vehicle device 105 and/or the vehicle 110” and “Similar to what was described in relation to FIG. 1B, each sphere may represent a possible set of location coordinates for the vehicle 110. In some implementations, one or more of the set of spheres may overlap. For example, in a 3-plane coordinate system, two sets of spheres may overlap, resulting in three possible sets of location coordinates. In some implementations, the set of spheres may converge at a single point (e.g., at least four spheres in a 3-plane coordinate system), resulting in one possible set of location coordinates. The single point of convergence may be used as a VPS position. In some implementations, the spheres may not intercept at a single point but instead surround an area in space”);
correcting the first self-position converted into an expression format (location coordination) in the second coordinate system using correction information map data that stores, as correction information, a positional deviation amount of a recording point in a real space expressed in the first coordinate system with respect to the second coordinate system (see at least Ohlarik Figs 1E and 1F and paragraphs 36-37 “As shown in FIG. 1E, and by reference number 145, the location platform 115 may determine whether the VPS location of the vehicle is accurate within another threshold accuracy. For example, the location platform 115 may determine whether the spheres formed using the above equation converge at a single point. In some implementations, the threshold accuracy may be based on how many spheres intercept, a size of the area the sphere surround, and/or the like. The location platform 115 may determine to utilize the VPS location of the vehicle 110 if the VPS location satisfies the threshold accuracy. In some implementations, the location platform 115 may determine to not utilize the VPS location of the vehicle 110 if the VPS location does not satisfy the threshold accuracy. As shown in FIG. 1E, and by reference number 150, the location platform 115 may determine to utilize the GNSS location of the vehicle based on the VPS location of the vehicle failing to satisfy the threshold accuracy. In this way, the VPS location may not augment the GNSS location of the vehicle if it is not determined to be useful. In this case, the location platform 115 may determine to use solely the GNSS location of the vehicle as a location of the vehicle, obtain other information to determine the location of the vehicle, and/or the like” and “As shown in FIG. 1F, and by reference number 155, the location platform 115 may calculate coordinate sets based on groups of all possible coordinate combinations from the GNSS location and the VPS location and based on distances between the groups. For example, the location platform 115 may calculate possible coordinate sets based on forming all combinations of the satellite coordinates and the reference point coordinates in sets of three. Each set may be used in conjunction with the aforementioned equations to generate spheres and identify location coordinate candidates. For example, a set of three possible coordinate sets (e.g., any combination of the satellite coordinate sets and reference point coordinate sets) may be used to output a set of three spheres using the equations mentioned above. If the spheres intersect, the intersecting point may be used as a location coordinate set candidate. If the spheres do not intersect, the set may be discarded. This process may be repeated for all possible coordinate combinations, until there is a list of location coordinate set candidates. The location platform 115 may assess each location coordinate set candidate for a likelihood of accuracy. In this way, the location platform 115 may generate sets of possible location coordinates and may evaluate each set of possible location coordinates to determine an accurate location”); and
controlling movement of the mobile body (110) while switching between a first movement control mode (650) and a second movement control mode (650), wherein in the first movement control mode, movement control of the mobile body (110) is performed using the first self-position, and in the second movement control mode, the movement control of the mobile body (110) is performed using the second self-position (Ohlarik performing the steps 650 and repeating the steps in Fig. 6 and if adjustments are required would switch between the first and second mode based on the accuracy of the first or second self-positions) (see at least Ohlarik Figs. 1H and 6 and paragraphs 10, 39, 95-96 and 102 “Some implementations described herein provide a location platform that utilizes a machine learning model to determine a determined location of a vehicle based on a combination of a geographical (e.g., GNSS) location and a visual positioning system (VPS) location of the vehicle. For example, the location platform may receive, from a vehicle device, a calculated