CTFR 18/375,067 CTFR 99913 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-06 AIA 15-10-15 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. 12-151 AIA 26-51 12-51 Status of Claims The following is an office action in response to the communication filed on 09/16/2025. Claims 1, 3, 10, 13, 16, and 18 are amended. Claims 1-20 are currently pending. Claims 1-20 have been examined. Claim Objections 07-29-01 AIA Claim 13 is objected to because of the following informalities: Claim 13 currently recites “. . . calibrate the senor device . . . an object detection senor . . .” which the examiner notes appears to contain a typographical error and recommends updating to “. . . calibrate the sensor device . . . an object detection sensor . . .” to avoid issues under 35 U.S.C. §112(b). For the purposes of examination, the examiner is interpreting the claim to mean “. . . calibrate the sensor device . . . an object detection sensor . . .” . Appropriate correction is required. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1-2, 6, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Al-Rasheed et al. (US 20210319699 A1; hereinafter Al-Rasheed ) in view of Megherby et al. (US 20210241377 A1; hereinafter Megherby ) and further in view of Fasola et al. (US 9884623 B2; hereinafter Fasola ) and Sommer (Sommer, S. (2019). What is Sensor Calibration and Why is it Important. Real-Pars, Dutchland, Tech. Rep.; hereinafter Sommer ) . Regarding claim 1 , Al-Rasheed discloses the subject matter indicated in bold below: An autonomous driving control apparatus (see Al-Rasheed at least [0025] “ . . . a mobile robot device 251 used in a parking control system . . . ”) , comprising: a sensor device including at least one sensor (see Al-Rasheed at least [0026] “ . . . the transceiver 252 may exchange wireless signals with global or regional navigation systems such as Global Positioning System (GPS). ”; [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”) ; a memory storing instructions (see Al-Rasheed at least [0075] “ . . . associated memory 704 (e.g., random access memory (RAM), cache memory, flash memory, etc.), one or more storage device(s) 706 (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory stick, etc.), . . . ”) ; and a controller operatively coupled to the sensor device and the memory (see Al-Rasheed at least [0075] “ . . . may include one or more computer processor(s) . . . ”) , wherein the instructions are configured to, when executed by the controller, cause the autonomous driving control apparatus to: control a mobility device to move to a specified place and obtain, using the sensor device, image data of at least one vehicle in the specified place (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”; [0071] “ . . . the mobile robot device may navigate the map of the parking lot and move to the parking spot that the mobile robot device is dispatched to. ”; [0072] “ . . . the mobile robot device may capture the identification information of the vehicle . . . ”) ; detect, using the image data, . . . license plate information of the at least one vehicle (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”) ; receive . . . identification information corresponding to the at least one vehicle (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”) ; and . . . While Al-Rasheed discloses a sensor device including at least one sensor, controlling a mobility device to move to a specified place and obtain, using the sensor device, image data of at least one vehicle in the specified place, detecting, using the image data, license plate information of the at least one vehicle, and receiving identification information corresponding to the at least one vehicle, it does not appear to explicitly disclose detecting, using the image data, location information of the at least one vehicle, receiving, from an external computing device, identification information corresponding to the at least one vehicle, nor calibrating, based on an error identified based on the location information and the identification information being out of a specified range, the sensor device using at least one of the location information, the identification information, the error identified based on the location information and the identification information, or a combination thereof. Megherby teaches the subject matter indicated with dotted underline below: . . . receive, from an external computing device, identification information corresponding to the at least one vehicle (see Megherby at least [0059] “ VIN information 24 may be used to pre-populate details on the vehicle. For example, the VIN number can identify the make, model, manufacture date, color and other features of the vehicle just from the number itself. Thus, the vehicle records 26 may indicate this information before photographs are added from the robots 6 . . . The system computer 12 will instruct the robots 6/mobile devices 7 where the target area 18 is . . . The VIN information 28 has also been provided to the robots 6 . . . ”) ; and . . . It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the receipt of identification information corresponding to the at least one vehicle and the external cloud server communications (see Al-Rasheed at least [0027] “ The memory 253 may also store dispatch instructions received from the cloud server. ”; [0028] “ . . . locate the parking spot specified in a dispatch instruction . . . ”) of Al-Rasheed with the receipt, from an external computing device, identification information corresponding to the at least one vehicle as taught by Megherby to receive, from an external computing device, identification information corresponding to the at least one vehicle. Doing so would provide a secondary source of vehicle information to confirm against, therefore improving reliability via verification as recognized by Megherby (see Megherby at least [0059] “ With this VIN information 28, as a VIN is captured 22 and images and locations 16 sent to the system computer 12, that vehicle is no longer on the list of VIN numbers that need to be captured. The VIN information 28 will typically be a list of VINs which are expected to be in the target area 18. The VIN information may also include, make, model, type of vehicle, color or other features. Thus, as the robot captures images, when a VIN number indicating a red car turns out to be e.g. black, this can indicate that there is a problem in that e.g. the VIN number was scanned wrong or there is some other issue that may require manual inspection. This location can be flagged and sent to the mobile device 7 for human inspection. ”). While Al-Rasheed and Megherby disclose a sensor device including at least one sensor, controlling a mobility device to move to a specified place and obtain, using the sensor device, image data of at least one vehicle in the specified place, detecting, using the image data, license plate information of the at least one vehicle, and receiving, from an external computing device, identification information corresponding to the at least one vehicle, they do not appear to explicitly disclose detecting, using the image data, location information of the at least one vehicle nor calibrating, based on an error identified based on the location information and the identification information being out of a specified range, the sensor device using at least one of the location information, the identification information, the error identified based on the location information and the identification information, or a combination thereof. Fasola teaches the subject matter underlined below: . . . detect, using the image data, location information of the at least one vehicle (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”) . . . calibrate, based on an error identified based on the location information and the identification information, the sensor device using at least one of: the location information, the identification information, the error identified based on the location information and the identification information, or a combination thereof (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”) . It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the sensor device including at least one sensor, controlling a mobility device to move to a specified place and obtain, using the sensor device, image data of at least one vehicle in the specified place, and receiving identification information corresponding to the at least one vehicle of Al-Rasheed and Megherby with the detecting, using the image data, location information of the at least one vehicle and calibrating, based on an error identified based on the location information and the identification information, the sensor device using the location information as taught by Fasola to detect, using the image data, location information of the at least one vehicle and calibrate, based on an error identified based on the location information and the identification information, the sensor device using the location information. Doing so would reduce the error present during localization when using sensors for navigation. While Al-Rasheed, Megherby, and Fasola disclose calibrating, based on an error identified based on the location information and the identification information, the sensor device using the location information, they do not appear to explicitly disclose calibrating, based on an error identified based on the location information and the identification information being out of a specified range, the sensor device. Sommer discloses calibrating sensors only when they show an error outside of a specified range (see Sommer at least pg. 6, paragraphs 1-3 “ Now let’s assume that the maximum deviation tolerance is 0.20%. Using the data from the calibration sheet, we see from the graph that some deviations are greater than the maximum deviation allowed of 0.20%. Therefore, sensor calibration is required. ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the calibrating, based on an error identified based on the location information and the identification information, the sensor device using the location information of Al-Rasheed, Megherby, and Fasola with the calibrating sensors only when they show an error outside of a specified range as taught by Sommer to calibrate, based on an error identified based on the location information and the identification information being out of a specified range, the sensor device using the location information. Doing so would reduce sensor error via calibration only when necessary, therefore saving resource costs. Regarding claim 2 , Al-Rasheed, Megherby, Fasola, and Sommer disclose claim 1 as recited in the claim and applied above. Additionally, Al-Rasheed discloses the subject matter indicated in bold below: . . . wherein the at least one sensor comprises at least one of a three-dimensional (3D) light detection and ranging (LiDAR) device (see Al-Rasheed at least [0029] “ The camera 255 may operate at visible light frequencies or infrared frequencies and may be coupled to an ultrasonic sensor or a Light Detection and Ranging (LiDAR) sensor. ”) , a front two-dimensional (2D) camera, a rear 2D camera, or a combination thereof. Regarding claim 6 , Al-Rasheed, Megherby, Fasola, and Sommer disclose claim 1 as recited in the claim and applied above. Additionally, Al-Rasheed discloses the subject matter indicated in bold below: . . . wherein the identification information comprises at least one of: a plurality of regions of interest (ROIs) respectively corresponding to a plurality of parking areas in the specified place (see Al-Rasheed at least [0041] “ In the event the mobile robot device 451 has been dispatched to a plurality of parking spots, the mobile robot device 451 may follow the dispatches one after another. ”) , at least one region of interest (ROI) corresponding to a parking area where a vehicle is parked among the plurality of ROIs (see Al-Rasheed at least [0041] “. . . may then dispatch a mobile robot device 451 from the docking station 450 to the parking spot 401. ”) , an identification number of the at least one ROI, a location of each of the at least one vehicle which is parked in at least one parking area of the plurality of parking areas (see Al-Rasheed at least [0040] “ From the