GNSS location of a vehicle, and may determine whether the GNSS location of the vehicle is accurate within a first threshold accuracy. The location platform may utilize the GNSS location of the vehicle as a determined location of vehicle when the GNSS location of the vehicle satisfies the first threshold accuracy, and may receive, from the vehicle device, an image identifying reference points associated with the vehicle. The location platform may process the image identifying the reference points, with a VPS, to calculate a VPS location of the vehicle, and may determine whether the VPS location of the vehicle is accurate within a second threshold accuracy. The location platform may utilize the GNSS location of the vehicle as the determined location of vehicle when the VPS location of the vehicle fails to satisfy the second threshold accuracy, and may calculate, when the VPS location of the vehicle satisfies the second threshold accuracy, coordinate sets based on groups of coordinate combinations from the GNSS location and the VPS location and based on distances between the groups. The location platform may process the coordinate sets, with a machine learning model, to determine the determined location of the vehicle, and may perform one or more actions based on the determined location of the vehicle” and “As shown in FIG. 1H, and by reference number 165, the location platform 115 may perform one or more actions based on the determined location of the vehicle 110. In some implementations, the location platform 115 may provide information based on the determined location of the vehicle 110, such as providing a user interface that includes an indication of the determined location of the vehicle 110, and/or the like. In some implementations, the location platform 115 may perform one or more actions concerning the vehicle 110, such as instructing the vehicle 110 to perform a maneuver based on the determined location, recalculating directions for the vehicle based on the determined location, and/or the like. In some implementations, the one or more actions may include vehicle device 105 and/or location platform 115 retraining one or more of the models described above based on the determined location of the vehicle 110. In this way, vehicle device 105 and/or location platform 115 may improve the accuracy of the models in determining the determined location of a vehicle, determining whether to perform further processing or not, and/or the like, which may improve speed and efficiency of the models and conserve computing resources, networking resources, and/or the like”, “As further shown in FIG. 6, process 600 may include performing one or more actions based on the determined location of the vehicle (block 650). For example, the device (e.g., using computing resource 220, processor 320, memory 330, storage component 340, communication interface 370, and/or the like) may perform one or more actions based on the determined location of the vehicle, as described above. In some implementations, performing the one or more actions may include providing a user interface that includes an indication of the determined location of the vehicle; augmenting a global navigation satellite system user interface with information identifying the reference points and the determined location of the vehicle; or instructing the vehicle to perform a maneuver based on the determined location of the vehicle”, “In some implementations, performing the one or more actions may include recalculating directions for the vehicle based on the determined location of the vehicle; retraining the visual positioning system based on the determined location of the vehicle; or retraining the machine learning model based on the determined location of the vehicle” and “Although FIG. 6 shows example blocks of process 600, in some implementations, process 600 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in FIG. 6. Additionally, or alternatively, two or more of the blocks of process 600 may be performed in parallel”).
Ohlarik teaches to correct the coordination the first self-position converted into an expression format (location coordination) in the second coordinate system however Ohlarik does not explicitly teaches (bolded and italic recitations above) that the coordination is compared to the information map data to be corrected. However, it is known in the art before the effective filing date of the claimed invention to for example, Kawal teaches comparing coordination to the information within the map data for correction. Kawal further teaches that performing such step provides accuracy of determining the location (see at least Kawal Figs.1-2 and paragraphs 22, 25-26, 33, and 37-38). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Ohlarik to include the step of comparing coordination for correction include the information map data as taught by Kawal in order to provides accuracy of determining the location.
In Reference to Claim 2
The self-position estimation system according to Claim 1 (see rejection to claim 1 above), wherein
the processor (320) further performs operations comprising:
extracting, from the correction information map data (taught by Kawal), the correction information stored in association with the recording point whose distance from the first self-position is equal to or less than a predetermined distance (threshold); (see at least Ohlarik Figs 1B ad 6 and paragraphs 22 “As shown in FIG. 1B, and by reference number 130, the location platform 115 may determine to utilize the GNSS location of the vehicle 110 if the GNSS location satisfies the threshold accuracy. In some implementations, the location platform 115 may determine to not utilize the GNSS location of the vehicle 110 if the GNSS location does not satisfy the threshold accuracy. In this case, the location platform 115 may determine to utilize another GNSS location of the vehicle 110 that may be received by the location platform 115. In this way, the location platform 115 may determine whether the GNSS location is accurate enough to use as a location of the vehicle, whether to augment the GNSS location with other location information, and/or the like”), and
correcting the first self-position based on the extracted correction information (see at least Kawal paragraph [0029] “The determination unit 175 determines whether it is necessary to update the regions R1 and R2 specified by the specification unit 172. For example, the detection intensity of the GNSS sensor 15 may be subsequently changed due to an object being installed later in the work region WR, a building being constructed later around the work region WR, or the like. In such a case, preferably, the notification unit 174 makes a notification on the basis of the results of determination by the determination unit 175, and the installation position of the marker MK is corrected by the user in accordance with the notification. The determination unit 175 can perform the determination during execution of the work by the work machine 1”) (see at least Kawal Figs.1-2 and paragraphs 22, 25-26, 29, 33, and 37-38).
In Reference to Claim 3
The self-position estimation system according to Claim 2 (see rejection to claim 2 above), wherein in a case where there are a plurality of the recording points, the processor (320) calculates a correction value to be applied to the first self-position by weight-averaging the correction information stored in association with the plurality of recording points based on a distance between the first self-position and each position of the plurality of recording points (see at least Ohlarik Fig. 4 and paragraphs 74 “The machine learning system may combine the cross-validation scores for each training procedure to generate an overall cross-validation score for the machine learning model. The overall cross-validation score may include, for example, an average cross-validation score (e.g., across all training procedures), a standard deviation across cross-validation scores, a standard error across cross-validation scores, and/or the like”).
In Reference to Claim 4
The self-position estimation system according to Claim 1 (see rejection to claim 1 above), wherein each of the first self-position and the second self-position includes a coordinate position and a direction of the mobile body (see at least Ohlarik Fig. 4 and paragraphs 9-10).
In Reference to Claim 5
The self-position estimation system according to Claim 1 (see rejection to claim 1 above), wherein
the processor (320) estimates the first self-position by a position estimation method using a ranging sensor (#105 image device) (see at least Ohlarik Figs. 1-3 and paragraphs 26), and
the processor (320) estimates the second self-position by a position estimation method using a satellite positioning system (#105 GNSS device) (see at least Ohlarik Figs. 1-3 and paragraphs 14).
In Reference to Claim 6
The self-position estimation system according to Claim 1 (see rejection to claim 1 above), wherein the mobile body is an autonomous mobile robot (110) (Ohlarik teaches that the vehicle is autonomous vehicle) (see at least Ohlarik Figs. 1-3 and claim 13).