entry time, the vehicle 402 is considered as parked in the parking spot 401. ”; [0041] “. . . may then dispatch a mobile robot device 451 from the docking station 450 to the parking spot 401. ”) , a vehicle number of each of the at least one vehicle (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”) , a vehicle class of each of the at least one vehicle, coordinates of each of the at least one vehicle, a width of each of the at least one vehicle, a height of each of the at least one vehicle, or a combination thereof. Regarding claim 16 , Al-Rasheed discloses the subject matter indicated in bold below: An autonomous driving control method (see Al-Rasheed at least claim 1 “ A parking control method . . . ”) , comprising: controlling, by a controller, a mobility device to move to a specified place (see Al-Rasheed at least [0071] “ . . . the mobile robot device may navigate the map of the parking lot and move to the parking spot that the mobile robot device is dispatched to. ”; [0072] “ . . . the mobile robot device may capture the identification information of the vehicle . . . ”; [0075] “ . . . may include one or more computer processor(s) . . . ”) ; obtaining, by the controller and using a sensor device, image data of at least one vehicle in the specified place (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”; [0075] “ . . . may include one or more computer processor(s) . . . ”) ; detecting, by the controller and using the image data, . . . license plate information of the at least one vehicle (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”) ; receiving . . . identification information corresponding to the at least one vehicle (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”) ; and . . . While Al-Rasheed discloses controlling, by a controller, a mobility device to move to a specified place, obtaining, by the controller and using a sensor device, image data of at least one vehicle in the specified place, detecting, by the controller and using the image data, license plate information of the at least one vehicle, and receiving identification information corresponding to the at least one vehicle, it does not appear to explicitly disclose detecting, by the controller and using the image data, location information of the at least one vehicle, receiving, from an external computing device via wireless communication, identification information corresponding to the at least one vehicle, nor calibrating, by the controller based on an error identified based on the location information and the identification information being out of a specified range, the sensor device using at least one of the location information, the identification information, the error identified based on the location information and the identification information, or a combination thereof. Megherby teaches the subject matter indicated with dotted underline below: . . . receiving, from an external computing device via a wireless communication, identification information corresponding to the at least one vehicle (see Megherby at least [0059] “ VIN information 24 may be used to pre-populate details on the vehicle. For example, the VIN number can identify the make, model, manufacture date, color and other features of the vehicle just from the number itself. Thus, the vehicle records 26 may indicate this information before photographs are added from the robots 6 . . . The system computer 12 will instruct the robots 6/mobile devices 7 where the target area 18 is . . . The VIN information 28 has also been provided to the robots 6 . . . The network 9 as shown may e.g. be a cellular network such as 3G, 4G, 5G or other telecommunications networks . . . Once back to the charging dock 10 where there may be a WiFi connection, the robots may send images via the network 9 to the system computer 12 which stores the images with their associated VIN numbers and locations in the vehicle records 26. ”; [0061] “ As shown, the robots can communicate with themselves and also via the network 9 with the system computer 12. ”) ; and . . . It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the receipt of identification information corresponding to the at least one vehicle and the external cloud server communications (see Al-Rasheed at least [0027] “ The memory 253 may also store dispatch instructions received from the cloud server. ”; [0028] “ . . . locate the parking spot specified in a dispatch instruction . . . ”) of Al-Rasheed with the receipt, from an external computing device via a wireless communication, identification information corresponding to the at least one vehicle as taught by Megherby to receive, from an external computing device via a wireless communication, identification information corresponding to the at least one vehicle. The examiner supplies the same rationale for the combination of these references as claim 1 above. While Al-Rasheed and Megherby disclose controlling, by a controller, a mobility device to move to a specified place, obtaining, by the controller and using a sensor device, image data of at least one vehicle in the specified place, detecting, by the controller and using the image data, license plate information of the at least one vehicle, and receiving, from external computing device via wireless communication, identification information corresponding to the at least one vehicle, they do not appear to explicitly disclose detecting, by the controller and using the image data, location information of the at least one vehicle nor calibrating, by the controller based on an error identified based on the location information and the identification information being out of a specified range, the sensor device using at least one of the location information, the identification information, the error identified based on the location information and the identification information, or a combination thereof. Fasola discloses the subject matter underlined below: . . . detecting, using the image data, location information of the at least one vehicle (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”) ; . . . calibrating, based on an error identified based on the location information and the identification information, the sensor device using at least one of: the location information (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”) , the identification information, the error identified based on the location information and the identification information, or a combination thereof. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified controlling, by a controller, a mobility device to move to a specified place, obtaining, by the controller and using a sensor device, image data of at least one vehicle in the specified place, and receiving, from an external computing device via a wireless communication, identification information corresponding to the at least one vehicle of Al-Rasheed and Megherby with the detecting, using the image data, location information of the at least one vehicle and calibrating, based on an error identified based on the location information and the identification information, the sensor device using the location information as taught by Fasola to detect, by the controller and using the image data, location information of the at least one vehicle and calibrate, by the controller and based on an error identified based on the location information and the identification information, the sensor device using the location information. Doing so would reduce the error present during localization when using sensors for navigation. While Al-Rasheed and Fasola disclose calibrating, based on an error identified based on the location information and the identification information, the sensor device using the location information, they do not appear to explicitly disclose calibrating, based on an error identified based on the location information and the identification information being out of a specified range, the sensor device. Sommer discloses calibrating sensors only when they show an error outside of a specified range (see Sommer at least pg. 6, paragraphs 1-3 “ Now let’s assume that the maximum deviation tolerance is 0.20%. Using the data from the calibration sheet, we see from the graph that some deviations are greater than the maximum deviation allowed of 0.20%. Therefore, sensor calibration is required. ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the calibrating, based on an error identified based on the location information and the identification information, the sensor device using the location information of Al-Rasheed, Megherby, and Fasola with the calibrating sensors only when they show an error outside of a specified range as taught by Sommer to calibrate, based on an error identified based on the location information and the identification information being out of a specified range, the sensor device using the location information. Doing so would reduce sensor error via calibration only when necessary, therefore saving resource costs. Regarding claim 17 , Al-Rasheed, Megherby, Fasola, and Sommer disclose the subject matter of claim 16 as recited in the claim and applied above. Additionally, Al-Rasheed discloses the subject matter indicated in bold below: . . . wherein the sensor device comprises at least one of a three- dimensional (3D) light detection and ranging (LiDAR) device (see Al-Rasheed at least [0029] “ The camera 255 may operate at visible light frequencies or infrared frequencies and may be coupled to an ultrasonic sensor or a Light Detection and Ranging (LiDAR) sensor. ”) , a front two-dimensional (2D) camera, a rear 2D camera, or a combination thereof . 07-21-aia AIA Claim s 3 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Al-Rasheed in view of Megherby and further in view of Fasola, Sommer, and Aher et al. (Qadri, M. T., & Asif, M. (2009, April). Automatic number plate recognition system for vehicle identification using optical character recognition. In 2009 international conference on education technology and computer (pp. 335-338). IEEE.; hereinafter Aher ) . Regarding claim 3 , Al-Rasheed, Megherby, Fasola, and Sommer disclose the subject matter of claim 1 as recited in the claim and applied above. Additionally, Al-Rasheed discloses the subject matter indicated in bold below: . . . wherein the instructions are configured to, when executed by the controller, cause the autonomous driving control apparatus to: identify the license plate information and the location information of the at least one vehicle using image data (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”) , . . . While Al-Rasheed discloses identifying license plate information and the location information of the at least one vehicle using image data, it does not appear to explicitly disclose identifying license plate information and the location information of the at least one vehicle, using at least one of a result of sensor fusion about the image data, a result of an optical character recognition (OCR) for the image data, or a combination thereof. Aher teaches the subject matter underlined below: . . . identify license plate information, using at least one of a result of sensor fusion about the image data, a result of an optical character recognition (OCR) for the image data (see Aher at least pg. 2517, section 4.a. “ The Automatic number plate recognition system works in three steps. The first step is the detection and capturing a vehicle image, the second steps is the detection and extraction of number plate in an image. The third step is to use image segmentation technique to get individual character and optical character recognition (OCR) to recognize the individual character with the help of database stored for each and every alphanumeric character. ”) , or a combination thereof. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the identifying license plate information and the location information of the at least one vehicle using image data of Al-Rasheed with the identifying license plate information, using a result of an optical character recognition (OCR) for the image data as taught by Aher to identifying license plate information and the location information of the at least one vehicle, using a result of an optical character recognition (OCR) for the image data. Doing so would provide a means to enable downstream system operations, such as notification to a user when parking meter time is running low, as recognized by Al-Rasheed (see Al-Rasheed at least [0044] “ When the balance time is low, i.e., less than a threshold, the cloud server 470 may search in a database for the contact information of the operator (represented as 403 in FIGS. 4B-4D) of the vehicle 402. ”). Regarding claim 18 , Al-Rasheed, Megherby, Fasola, and Sommer disclose the subject matter of claim 16 as recited in the claim and applied above. Additionally, Al-Rasheed discloses the subject matter indicated in bold below: . . . wherein the detecting of the location information comprises: identifying, by the controller, the license plate information and the location information of the at least one vehicle (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”) , . . . While Al-Rasheed discloses identifying license plate information and the location information of the at least one vehicle using image data, it does not appear to explicitly disclose identifying license plate information and the location information of the at least one vehicle, using at least one of a result of sensor fusion about the image data, a result of an optical character recognition (OCR) for the image data, or a combination thereof. Aher teaches the subject matter underlined below: . . . identifying license plate information using at least one of a result of sensor fusion about the image data, a result of an optical character recognition (OCR) for the image data (see Aher at least pg. 2517, section 4.a. “ The Automatic number plate recognition system works in three steps. The first step is the detection and capturing a vehicle image, the second steps is the detection and extraction of number plate in an image. The third step is to use image segmentation technique to get individual character and optical character recognition (OCR) to recognize the individual character with the help of database stored for each and every alphanumeric character. ”) , or a combination thereof. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the identifying license plate information and the location information of the at least one vehicle using image data of Al-Rasheed with the identifying license plate information, using a result of an optical character recognition (OCR) for the image data as taught by Aher to identifying license plate information and the location information of the at least one vehicle, using a result of an optical character recognition (OCR) for the image data. Doing so would provide a means to enable downstream system operations, such as notification to a user when parking meter time is running low, as recognized by Al-Rasheed (see Al-Rasheed at least [0044] “ When the balance time is low, i.e., less than a threshold, the cloud server 470 may search in a database for the contact information of the operator (represented as 403 in FIGS. 4B-4D) of the vehicle 402. ”) . 07-21-aia AIA Claim s 4 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Al-Rasheed in view of Megherby and further in view of Fasola, Sommer, Aher, and Huimin (CN 115188216 A; hereinafter Huimin ) . Regarding claim 4 , Al-Rasheed, Megherby, Fasola, Sommer, and Aher disclose the subject matter of claim 3 as recited in the claim and applied above. While Al-Rasheed discloses instructions that are configured to, when executed by the controller, cause the autonomous driving control apparatus to: obtain the license plate information and receive the identification information (see Al-Rasheed at least see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”), it does not appear to explicitly disclose comparing the license plate information and the location information with the identification information to calculate the error. Huimin teaches using license plate information associated with a parking spot to navigate a mobility device in a parking lot (see Huimin at least pg. 2, paragraphs 8-11 “ determining a license plate number of the target vehicle based on the vehicle searching instruction; finding out target parking space information corresponding to the license plate number of the target vehicle from the vehicle parking information; determining a vehicle searching route matched with the target parking space information by using the navigation unit; and moving to a target parking space corresponding to the target parking space information according to the vehicle searching route. ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the obtaining the license plate information and receiving the identification information of Al-Rasheed with the using license plate information associated with a parking spot to navigate a mobility device in a parking lot as taught by Huimin to have the identification information be the expected location of a vehicle based on the license plate number. Doing so would enable the license plate information obtained in the system of Al-Rasheed to be used for navigational purposes, as recognized by Huimin (see Huimin at least pg. 2, paragraphs 9-10 “ finding out target parking space information corresponding to the license plate number of the target vehicle from the vehicle parking information; determining a vehicle searching route matched with the target parking space information by using the navigation unit . . . ”). Fasola teaches comparing collected location data with map data to calibrate sensors (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the obtaining the license plate information and receiving the identification information of Al-Rasheed and the using license plate information associated with a parking spot to navigate a mobility device in a parking lot as taught by Huimin with the comparing collected location data with map data to calibrate sensors as taught by Fasola to compare the license plate information and the location information with the identification information to calculate the error. In addition to calibrating sensors based on observed image data of the surrounding environment with received location information, as described above regarding claim 1, the addition of collected license plate information being compared with surroundings location information that includes the expected location of a vehicle based on the license plate number provides an additional piece of redundant information that is used for sensor calibration and, therefore, error computation. Doing so would provide an alternative means for calibrating sensors using available collected and pre-recorded information and therefore would reduce the error present during localization when using sensors for navigation. Regarding claim 19 , Al-Rasheed, Megherby, Fasola, Sommer, and Aher disclose the subject matter of claim 18 as recited in the claim and applied above. While Al-Rasheed discloses obtaining the license plate information and receiving the identification information (see Al-Rasheed at least see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”), it does not appear to explicitly disclose comparing the license plate information and the location information with the identification information to calculate the error. Huimin teaches using license plate information associated with a parking spot to navigate a mobility device in a parking lot (see Huimin at least pg. 2, paragraphs 8-11 “ determining a license plate number of the target vehicle based on the vehicle searching instruction; finding out target parking space information corresponding to the license plate number of the target vehicle from the vehicle parking information; determining a vehicle searching route matched with the target parking space information by using the navigation unit; and moving to a target parking space corresponding to the target parking space information according to the vehicle searching route. ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the obtaining the license plate information and receiving the identification information of Al-Rasheed with the using license plate information associated with a parking spot to navigate a mobility device in a parking lot as taught by Huimin to have the identification information be the expected location of a vehicle based on the license plate number. Doing so would enable the license plate information obtained in the system of Al-Rasheed to be used for navigational purposes, as recognized by Huimin (see Huimin at least pg. 2, paragraphs 9-10 “ finding out target parking space information corresponding to the license plate number of the target vehicle from the vehicle parking information; determining a vehicle searching route matched with the target parking space information by using the navigation unit . . . ”). Fasola teaches comparing collected location data with map data to calibrate sensors (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the obtaining the license plate information and receiving the identification information of Al-Rasheed and the using license plate information associated with a parking spot to navigate a mobility device in a parking lot as taught by Huimin with the comparing collected location data with map data to calibrate sensors as taught by Fasola to compare the license plate information and the location information with the identification information to calculate the error. In addition to calibrating sensors based on observed image data of the surrounding environment with received location information, as described above regarding claim 1, the addition of collected license plate information being compared with surroundings location information that includes the expected location of a vehicle based on the license plate number provides an additional piece of redundant information that is used for sensor calibration and, therefore, error computation. Doing so would provide an alternative means for calibrating sensors using available collected and pre-recorded information and therefore would reduce the error present during localization when using sensors for navigation . 07-21-aia AIA Claim s 5 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Al-Rasheed in view of Megherby and further in view of Fasola, Sommer, Aher, Agisoft ([JMR]. (2016, March 31). Re: Do I need to calibrate cameras? [Online forum post]. Agisoft.; hereinafter Agisoft ), and Fan et al. (Fan, J., Huang, Y., Shan, J., Zhang, S., & Zhu, F. (2019). Extrinsic Calibration between a Camera and a 2D Laser Rangefinder using a Photogrammetric Control Field. Sensors (Basel, Switzerland), 19(9), 2030.; hereinafter Fan ) . Regarding claim 5 , Al-Rasheed, Megherby, Fasola, Sommer, and Aher disclose the subject matter of claim 3 as recited in the claim and applied above. While Al-Rasheed discloses instructions that are configured to, when executed by the controller, cause the autonomous driving control apparatus to perform specific functions, a camera, and LiDAR sensors (see Al-Rasheed at least [0029] “ The camera 255 may operate at visible light frequencies or infrared frequencies and may be coupled to an ultrasonic sensor or a Light Detection and Ranging (LiDAR) sensor. ”; [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”; [0075] “. . . may include one or more computer processor(s) . . . ”), it does not appear to explicitly disclose performing extrinsic calibration for the at least one sensor, before controlling the mobility device to move to the specified place nor mapping point cloud data obtained using the sensor device to two-dimensional (2D) image data to generate the result of the sensor fusion, based on a result of performing the extrinsic calibration and an intrinsic parameter of the at least one sensor. Agisoft teaches performing camera sensor calibration prior to usage (see Agisoft at least post 2, paragraph 1 “ Pre[-]calibration is a need under certain circumstances. For example an object that cannot fill a frame due to its linear shape (a knife) ”; post 2, paragraph 2 “ Also can be recommended if object's poor texture gives little opportunity for a reliable self[-]calibration ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the camera sensor of Al-Rasheed with the camera sensor calibration prior to usage as taught by Agisoft to perform calibration for the at least one sensor, before controlling the