In Reference to Claim 8
A self-position estimation method for a mobile body, comprising:
estimating a first self-position in a first coordinate system by using a first positioning sensor (#105 image device) mounted on the mobile body (110) (see at least Ohlarik Figs. 1-3 and paragraphs 26 and 61, “In some implementations, the location platform 115 may receive, from the vehicle device 105, additional information about the image and/or the vehicle 110. For example, the location platform 115 may receive information associated with camera hardware associated with the image, such as a focal length associated with the image, a lens type associated with the image, and/or the like. In some implementations, the location platform 115 may receive additional information associated with the vehicle 110. For example, the location platform 115 may obtain a sample timestamp, a direction of the vehicle 110, speed information associated with the vehicle 110, acceleration data associated with the vehicle 110, and/or the like” and “Software instructions may be read into memory 330 and/or storage component 340 from another computer-readable medium or from another device via communication interface 370. When executed, software instructions stored in memory 330 and/or storage component 340 may cause processor 320 to perform one or more processes described herein”);
estimating a second self-position in a second coordinate system by using a second positioning sensor (#105 GNSS device) mounted on the mobile body (110) (see at least Ohlarik Figs. 1-3 and paragraphs 14 “As shown in FIG. 1A, and by reference number 120, location platform 115 may receive, from vehicle device 105, a calculated global navigation satellite system (GNSS) location of the vehicle 110. The calculated GNSS location may have been determined through the vehicle device 105 located on the vehicle 110. For example, the vehicle device 105 (e.g., using a vehicle sensor such as a GNSS device and/or the like) may have obtained information from a set of satellites (e.g., satellite 1, satellite 2, satellite 3, satellite 4, and/or the like) indicating satellite coordinates for the vehicle 110. Each satellite, of the set of satellites, may send a respective set of satellite coordinates for the vehicle 110. The vehicle device 105 may combine the multiple sets of satellite coordinates from the set of satellites to determine a GNSS location”);
converting the first self-position in the first coordinate system into an expression format (location coordination) in the second coordinate system (see at least Ohlarik Figs. 1D, 1E and paragraphs 27 and 35 “As shown in FIG. 1D, and by reference number 140, the location platform 115 may process the image identifying the reference points, with a vision positioning system (VPS), to determine a VPS location of the vehicle. For example, in some implementations, the location platform 115 may perform image processing to identify one or more reference points from the images. In some implementations, the location platform 115 may use vehicle device information (e.g., focal length of a lens and/or the like), image information (e.g., height of a reference point in the image, and/or the like) and information known about a reference point (e.g., height of a known reference point) to determine a relative distance of the reference point to the vehicle device 105 and/or the vehicle 110. For example, the location platform 115 may use the equations below to determine the relative position of the one or more reference points to the vehicle device 105 and/or the vehicle 110” and “Similar to what was described in relation to FIG. 1B, each sphere may represent a possible set of location coordinates for the vehicle 110. In some implementations, one or more of the set of spheres may overlap. For example, in a 3-plane coordinate system, two sets of spheres may overlap, resulting in three possible sets of location coordinates. In some implementations, the set of spheres may converge at a single point (e.g., at least four spheres in a 3-plane coordinate system), resulting in one possible set of location coordinates. The single point of convergence may be used as a VPS position. In some implementations, the spheres may not intercept at a single point but instead surround an area in space”);
correcting the first self-position converted into the expression format (location coordination) in the second coordinate system using correction information map data that stores, as correction information, a positional deviation amount of a recording point in a real space expressed in the first coordinate system with respect to the second coordinate system (see at least Ohlarik Figs 1E and 1F and paragraphs 36-37 “As shown in FIG. 1E, and by reference number 145, the location platform 115 may determine whether the VPS location of the vehicle is accurate within another threshold accuracy. For example, the location platform 115 may determine whether the spheres formed using the above equation converge at a single point. In some implementations, the threshold accuracy may be based on how many spheres intercept, a size of the area the sphere surround, and/or the like. The location platform 115 may determine to utilize the VPS location of the vehicle 110 if the VPS location satisfies the threshold accuracy. In some implementations, the location platform 115 may determine to not utilize the VPS location of the vehicle 110 if the VPS location does not satisfy the threshold accuracy. As shown in FIG. 1E, and by reference number 150, the location platform 115 may determine to utilize the GNSS location of the vehicle based on the VPS location of the vehicle failing to satisfy the threshold accuracy. In this way, the VPS location may not augment the GNSS location of the vehicle if it is not determined to be useful. In this case, the location platform 115 may determine to use solely the GNSS location of the vehicle as a location of the vehicle, obtain other information to determine the location of the vehicle, and/or the like” and “As shown in FIG. 1F, and by reference number 155, the location platform 115 may calculate coordinate sets based on groups of all possible coordinate combinations from the GNSS location and the VPS location and based on distances between the groups. For example, the location platform 115 may calculate possible coordinate sets based on forming all combinations of the satellite coordinates and the reference point coordinates in sets of three. Each set may be used in conjunction with the aforementioned equations to generate spheres and identify location coordinate candidates. For example, a set of three possible coordinate sets (e.g., any combination of the satellite coordinate sets and reference point coordinate sets) may be used to output a set of three spheres using the equations mentioned above. If the spheres intersect, the intersecting point may be used as a location coordinate set candidate. If the spheres do not intersect, the set may be discarded. This process may be repeated for all possible coordinate combinations, until there is a list of location coordinate set candidates. The location platform 115 may assess each location coordinate set candidate for a likelihood of accuracy. In this way, the location platform 115 may generate sets of possible location coordinates and may evaluate each set of possible location coordinates to determine an accurate location”); and
controlling movement of the mobile body (110) while switching between a first movement control mode (650) and a second movement control mode (650), wherein in the first movement control mode, movement control of the mobile body (110) is performed using the first self-position, and in the second movement control mode, the movement control of the mobile body (110) is performed using the second self-position (Ohlarik performing the steps 650 and repeating the steps in Fig. 6 and if adjustments are required would switch between the first and second mode based on the accuracy of the first or second self-positions) (see at least Ohlarik Figs. 1H and 6 and paragraphs 10, 39, 95-96 and 102 “Some implementations described herein provide a location platform that utilizes a machine learning model to determine a determined location of a vehicle based on a combination of a geographical (e.g., GNSS) location and a visual positioning system (VPS) location of the vehicle. For example, the location platform may receive, from a vehicle device, a calculated GNSS location of a vehicle, and may determine whether the GNSS location of the vehicle is accurate within a first threshold accuracy. The location platform may utilize the GNSS location of the vehicle as a determined location of vehicle when the GNSS location of the vehicle satisfies the first threshold accuracy, and may receive, from the vehicle device, an image identifying reference points associated with the vehicle. The location platform may process the image identifying the reference points, with a VPS, to calculate a VPS location of the vehicle, and may determine whether the VPS location of the vehicle is accurate within a second threshold accuracy. The location platform may utilize the GNSS location of the vehicle as the determined location of vehicle when the VPS location of the vehicle fails to satisfy the second threshold accuracy, and may calculate, when the VPS location of the vehicle satisfies the second threshold accuracy, coordinate sets based on groups of coordinate combinations from the GNSS location and the VPS location and based on distances between the groups. The location platform may process the coordinate sets, with a machine learning model, to determine the determined location of the vehicle, and may perform one or more actions based on the determined location of the vehicle” and “As shown in FIG. 1H, and by reference number 165, the location platform 115 may perform one or more actions based on the determined location of the vehicle 110. In some implementations, the location platform 115 may provide information based on the determined location of the vehicle 110, such as providing a user interface that includes an indication of the determined location of the vehicle 110, and/or the like. In some implementations, the location platform 115 may perform one or more actions concerning the vehicle 110, such as instructing the vehicle 110 to perform a maneuver based on the determined location, recalculating directions for the vehicle based on the determined location, and/or the like. In some implementations, the one or more actions may include vehicle device 105 and/or location platform 115 retraining one or more of the models described above based on the determined location of the vehicle 110. In this way, vehicle device 105 and/or location platform 115 may improve the accuracy of the models in determining the determined location of a vehicle, determining whether to perform further processing or not, and/or the like, which may improve speed and efficiency of the models and conserve computing resources, networking resources, and/or the like”, “As further shown in FIG. 6, process 600 may include performing one or more actions based on the determined location of the vehicle (block 650). For example, the device (e.g., using computing resource 220, processor 320, memory 330, storage component 340, communication interface 370, and/or the like) may perform one or more actions based on the determined location of the vehicle, as described above. In some implementations, performing the one or more actions may include providing a user interface that includes an indication of the determined location of the vehicle; augmenting a global navigation satellite system user interface with information identifying the reference points and the determined location of the vehicle; or instructing the vehicle to perform a maneuver based on the determined location of the vehicle”, “In some implementations, performing the one or more actions may include recalculating directions for the vehicle based on the determined location of the vehicle; retraining the visual positioning system based on the determined location of the vehicle; or retraining the machine learning model based on the determined location of the vehicle” and “Although FIG. 6 shows example blocks of process 600, in some implementations, process 600 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in FIG. 6. Additionally, or alternatively, two or more of the blocks of process 600 may be performed in parallel”).