mobility device to move to the specified place. Doing so would improve accuracy of the camera readings. While Al-Rasheed and Agisoft disclose performing calibration for the at least one sensor, before controlling the mobility device to move to the specified place, they do not appear to explicitly disclose performing extrinsic calibration for the at least one sensor nor mapping point cloud data obtained using the sensor device to two-dimensional (2D) image data to generate the result of the sensor fusion, based on a result of performing the extrinsic calibration and an intrinsic parameter of the at least one sensor. Fan teaches the subject matter underlined below: . . . perform extrinsic calibration for the at least one sensor (see Fan at least Abstract “ The combination of a camera and a 2D laser rangefinder (LRF) is widely used in robotics, mapping, and unmanned driving to simultaneously obtain the 3D geometry and color texture of a scene . . . Extrinsic calibration between the camera and the LRF is necessary. ”), . . . and map point cloud data obtained using the sensor device to two-dimensional (2D) image data to generate the result of the sensor fusion, based on a result of performing the extrinsic calibration and an intrinsic parameter of the at least one sensor (see Fan at least Introduction paragraph 1 “ High-resolution cameras and 2D laser rangefinders (LRFs) are often combined in mobile mapping [1], object detection [2], and simultaneous localization and mapping [3,4] due to their small size, low cost, and high flexibility . . . To take full advantages of the two sensors and obtain the 3D geometry and color texture information of a scene, data fusion of the two sensors is needed. ”; Introduction paragraph 2 “ First, the laser range data captured by the 2D LRF record only one line formed by the intersection points of the laser scanning plane and the object surface [(i.e., intrinsic properties of 2D LRF influence the scope of calibration)]. ”; Section 2.1, paragraph 2 “ Extrinsic calibration aims to obtain the extrinsic parameters that define the rigid relationship, that is, the rotation matrix and translation vector between two coordinate systems. Let (R CW |T CW ) and (R LW |T LW ) denote the extrinsic parameters of the camera and LRF coordinate systems, respectively, with respect to the control field coordinate system [(i.e. mapping point cloud data to image data)]. ”; Section 2.3, paragraph 3 “ . . . the camera used for calibration in our integrated sensor is a fisheye camera [(i.e., captures 2D images)] . . . ”) . It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the a camera, and LiDAR sensors of Al-Rasheed and the performing calibration for the at least one sensor, before controlling the mobility device to move to the specified place of Al-Rasheed and Agisoft with the performing extrinsic calibration for the at least one sensor and mapping point cloud data obtained using the sensor device to two-dimensional (2D) image data to generate the result of the sensor fusion, based on a result of performing the extrinsic calibration and an intrinsic parameter of the at least one sensor as taught by Fan to perform calibration for the at least one sensor, before controlling the mobility device to move to the specified place, and map point cloud data obtained using the sensor device to two-dimensional (2D) image data to generate the result of the sensor fusion, based on a result of performing the extrinsic calibration and an intrinsic parameter of the at least one sensor. Doing so would simply substitute the LiDAR sensor of Al-Rasheed with the 2D laser range rangefinder as taught by Fan for point cloud data acquisition and would subsequently prevent data misregistration, as recognized by Fan (see Fan at least Abstract “ . . . data misregistration between the camera and the LRF frequently occurs due to the difficulty of precise installation and alignment between them. Extrinsic calibration between the camera and the LRF is necessary. ”). Regarding claim 20 , Al-Rasheed, Megherby, Fasola, Sommer, and Aher disclose the subject matter of claim 18 as recited in the claim and applied above. While Al-Rasheed discloses a controller, a camera, and LiDAR sensors (see Al-Rasheed at least [0029] “ The camera 255 may operate at visible light frequencies or infrared frequencies and may be coupled to an ultrasonic sensor or a Light Detection and Ranging (LiDAR) sensor. ”; [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”; [0075] “. . . may include one or more computer processor(s) . . . ”), it does not appear to explicitly disclose performing extrinsic calibration for the at least one sensor, before controlling the mobility device to move to the specified place nor mapping point cloud data obtained using the sensor device to two-dimensional (2D) image data to generate the result of the sensor fusion, based on a result of performing the extrinsic calibration and an intrinsic parameter of the at least one sensor. Agisoft teaches performing camera sensor calibration prior to usage (see Agisoft at least post 2, paragraph 1 “ Pre[-]calibration is a need under certain circumstances. For example an object that cannot fill a frame due to its linear shape (a knife) ”; post 2, paragraph 2 “ Also can be recommended if object's poor texture gives little opportunity for a reliable self[-]calibration ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the camera sensor of Al-Rasheed with the camera sensor calibration prior to usage as taught by Agisoft to perform calibration for the at least one sensor, before controlling the mobility device to move to the specified place. Doing so would improve accuracy of the camera readings. While Al-Rasheed and Agisoft disclose performing calibration for the at least one sensor, before controlling the mobility device to move to the specified place, they do not appear to explicitly disclose performing, by the controller, extrinsic calibration for at least one sensor of the sensor device, before controlling the mobility device to move to the specified place nor mapping, by the controller, point cloud data obtained using the sensor device to two-dimensional (2D) image data to generate the result of the sensor fusion, based on a result of performing the extrinsic calibration and an intrinsic parameter of the at least one sensor. Fan teaches the subject matter underlined below: . . . further comprising: performing extrinsic calibration for at least one sensor of the sensor device (see Fan at least Abstract “ The combination of a camera and a 2D laser rangefinder (LRF) is widely used in robotics, mapping, and unmanned driving to simultaneously obtain the 3D geometry and color texture of a scene . . . Extrinsic calibration between the camera and the LRF is necessary. ”), . . . ; and mapping point cloud data obtained using the sensor device to two-dimensional (2D) image data to generate the result of the sensor fusion, based on a result of performing the extrinsic calibration and an intrinsic parameter of the at least one sensor (see Fan at least Introduction paragraph 1 “ High-resolution cameras and 2D laser rangefinders (LRFs) are often combined in mobile mapping [1], object detection [2], and simultaneous localization and mapping [3,4] due to their small size, low cost, and high flexibility . . . To take full advantages of the two sensors and obtain the 3D geometry and color texture information of a scene, data fusion of the two sensors is needed. ”; Introduction paragraph 2 “ First, the laser range data captured by the 2D LRF record only one line formed by the intersection points of the laser scanning plane and the object surface [(i.e., intrinsic properties of 2D LRF influence the scope of calibration)]. ”; Section 2.1, paragraph 2 “ Extrinsic calibration aims to obtain the extrinsic parameters that define the rigid relationship, that is, the rotation matrix and translation vector between two coordinate systems. Let (R CW |T CW ) and (R LW |T LW ) denote the extrinsic parameters of the camera and LRF coordinate systems, respectively, with respect to the control field coordinate system [(i.e. mapping point cloud data to image data)]. ”; Section 2.3, paragraph 3 “ . . . the camera used for calibration in our integrated sensor is a fisheye camera [(i.e., captures 2D images)] . . . ”) . It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the a camera, and LiDAR sensors of Al-Rasheed and the performing calibration for the at least one sensor, before controlling the mobility device to move to the specified place of Al-Rasheed and Agisoft with the performing extrinsic calibration for the at least one sensor and mapping point cloud data obtained using the sensor device to two-dimensional (2D) image data to generate the result of the sensor fusion, based on a result of performing the extrinsic calibration and an intrinsic parameter of the at least one sensor as taught by Fan to perform, by the controller, extrinsic calibration for at least one sensor of the sensor device, before controlling the mobility device to move to the specified place and map, by the controller, point cloud data obtained using the sensor device to two-dimensional (2D) image data to generate the result of the sensor fusion, based on a result of performing the extrinsic calibration and an intrinsic parameter of the at least one sensor. Doing so would simply substitute the LiDAR sensor of Al-Rasheed with the 2D laser range rangefinder as taught by Fan for point cloud data acquisition and would subsequently prevent data misregistration, as recognized by Fan (see Fan at least Abstract “ . . . data misregistration between the camera and the LRF frequently occurs due to the difficulty of precise installation and alignment between them. Extrinsic calibration between the camera and the LRF is necessary. ”) . 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Al-Rasheed in view of Megherby and further in view of Fasola, Sommer, Miura et al. (Miura, J., Negishi, Y., & Shirai, Y. (2006). Adaptive robot speed control by considering map and motion uncertainty. Robotics and Autonomous Systems, 54(2), 110-117.; hereinafter Miura ), and Hossain et al. (Hossain, T., Habibullah, H., & Islam, R. (2022). Steering and speed control system design for autonomous vehicles by developing an optimal hybrid controller to track reference trajectory. Machines, 10(6), 420.; hereinafter Hossain ) . Regarding claim 7 , Al-Rasheed, Megherby, Fasola, and Sommer disclose the subject matter of claim 1 as recited in the claim and applied above. While Al-Rasheed discloses instructions are configured to, when executed by the controller, cause the autonomous driving control apparatus to perform actions and obtaining identification information (see Al-Rasheed at least see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”), it does not appear to explicitly disclose identifying, based on the error identified based on the location information and the identification information being within the specified range, a driving path from a current location of the mobility device to a destination nor increasing, based on the driving path being a straight path, a speed of the mobility device. Fasola teaches identifying an error based on the location information and the identification information (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the obtaining identification information of Al-Rasheed with the identifying an error based on the location information and the identification information as taught by Fasola to identify an error based on the location information and the identification information. The examiner supplies the same rationale for the combination of these references as applied above in claim 1. While Al-Rasheed and Fasola disclose identifying an error based on the location information and the identification information, they do not appear to explicitly disclose identifying, based on the error identified based on the location information and the identification information being within the specified range, a driving path from a current location of the mobility device to a destination nor increasing, based on the driving path being a straight path, a speed of the mobility device. Sommer discloses an acceptable range for sensor errors (see Sommer at least pg. 6, paragraphs 1-3 “ Now let’s assume that the maximum deviation tolerance is 0.20%. Using the data from the calibration sheet, we see from the graph that some deviations are greater than the maximum deviation allowed of 0.20%. Therefore, sensor calibration is required. ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the identifying an error based on the location information and the identification information of Al-Rasheed and Fasola with the acceptable range for sensor errors as taught by Sommer to confirm that the error identified based on the location information and the identification information is within the specified range. The examiner supplies the same rationale for the combination of these references as applied above in claim 1. While Al-Rasheed, Fasola, and Sommer disclose confirming that the error identified based on the location information and the identification information is within the specified range, they do not appear to explicitly disclose identifying, based on the error identified based on the location information and the identification information being within the specified range, a driving path from a current location of the mobility device to a destination nor increasing, based on the driving path being a straight path, a speed of the mobility device. Miura teaches the subject matter underlined below: . . . identifying, based on the uncertainty in location identified, a driving path from a current location of the mobility device to a destination (see Miura at least Section 3, paragraph 1 “ This section describes a method for limiting the robot speed so that the robot does not enter an undecided region whose vacancy has not been sufficiently decided. ”; Section 3.3, paragraph 3 “ Fig. 10 illustrates the process of path planning. The planner first calculates the circular path which connects the current robot position (P 0 in the figure) and a destination (G 0 ) and satisfies the orientation constraint at the current position (arc P 0 V 0 G 0 ). If this path is safe, it is selected. ”) ; . . . It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the confirming that the error identified based on the location information and the identification information is within the specified range of Al-Rasheed, Fasola, and Sommer with the identifying, based on the uncertainty in location identified, a driving path from a current location of the mobility device to a destination as taught by Miura to identify, based on the error identified based on the location information and the identification information being within the specified range, a driving path from a current location of the mobility device to a destination. Doing so would prevent sudden acceleration or deceleration and improve safety, as recognized by Miura (see Miura at least Section 3.1, paragraph 2" One motion strategy of the robot is to reach an undecided region at the highest speed and observe there; but this may result in an undesirable sudden acceleration/deceleration. We, therefore, control the robot speed so that the robot can make a number of observations large enough to be confident with the vacancy of the region until it reaches there. We call such a speed a safe speed. "). While Al-Rasheed, Fasola, Sommer, and Miura disclose identifying, based on the error identified based on the location information and the identification information being within the specified range, a driving path from a current location of the mobility device to a destination, they do not appear to explicitly disclose increasing, based on the driving path being a straight path, a speed of the mobility device. Hossain teaches the subject matter indicated with dashed underline below: . . . increase, based on the driving path being a straight path, a speed of the mobility device (see Hossain at least Section 3.1.1, paragraph 4 “ . . . only dangerous curves are considered to reduce vehicle speed. ”; Section 4, Experimental Results subsection, paragraph 2 “ When the value of the curvature is high, the reduction of the speed is also high in the proposed method to ensure that the autonomous vehicle is able to follow the trajectory successfully. Figure 14 shows the speed adjustment of the vehicle depending on the sharp curves of the road, whereas Figure 15 represents the yaw angle comparison for different trajectory tracking controllers. Figure 16 represent the steering angle of the proposed method. ”; Figures 14, 15, and 16- when the vehicle is navigating straight segments of road, the speed increases up to a maximum (e.g., between t=0 and t=5, between t=10 and t=20) . It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the identifying, based on the error identified based on the location information and the identification information being within the specified range, a driving path from a current location of the mobility device to a destination of Al-Rasheed, Fasola, Sommer, and Miura with the increasing, based on the driving path being a straight path, a speed of the mobility device as taught by Hossain to increase, based on the driving path being a straight path, a speed of the mobility device. Doing so would allow the vehicle to move at maximum speed when no dangerous curves require the vehicle to slow, as recognized by Hossain (see Hossain at least Section 4, Experimental Results subsection, paragraph 2 “ When the value of the curvature is high, the reduction of the speed is also high in the proposed method to ensure that the autonomous vehicle is able to follow the trajectory successfully. ”) . 07-21-aia AIA Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Al-Rasheed in view of Megherby and further in view of Fasola, Sommer, and Zhichuan et al. (CN 112572465 A; hereinafter Zhichuan ) . Regarding claim 8 , Al-Rasheed, Megherby, Fasola, and Sommer disclose the subject matter of claim 1 as recited in the claim and applied above. While Al-Rasheed discloses instructions are configured to, when executed by the controller, cause the autonomous driving control apparatus to perform actions and obtaining identification information (see Al-Rasheed at least see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”), it does not appear to explicitly disclose identifying, based on the error identified based on the location information and the identification information being within the specified range, a driving path from a current location of the mobility device to a destination nor increasing, based on the driving path being a straight path, a speed of the mobility device. Fasola teaches identifying an error based on the location information and the identification information (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the obtaining identification information of Al-Rasheed with the identifying an error based on the location information and the identification information as taught by Fasola to identify an error based on the location information and the identification information. The examiner supplies the same rationale for the combination of these references as applied above in claim 1. While Al-Rasheed and Fasola disclose identifying an error based on the location information and the identification information, they do not appear to explicitly disclose identifying, based on the error identified based on the location information and the identification information being within the specified range, a driving path from a current location of the mobility device to a destination nor increasing, based on the driving path being a straight path, a speed of the mobility device. Sommer discloses an acceptable range for sensor errors (see Sommer at least pg. 6, paragraphs 1-3 “ Now let’s assume that the maximum deviation tolerance is 0.20%. Using the data from the calibration sheet, we see from the graph that some deviations are greater than the maximum deviation allowed of 0.20%. Therefore, sensor calibration is required. ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the identifying an error based on the location information and the identification information of Al-Rasheed and Fasola with the acceptable range for sensor errors as taught by Sommer to confirm that the error identified based on the location information and the identification information is within the specified range. The examiner supplies the same rationale for the combination of these references as applied above in claim 1. While Al-Rasheed, Fasola, and Sommer disclose confirming that the error identified based on the location information and the identification information is within the specified range, they do not appear to explicitly disclose decreasing, based on the error identified based on the location information and the identification information being out of the specified range, a real-time driving speed of the mobility device. Zhichuan teaches the subject matter underlined below: . . . decrease, based on the error identified in the sensor, a real-time driving speed of the mobility device (see Zhichuan at least pg. 7, paragraphs 3-4 “ . . . when the decision controller determines that the sensing system is a secondary failure, it may issue a related instruction to perform the following processing: A. the vehicle speed is immediately reduced to a predetermined speed (e.g., 15km/h) while keeping the vehicle direction unchanged . . .”) . It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the disclose confirming that the error identified based on the location information and the identification information is within the specified range of Al-Rasheed, Fasola, and Sommer with the decreasing, based on the error identified in the sensor, a real-time driving speed of the mobility device as taught by Zhichuan to decrease, based on the error identified based on the location information and the identification information being out of the specified range, a real-time driving speed of the mobility device.. Doing so would improve safety, as recognized by Zhichuan (see Zhichuan at least pg. 5, paragraph 4 " In one embodiment, to further ensure driving safety, the top speed of the vehicle may be reduced. ") . 07-21-aia AIA Claim s 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Al-Rasheed in view of Megherby and further in view of Fasola, Sommer, and Huimin . Regarding claim 9 , Al-Rasheed, Megherby, Fasola, and Sommer disclose the subject matter of claim 1 as recited in the claim and applied above. Additionally, Al-Rasheed discloses the subject matter indicated in bold below: . . . wherein the instructions are configured to, when executed by the controller, cause the autonomous driving control apparatus to: detect, using the sensor device, first location information and first license plate information of a first vehicle of the at least one vehicle (see Al-Rasheed at least [0026] “ . . . the transceiver 252 may exchange wireless signals with global or regional navigation systems such as Global Positioning System (GPS) [(i.e., detect location information)]. ”; [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402 [(i.e., detect license plate information)]. ”) ; receive, using a communication device, first identification information of the first vehicle (see Al-Rasheed at least [0041] “ The cloud server 470 may then dispatch a mobile robot device 451 [(i.e., communication device inherently required to interface between cloud server and mobility device)] . . .”; [0042] “ After being dispatched to the parking spot 401 [(i.e., identification information)], the mobile robot device 451 may navigate the parking lot 400 and find its way to the parking spot 401. In the navigation process, the mobile robot device 451 may rely on the map stored in its memory, and may use signals from external navigation systems. ”) ; and . . . While Al-Rasheed discloses detecting, using the sensor device, first location information and first license plate information of a first vehicle of the at least one vehicle and receiving, using a communication device, first identification information of the first vehicle, it does not appear to explicitly disclose comparing, based on a number included in the first identification information corresponding to the first license plate information, the first location information with location information included in the first identification information to calculate the error. Huimin teaches using license plate information associated with a parking spot to navigate a mobility device in a parking lot (see Huimin at least pg. 2, paragraphs 8-11 “ determining a license plate number of the target vehicle based on the vehicle searching instruction; finding out target parking space information corresponding to the license plate number of the target vehicle from the vehicle parking information; determining a vehicle searching route matched with the target parking space information by using the navigation unit; and moving to a target parking space corresponding to the target parking space information according to the vehicle searching route. ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the detecting, using the sensor device, first location information and first license plate information of a first vehicle of the at least one vehicle and receiving, using a communication device, first identification information of the first vehicle of Al-Rasheed with the using license plate information associated with a parking spot to navigate a mobility device in a parking lot as taught by Huimin to confirm that a number included in the first identification information corresponds to the first license plate information. Doing so would enable the license plate information obtained in the system of Al-Rasheed to be used for navigational purposes, as recognized by Huimin (see Huimin at least pg. 2, paragraphs 9-10 “ finding out target parking space information corresponding to the license plate number of the target vehicle from the vehicle parking information; determining a vehicle searching route matched with the target parking space information by using the navigation unit . . . ”). Fasola teaches comparing collected location data with map data to calibrate sensors (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the detecting, using the sensor device, first location information and first license plate information of a first vehicle of the at least one vehicle, receiving, using a communication device, first identification information of the first vehicle, and confirm that a number included in the first identification information corresponds to the first license plate information of Al-Rasheed and Huimin with the comparing collected location data with map data to calibrate sensors as taught by Fasola to compare, based on a number included in the first identification information corresponding to the first license plate information, the first location information with location information included in the first identification information to calculate the error. In the event that the confirmation of matching results as described by Al-Rasheed and Huimin fails, the sensors require calibration and can be calibrated by comparing the obtained location information, such as current location observed by the mobility robot, with location information included in the identification information, such as expected location of the vehicle based on the occupied parking space. Doing so would provide an alternative means for calibrating sensors using available collected and pre-recorded information and therefore would reduce the error present during localization when using sensors for navigation. Regarding claim 10 , Al-Rasheed, Megherby, Fasola, Sommer, and Huimin disclose claim 9 as recited in the claim and applied above. While Al-Rasheed discloses obtaining location information and license plate information (see Al-Rasheed at least [0026] “ . . . the transceiver 252 may exchange wireless signals with global or regional navigation systems such as Global Positioning System (GPS) [(i.e., detect location information)]. ”; [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402 [(i.e., detect license plate information)]. ”), it does not appear to explicitly disclose calibrating the sensor device by using at least one of: a current location of the mobility device, the current location being obtained using the sensor device, a relative location of the first vehicle relative to the mobility device, the first location information, the first license plate information, or a combination thereof. Fasola teaches the subject matter underlined below: . . . calibrate the sensor device by using at least one of: a current location of the mobility device (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”) , the current location being obtained using the sensor device (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”) , a relative location of the first vehicle relative to the mobility device, the first location information, the first license plate information, or a combination thereof (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”) . It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the obtaining location information of Al-Rasheed with the calibrating the sensor device by using at least one of: a current location of the mobility device, the current location being obtained using the sensor device, or a combination thereof as taught by Fasola to calibrate the sensor device by using at least one of: a current location of the mobility device, the current location being obtained using the sensor device, or a combination thereof. Doing so would reduce the error present during localization when using sensors for navigation . 07-21-aia AIA Claim s 11-12 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Al-Rasheed et al. (US 20210319699 A1; hereinafter Al-Rasheed ) in view of Fasola et al. (US 9884623 B2; hereinafter Fasola ) and further in view of Sommer (Sommer, S. (2019). What is Sensor Calibration and Why is it Important. Real-Pars, Dutchland, Tech. Rep.; hereinafter Sommer ) and Megherby et al. (US 20210241377 A1; hereinafter Megherby ) . Regarding claim 11 , Al-Rasheed discloses the subject matter indicated in bold below: An autonomous driving control system (see Al-Rasheed at least [0025] “ . . . a mobile robot device 251 used in a parking control system . . . ”) , comprising: an autonomous driving control apparatus (see Al-Rasheed at least [0025] “ . . . a mobile robot device 251 used in a parking control system . . . ”) configured to: control a mobility device to move to a specified place to obtain, using a sensor device, first image data of at least one vehicle in the specified place (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”; [0071] “ . . . the mobile robot device may navigate the map of the parking lot and move to the parking spot that the mobile robot device is dispatched to. ”; [0072] “ . . . the mobile robot device may capture the identification information of the vehicle . . . ”) , . . . receive identification information corresponding to the at least one vehicle (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”) , and . . . a computing device (see Al-Rasheed [0075] “ Embodiments of the invention may be implemented on a computing system. ”) configured to: obtain second data of the at least one vehicle using at least one sensor disposed in the specified place (see Al-Rasheed at least [0032] “ The sensing circuit 331 may detect the entry and exit of a vehicle in and out of the parking spot monitored by the sensor 330. The sensing circuit 331 may be an ultrasonic detector or a magnetometer, or use any other suitable sensing technologies. ”; [0039] “ The entry of the vehicle 402 may be detected by the sensor 430, which may immediately send a first wireless signal 491 to a transceiver 471 of a cloud server 470. ”) , generate, based on the second data, the identification information corresponding to the at least one vehicle (see Al-Rasheed at least [0033] “ The transmitter 332 may transmit a wireless signal to the cloud server upon detecting the vehicle entering and exiting the parking spot. The wireless signal may include information unique to the sensor 330, such as a sensor identification or a location of the sensor 330. ”) , and transmit the identification information to the autonomous driving control apparatus (see Al-Rasheed at least [0041] “ The cloud server 470 may then dispatch a mobile robot device 451 from the docking station 450 to the parking spot 401. ”) . While Al-Rasheed discloses controlling a mobility device to move to a specified place to obtain, using a sensor device, first image data of at least one vehicle in the specified place, and receiving identification information corresponding to the at least one vehicle, it does not appear to explicitly disclose detecting, using the first image data, location information of the at least one vehicle nor calibrating, based on an error identified based on the location information and the identification information being out of a specified range, the sensor device using at least one of the location information, the identification information, the error identified based on the location information and the identification information, or a combination thereof. Furthermore, while Al-Rasheed discloses a computing device obtaining second data of the at least one vehicle using at least one sensor disposed in the specified place, it does not appear to explicitly disclose obtaining second image data of the at least one vehicle using at least one camera disposed in the specified place. However, Al-Rasheed does disclose using a camera to obtain identification information and using any “suitable sensing technology” to obtain the second data (see Al-Rasheed at least [0032] “ The sensing circuit 331 may detect the entry and exit of a vehicle in and out of the parking spot monitored by the sensor 330. The sensing circuit 331 may be an ultrasonic detector or a magnetometer, or use any other suitable sensing technologies. ”; [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”). Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the camera to obtain identification information of Al-Rasheed with the using any “suitable sensing technology” to obtain the second data of Al-Rasheed to obtain second image data of the at least one vehicle using at least one camera disposed in the specified place. Doing so would serve as a simple substitution of the sensing technology used by the computing system. Still unaddressed, however, is that while Al-Rasheed discloses controlling a mobility device to move to a specified place to obtain, using a sensor device, first image data of at least one vehicle in the specified place, and receiving identification information corresponding to the at least one vehicle, it does not appear to explicitly disclose detecting, using the first image data, location information of the at least one vehicle nor calibrating, based on an error identified based on the location information and the identification information being out of a specified range, the sensor device using at least one of the location information, the identification information, the error identified based on the location information and the identification information, or a combination thereof. Fasola teaches the subject matter underlined below: . . . detect, using the first image data, location information of the at least one vehicle (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”) , . . . calibrate, based on an error identified based on the location information and the identification information, the sensor device using at least one of the location information (see Fasola at least pg. 11, col. 4, lines 18-25 “ Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to that road surface image). ”) , the identification information, the error identified based on the location information and the identification information, or a combination thereof; and . . . It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the controlling a mobility device to move to a specified place to obtain, using a sensor device, first image data of at least one vehicle in the specified place, and receiving identification information corresponding to the at least one vehicle of Al-Rasheed with the detecting, using the first image data, location information of the at least one vehicle and calibrating, based on an error identified based on the location information and the identification information, the sensor device using the location information as taught by Fasola to detect, using the image data, location information of the at least one vehicle and calibrate, based on an error identified based on the location information and the identification information, the sensor device using the location information. Doing so would reduce the error present during localization when using sensors for navigation. While Al-Rasheed and Fasola disclose calibrating, based on an error identified based on the location information and the identification information, the sensor device using the location information, they do not appear to explicitly disclose calibrating, based on an error identified based on the location information and the identification information being out of a specified range, the sensor device. Sommer discloses calibrating sensors only when they show an error outside of a specified range (see Sommer at least pg. 6, paragraphs 1-3 “ Now let’s assume that the maximum deviation tolerance is 0.20%. Using the data from the calibration sheet, we see from the graph that some deviations are greater than the maximum deviation allowed of 0.20%. Therefore, sensor calibration is required. ”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the calibrating, based on an error identified based on the location information and the identification information, the sensor device using the location information of Al-Rasheed and Fasola with the calibrating sensors only when they show an error outside of a specified range as taught by Sommer to calibrate, based on an error identified based on the location information and the identification information being out of a specified range, the sensor device using the location information. Doing so would reduce sensor error via calibration only when necessary, therefore saving resource costs. Regarding claim 12 , Al-Rasheed, Fasola, and Sommer disclose the subject matter of claim 11 as recited in the claim and applied above. Additionally, Al-Rasheed discloses the subject matter indicated in bold below: . . . wherein the computing device is configured to: set a plurality of regions of interest (ROIs) respectively corresponding to a plurality of parking areas in the specified place (see Al-Rasheed at least [0023] “ The parking control system 10 may be used for managing and controlling parking in the parking lot 100 that has a plurality of parking spots 101. ”; [0041] “ In the event the mobile robot device 451 has been dispatched to a plurality of parking spots, the mobile robot device 451 may follow the dispatches one after another. ”) ; and obtain, using the at least one sensor, the second data including the plurality of ROIs (see Al-Rasheed at least [0023] “ The parking control system 10 may be used for managing and controlling parking in the parking lot 100 that has a plurality of parking spots 101. ”; [0032] “ The sensing circuit 331 may detect the entry and exit of a vehicle in and out of the parking spot monitored by the sensor 330. The sensing circuit 331 may be an ultrasonic detector or a magnetometer, or use any other suitable sensing technologies. ”) . While Al-Rasheed discloses setting a plurality of regions of interest (ROIs) respectively corresponding to a plurality of parking areas in the specified place and obtaining, using the at least one sensor, the second data including the plurality of ROIs, it does not appear to explicitly disclose obtaining, using the at least one camera, the second image data including the plurality of ROIs. However, Al-Rasheed does disclose using a camera to obtain identification information and using any “suitable sensing technology” to obtain the second data (see Al-Rasheed at least [0032] “ The sensing circuit 331 may detect the entry and exit of a vehicle in and out of the parking spot monitored by the sensor 330. The sensing circuit 331 may be an ultrasonic detector or a magnetometer, or use any other suitable sensing technologies. ”; [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”). Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the camera to obtain identification information of Al-Rasheed with the using any “suitable sensing technology” to obtain the second data of Al-Rasheed to obtain second image data of the at least one vehicle using at least one camera disposed in the specified place. Further in combination with disclosing obtaining, using the at least one sensor, the second data including the plurality of ROIs, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the obtaining second image data of the at least one vehicle using at least one camera disposed in the specified place and the obtaining, using the at least one sensor, the second data including the plurality of ROIs of Al-Rasheed to obtain, using the at least one camera, the second image data including the plurality of ROIs. Doing so would serve as a simple substitution of the sensing technology used by the computing system. Regarding claim 14 , Al-Rasheed, Fasola, and Sommer disclose the subject matter of claim 12 as recited in the claim and applied above. Additionally, Al-Rasheed discloses the subject matter indicated in bold below: . . . wherein the computing device is configured to: update, based on a specified period, the identification information corresponding to the at least one vehicle in the specified place (see Al-Rasheed at least [0071] “ In the event the mobile robot device has been dispatched to more than one parking spots, the mobile robot device may execute a ranking algorithm to determine the order of the parking spots to visit. The ranking algorithm may be time-based such as first-come-first-serve, may be distance-based such as closest-spot-first, or may be based on a priority system such as reserved-spot-first. ”) . Regarding claim 15 , Al-Rasheed, Fasola, and Sommer disclose the subject matter of claim 12 as recited in the claim and applied above. Additionally, Al-Rasheed discloses the subject matter indicated in bold below: . . . wherein the identification information comprises at least one of: the plurality of ROIs respectively corresponding to the plurality of parking areas (see Al-Rasheed at least [0041] “ In the event the mobile robot device 451 has been dispatched to a plurality of parking spots, the mobile robot device 451 may follow the dispatches one after another. ”) , at least one region of interest (ROT) corresponding to a parking area where a vehicle is parked among the plurality of ROIs (see Al-Rasheed at least [0041] “. . . may then dispatch a mobile robot device 451 from the docking station 450 to the parking spot 401. ”) , an identification number of the at least one ROT, a location of each of the at least one vehicle parked in at least one parking area of the plurality of parking areas (see Al-Rasheed at least [0040] “ From the entry time, the vehicle 402 is considered as parked in the parking spot 401. ”; [0041] “. . . may then dispatch a mobile robot device 451 from the docking station 450 to the parking spot 401. ”) , a vehicle number of each of the at least one vehicle (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”) , a vehicle class of each of the at least one vehicle, coordinates of each of the at least one vehicle, a width of each of the at least one vehicle, a height of each of the at least one vehicle, or a combination thereof . Allowable Subject Matter Claim 13 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims with outstanding claim objections above addressed. The claim recites the limitation “. . . calibrate the senor device further based on a comparison of the license plate information and the identification information, wherein the sensor device comprises at least one of an object detection senor or an image sensor,” which the examiner notes distinguishes itself over the prior art by requiring image sensor calibration in response to the comparison of license plate information with identification information. The closest prior art disclose obtaining image data for at least one region of interest (ROI) corresponding to a parking area where the at least one vehicle is parked among the plurality of ROIs to identify a vehicle number of the at least one vehicle (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”), detecting, using the first image data, license plate information of the at least one vehicle (see Al-Rasheed at least [0043] “ . . . the mobile robot device 451 may use its camera to capture identification information of the vehicle 402. For example, the mobile robot device 451 may capture a picture of the license plate 403 of the vehicle 402. ”), identifying license plate information as a result of an optical character recognition (OCR) for the image data (see Aher at least pg. 2517, section 4.a. “ The Automatic number plate recognition system works in three steps. The first step is the detection and capturing a vehicle image, the second steps is the detection and extraction of number plate in an image. The third step is to use image segmentation technique to get individual character and optical character recognition (OCR) to recognize the individual character with the help of database stored for each and every alphanumeric character. ”), and calibrating sensors when they show an error (see Sommer at least pg. 6, paragraphs 1-3 “ Now let’s assume that the maximum deviation tolerance is 0.20%. Using the data from the calibration sheet, we see from the graph that some deviations are greater than the maximum deviation allowed of 0.20%. Therefore, sensor calibration is required. ”). The prior art fail to disclose or otherwise suggest requiring image sensor calibration in response to the comparison of license plate information with identification information. Thus, even in combination, the prior art would not yield the invention as claimed to one of ordinary skill in the art. Response to Arguments 07-37 AIA Applicant's arguments filed 09/16/2025 have been fully considered but they are not persuasive. (A) Applicant argues, “The alleged combination fails to disclose or suggest at least the above features of claim 11. “First, the alleged combination fails to disclose or suggest at least ‘control a mobility device to move to a specified place to obtain, using a sensor device, first image data of at least one vehicle in the specified place, ... calibrate , based on an error identified based on the location information and the identification information being out of a specified range, the sensor device ...,’ as recited in claim 11. “The Office appears to acknowledge Al-Rasheed does not disclose ‘ calibrate , based on an error identified based on the location information and the identification information being out of a specified range, the sensor device ...,’ but the Office relies on Fasola in an attempt to cure the deficiencies of Al-Rasheed. Office Action, pp. 13-14. However, even if combined, the alleged combination fails to disclose or suggest calibration of a sensor device that is used to obtain image data of at least one vehicle. Rather, the alleged combination calibrates ‘a GPS receiver’ of a vehicle. Fasola, col. 4, lines 18-25. Notably, Fasola's GPS receiver is not a sensor capable of obtaining image data of its vehicle. “Specifically, Fasola states: Pre-existing map data may also be used to calibrate sensors. For example, a set of road surface images may be linked to a precisely known location. This map data may be used to calibrate a GPS receiver of the vehicle (i.e., when the vehicle image sensor captures an image substantially similar to one of the set of road surface images, the GPS receiver is calibrated using the precisely known location linked to the road surface image). Fasola col. 4, lines 18-25. (Emphasis added). “According to the alleged combination, ‘when the vehicle image sensor captures an image substantially similar to one of the set of road surface images,’ ‘the GPS receiver is calibrated using the precisely know location linked to that road surface image.’ Id. “However, the Al-Rasheed-Fasola system does not provide or suggest any configuration to calibrate its ‘vehicle image sensor’ that ‘captures an image’ or any other sensor device that is used to obtain image data of a vehicle. Thus, even if combined, the alleged combination fails to disclose or suggest at least ‘control a mobility device to move to a specified place to obtain, using a sensor device, first image data of at least one vehicle in the specified place, ... calibrate , based on an error identified based on the location information and the identification information being out of a specified range, the sensor device ...,’ as recited in claim 11. (Emphasis added). “Second, the alleged combination fails to disclose or suggest at least "calibrate, based on an error identified based on the location information and the identification information being out of a specified range, the sensor device using at least one of the location information, the identification information, the error identified based on the location information and the identification information, or a combination thereof," as recited in claim 11. The Office appears to acknowledge Al-Rasheed and Fasola fail to disclose or suggest the above features of claim 11, but the Office relies on Sommer in an attempt to cure the deficiencies of Al-Rasheed and Fasola. Office Action at 15-16 . . . “Sommer simply sets the deviation tolerance with respect a single parameter, the electric current (e.g., 4 mA, 8 mA, etc.). Id. However, Sommer's deviation tolerance with respect to the electric current is not relevant to any of the features of Fasola (e.g. Fasola' s precisely known location linked to that road surface image) in calibrating Fasola' s GPS receiver. Fasola, col. 4, lines 18- 25. Furthermore, Sommer simply shows a single parameter (the electric current, which is not relevant to Fasola or the claimed features), thereby failing to disclose or suggest at least ‘calibrate, based on an error identified based on the location information and the identification information being out of a specified range , the sensor device using at least one of the location information, the identification information, the error identified based on the location information and the identification information, or a combination thereof,’ as recited in claim 11. “Applicant respectfully submits that the Office should not use Applicant's claim as a guide in formulating the obviousness rationale. MPEP 2142 states: “To reach a proper determination under 35 U.S.C. 103, the examiner must step backward in time and into the shoes worn by the hypothetical ‘person of ordinary skill in the art’. That time is ‘before the effective filing date of the claimed invention’ for 35 U.S.C. 103 or ‘at the time the invention was made’ for pre-AIA 35 U.S.C. 103. In view of all factual information, the examiner must then make a determination whether the claimed invention ‘as a whole’ would have been obvious at that time to a hypothetical person of ordinary skill in the art. Knowledge of applicant's disclosure must be put aside in reaching this determination , yet kept in mind in order to determine the ‘differences,’ conduct the search and evaluate the ‘subject matter as a whole’ of the invention. The tendency to resort to ‘hindsight’ based upon applicant's disclosure is often difficult to avoid due to the very nature of the examination process. However, impermissible hindsight must be avoided and the legal conclusion must be reached on the basis of the facts gleaned from the prior art . (Emphasis added). “For at least the reasons discussed above, Applicant respectfully submits that claim 11 is allowable over the cited references. “Each of present claims 1 and 16, while different from claim 11, is allowable for at least similar reasons. Each of pending dependent claims, which ultimately depends from claims 1, 11, or 16, is allowable due to its dependency and further in view of the additional features recited therein,” ( from remarks pg. 10-13 ). As to Point (A) , Examiner respectfully disagrees. Applicant appears to argue that the prior art does not disclose obtaining first image data using a sensor device and subsequently calibrating the sensor device because the art relied upon for teaching the claim limitations does not calibrate the sensor device that obtains image data. Applicant appears to further argue that the combination of references used to teach the claim limitation “. . . calibrate, based on an error identified based on the location information and identification information being out of a specified range . . .” relies upon improper hindsight reasoning. Firstly addressing the argument that the prior art does not disclose obtaining first image data using a sensor device and subsequently calibrating the sensor device because the art relied upon for teaching the claim limitations does not calibrate the sensor device that obtains image data, the claims as recited under their broadest reasonable interpretation allow for a sensor device to include more than just a sensor. While not specifically recited in claim 11 as referenced, this interpretation is clearly supported by both the specification and other independent claims in the present application, such as claim 1 which recites “. . . a sensor device including at least one sensor . . .” The sensor device in the combination of Al-Rasheed and Fasola as applied above with respect to claim 11 would reasonably include both the GPS and image sensing elements, as both are information-gathering or sensing means. As such, in light of the specification, the broadest reasonable interpretation of the claims would encompass the entire sensing device that would result from combining Al-Rasheed and Fasola as presented above. Secondly addressing the argument of hindsight reasoning, Applicant is correct in asserting that the relied upon reference Sommer sets a deviation tolerance with respect to a single parameter, electric current. Sommer is relied upon for teaching the concept of calibrating sensors only when they show an error outside of a specified range. Al-Rasheed and Fasola, in combination, collect and utilize location and identification information for performing the functions outlined. The concept of calibrating sensors only when they show an error outside of a specified range as presented in Sommer, when applied to the combined invention of Al-Rasheed and Fasola would yield the claim limitation for which it is relied upon above. Sommer merely uses setting a deviation tolerance with respect to an electric current parameter as an example to communicate the concept of calibrating sensors only when they show an error outside of a specified range. Sommer discloses many reasons as to why sensors should be calibrated in any context, including “ . . . yield[ing] accurate measurements . . . [and making] good control . . . possible. When good control is realized, then the process has the best chance of running efficiently and safely, ” (see Sommer at least pg. 8, paragraph 1), and given that there are sensors involved in the combined invention of Al-Rasheed and Fasola, it would make sense to further apply the teachings of Sommer. As to why sensor calibration should occur only when specific bounds for a parameter are exceeded, one of ordinary skill in the art would recognize that a reasonable amount of sensor noise or other deviation for an application should be expected, and by not forcing sensor calibration when sensors operate within reasonable bounds for the application, resource costs can be saved. The combination of references relies not upon hindsight reasoning provided within the application, but by the standards set forth by 35 U.S.C. 103. See MPEP §2142 for guidance on the standards set forth for establishing a prima facie case of obviousness and MPEP §2143 for examples of valid motivations to combine references . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhang et al. (US 20190218810 A1) discloses a robot used for identifying vehicles in a parking lot and verifying vehicle parking authority using QR code reading technology and an on-board vision system. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TABITHA KRESS whose telephone number is (703) 756-1763. The examiner can normally be reached MTWR 06:30-16:30 CST. 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, Hitesh Patel can be reached at (571) 270-5442. 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. /TABITHA KRESS/Examiner, Art Unit 3667 /Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667 6/1/26 Application/Control Number: 18/375,067 Page 2 Art Unit: 3667 Application/Control Number: 18/375,067 Page 3 Art Unit: 3667 Application/Control Number: 18/375,067 Page 4 Art Unit: 3667 Application/Control Number: 18/375,067 Page 5 Art Unit: 3667 Application/Control Number: 18/375,067 Page 6 Art Unit: 3667 Application/Control Number: 18/375,067 Page 7 Art Unit: 3667 Application/Control Number: 18/375,067 Page 8 Art Unit: 3667 Application/Control Number: 18/375,067 Page 9 Art Unit: 3667 Application/Control Number: 18/375,067 Page 10 Art Unit: 3667 Application/Control Number: 18/375,067 Page 11 Art Unit: 3667 Application/Control Number: 18/375,067 Page 12 Art Unit: 3667 Application/Control Number: 18/375,067 Page 13 Art Unit: 3667 Application/Control Number: 18/375,067 Page 14 Art Unit: 3667 Application/Control Number: 18/375,067 Page 15 Art Unit: 3667 Application/Control Number: 18/375,067 Page 16 Art Unit: 3667 Application/Control Number: 18/375,067 Page 17 Art Unit: 3667 Application/Control Number: 18/375,067 Page 18 Art Unit: 3667 Application/Control Number: 18/375,067 Page 19 Art Unit: 3667 Application/Control Number: 18/375,067 Page 20 Art Unit: 3667 Application/Control Number: 18/375,067 Page 21 Art Unit: 3667 Application/Control Number: 18/375,067 Page 22 Art Unit: 3667 Application/Control Number: 18/375,067 Page 23 Art Unit: 3667 Application/Control Number: 18/375,067 Page 24 Art Unit: 3667 Application/Control Number: 18/375,067 Page 25 Art Unit: 3667 Application/Control Number: 18/375,067 Page 26 Art Unit: 3667 Application/Control Number: 18/375,067 Page 27 Art Unit: 3667 Application/Control Number: 18/375,067 Page 28 Art Unit: 3667 Application/Control Number: 18/375,067 Page 29 Art Unit: 3667 Application/Control Number: 18/375,067 Page 30 Art Unit: 3667 Application/Control Number: 18/375,067 Page 31 Art Unit: 3667 Application/Control Number: 18/375,067 Page 32 Art Unit: 3667 Application/Control Number: 18/375,067 Page 33 Art Unit: 3667 Application/Control Number: 18/375,067 Page 34 Art Unit: 3667 Application/Control Number: 18/375,067 Page 35 Art Unit: 3667 Application/Control Number: 18/375,067 Page 36 Art Unit: 3667 Application/Control Number: 18/375,067 Page 37 Art Unit: 3667 Application/Control Number: 18/375,067 Page 38 Art Unit: 3667 Application/Control Number: 18/375,067 Page 39 Art Unit: 3667 Application/Control Number: 18/375,067 Page 40 Art Unit: 3667 Application/Control Number: 18/375,067 Page 41 Art Unit: 3667 Application/Control Number: 18/375,067 Page 42 Art Unit: 3667 Application/Control Number: 18/375,067 Page 43 Art Unit: 3667 Application/Control Number: 18/375,067 Page 44 Art Unit: 3667 Application/Control Number: 18/375,067 Page 45 Art Unit: 3667 Application/Control Number: 18/375,067 Page 46 Art Unit: 3667 Application/Control Number: 18/375,067 Page 47 Art Unit: 3667 Application/Control Number: 18/375,067 Page 48 Art Unit: 3667 Application/Control Number: 18/375,067 Page 49 Art Unit: 3667 Application/Control Number: 18/375,067 Page 50 Art Unit: 3667 Application/Control Number: 18/375,067 Page 51 Art Unit: 3667 Application/Control Number: 18/375,067 Page 52 Art Unit: 3667 Application/Control Number: 18/375,067 Page 53 Art Unit: 3667 Application/Control Number: 18/375,067 Page 54 Art Unit: 3667 Application/Control Number: 18/375,067 Page 55 Art Unit: 3667 Application/Control Number: 18/375,067 Page 56 Art Unit: 3667 Application/Control Number: 18/375,067 Page 57 Art Unit: 3667 Application/Control Number: 18/375,067 Page 58 Art Unit: 3667 Application/Control Number: 18/375,067 Page 59 Art Unit: 3667 Application/Control Number: 18/375,067 Page 60 Art Unit: 3667 Application/Control Number: 18/375,067 Page 61 Art Unit: 3667 Application/Control Number: 18/375,067 Page 62 Art Unit: 3667 Application/Control Number: 18/375,067 Page 63 Art Unit: 3667 Application/Control Number: 18/375,067 Page 64 Art Unit: 3667 Application/Control Number: 18/375,067 Page 65 Art Unit: 3667 Application/Control Number: 18/375,067 Page 66 Art Unit: 3667 Application/Control Number: 18/375,067 Page 67 Art Unit: 3667 Application/Control Number: 18/375,067 Page 68 Art Unit: 3667
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