Ohlarik teaches to correct the coordination the first self-position converted into an expression format (location coordination) in the second coordinate system however Ohlarik does not explicitly teaches (bolded and italic recitations above) that the coordination is compared to the information map data to be corrected. However, it is known in the art before the effective filing date of the claimed invention to for example, Kawal teaches comparing coordination to the information within the map data for correction. Kawal further teaches that performing such step provides accuracy of determining the location (see at least Kawal Figs.1-2 and paragraphs 22, 25-26, 33, and 37-38). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Ohlarik to include the step of comparing coordination for correction include the information map data as taught by Kawal in order to provides accuracy of determining the location.
In Reference to Claim 9
The self-position estimation system according to Claim 1 (see rejection to claim 1 above), wherein the processor (320) controls the movement of the mobile body (110) by alternately switching between the first movement control mode and the second movement control mode (when the accuracy of the first or second self-position is deemed inaccurate) (see at least Ohlarik Figs. 1H and 6 and paragraphs 10, 39, 95-96 and 102).
In Reference to Claim 10
The self-position estimation method according to Claim 8 (see rejection to claim 8 above), wherein in the controlling movement of the mobile body (110), the first movement control mode and the second movement control mode are alternately switched (when the accuracy of the first or second self-position is deemed inaccurate) (see at least Ohlarik Figs. 1H and 6 and paragraphs 10, 39, 95-96 and 102).
Response to Arguments
Applicant's arguments filed 7/13/2026 have been fully considered but they are not persuasive.
The applicant argues that “Although the cited portion of Ohlarik generally teaches that the location platform 115 may perform one or more actions related to movement of the vehicle 110, Applicant respectfully submits that Ohlarik does not specifically teach controlling movement of the vehicle 110 by switching between a first movement control based on a first self-position and a second movement control based on a second self-position. Accordingly, Ohlarik necessarily fails to teach "controlling movement of the mobile body while switching between a first movement control mode and a second movement control mode," where "in the first movement control mode, movement control of the mobile body is performed using the first self-position, and in the second movement control mode, the movement control of the mobile body is performed using the second self-position," as required by the above-noted features of claim 1. Further, it is respectfully submitted that Kawai fails to provide disclosure that would obviate the above-mentioned deficiencies of Ohlarik, that is, Kawai fails to teach "controlling movement of the mobile body while switching between a first movement control mode and a second movement control mode," where "in the first movement control mode, movement control of the mobile body is performed using the first self-position, and in the second movement control mode, the movement control of the mobile body is performed using the second self-position," as required by the above-noted features of claim 1” however the examiner respectfully disagree with the applicant since Ohlarik teaches to control the movement of the mobile body based on accuracy of the positions determined based on the first or second self-positions such as continuing to perform the steps in Fig.6 continuously therefore once the inaccuracy of the self-position is determined the movement will be adjusted until current self-position becomes inaccurate therefore Ohlarik would read on the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pub No. US 2011/0010033 A1 to Asahara et. al. (Asahara) teaches to determine the location of the machine based on range sensor and map data.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON DONGPA LEE whose telephone number is (571)270-3525. The examiner can normally be reached Monday - Friday, 8:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aniss Chad can be reached at (571) 270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRANDON D LEE/Primary Examiner, Art Unit 3662
August 13, 2026
Prosecution Insights