Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Regarding the double patenting rejections, while the request for holding them in abeyance is noted; rejections cannot be held in abeyance. Only objections or requirements as to form of claims can be held in abeyance. (MPEP 804, section I, B, 1. “…As filing a terminal disclaimer, or filing a showing that the claims subject to the rejection are patentably distinct from the reference application’s claims, is necessary for further consideration of the rejection of the claims, such a filing should not be held in abeyance. Only compliance with objections or requirements as to form not necessary for further consideration of the claims may be held in abeyance until allowable subject matter is indicated…”)
The double patenting rejections still apply despite the amended limitations. An updated double patenting rejection appears below.
Applicant’s amendments have overcome to previous 101 rejections; the cleaning operation integrates the claimed invention onto a specific machine and/or cleaning a sensor is not an abstract idea which can be reasonable performed in the human mind.
Applicant’s amendments and/or arguments have overcome the previous 112(a) (new matter rejections) from the non-final rejection
Applicant’s amendments to claims 17-18 have overcome their respective 112(b) rejections of the non-final rejection. The amended claim limitations are such that claims 17-18 are no longer indefinite.
Applicant’s arguments with respect to claim(s) 1, 19, and 20 over Terazawa, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
An updated search and consideration was performed in view of the claim amendments, a piece of prior art was found, US 20220189054 A1, which teaches an autonomous vehicle localization system which includes, in response to detecting a calibration parameter of a sensor of the vehicle being having an error (this parameter detected/determined based on sensed characteristics of objects in the vicinity of the vehicle), triggering a variety of responses by the vehicle which includes initiating a cleaning operation for clean the sensor. Thus in combination with Terazawa renders obvious initiating a cleaning process for the sensor. An updated rejection appears below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The 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.
Claim(s) 1, 6, 9-12, 14, 16-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230147535 A1, “VEHICLE POSITION ESTIMATION DEVICE AND TRAVELING CONTROL DEVICE”, Terazawa and further in view of US 20220189054 A1, “LOCALIZATION BASED ON SEMANTIC OBJECTS”, Kaiser et al
Regarding Claim 1, Terazawa teaches “ A method comprising: receiving, at a computing system, sensor data from at least one sensor associated with a vehicle, the sensor data representing surroundings of the vehicle; detecting, based on the sensor data, one or more objects in the surroundings; determining at least one detection parameter associated with at least one object; analyzing the at least one detection parameter relative to at least one baseline detection parameter, wherein the at least one baseline detection parameter is associated with at least one characteristic of the at least one object; ”( [0081] For example, the adverse environment determination unit F6 may determine whether the surrounding environment corresponds to the adverse environment based on the effective recognition distance calculated by the recognition accuracy evaluation unit F5. For example, the adverse environment determination unit F6 may determine that the environment is adverse when the effective recognition distance is smaller than a predetermined threshold (hereinafter referred to as distance threshold). A specific value of the distance threshold may be set to, for example, within a range of 20 meters to 30 meters. As an example, the distance threshold is set to 25 meters. The distance threshold may be determined according to the designed recognition limit distance of the system or device” Here the vehicle (based on sensor data) determines the actual recognition limit (detection parameter) for landmarks (objects) surrounding the vehicle and compares it to the designed recognition limit distance of the system (baseline detection distance) + [0086]);
Terazawa while teaching a sensor configuration change in general ([0161]-[0162]) however does not teach the specific response being a cleaning process, i.e. it does not teach “and modifying, based on the analysis, ; a configuration of the at least one sensor, wherein modifying the configuration of the at least one sensor comprises triggering a cleaning process for the at least one sensor.”
Kaiser et al teaches a similar autonomous vehicle control system, which includes in response to detecting a degredation the sensing performance of the vehicle’s onboard sensors ([0061] here teaches detecting of a calibration issue with the sensors (camera)), triggering a variety of responses which includes triggering a cleaning process of the sensor([0062] “In some examples, the operation 246 may include, actions based on the calibration parameter. These actions may include, but are not limited to, following a trajectory (e.g., without modification of the trajectory), reducing (e.g., down weighting) a confidence level associated with the sensor data or data derived from the sensor data (e.g., bounding boxes, extents, pose, velocity, classification data, segmentation data, prediction data, etc.), initiating a cleaning operation, scheduling maintenance (e.g., which may include sending an indication of the calibration parameter and/or the sensor data to a remote computing device), reducing a velocity associated with a trajectory (or modifying some other aspect of the trajectory), generating a stopping trajectory, and the like.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application to modify Terazawa to include triggering a sensor cleaning response when detecting a sensing degradation as taught by Kaiser in addition to the slowing down and/or weighting changes taught in Terazawa. One would be motivated to include triggering sensor cleaning to improve the ability of the vehicle to recover operating a full capacity. (Kaiser [0018] “…The localization and calibration techniques described herein can improve a functioning of a computing device by providing a robust method of determining a position in a world and determine inconsistent sensor data … Calibration information can be used to determine when sensor(s) are out of alignment or when calibration data is incorrect, which can avoid problems for other operations such as fusing data, segmenting, classifying, and tracking data, which may be difficult or impossible without calibrated sensors. These and other improvements to the functioning of the computer are discussed herein.”) additionally the combination would have a reasonably expectation of success given that Kaiser already identifies the two same responses Terazawa makes use of (slowing down the vehicle and/or reducing the weights of uncertain sensor data) are also taught by Kaiser as possible responses (Kaiser [0062]) as such the combination is merely adding an additional possible solution for sensor performance (calibration) issues onto Terazawa which is a known equivalent to the already taught solutions (slowing down/de-weighting uncertain sensor data) already taught in Terazawa as shown by Kaiser.
Regarding Claim 6, Terazawa teaches “The method of claim 1, wherein analyzing the at least one detection parameter comprises: determining a difference between the at least one detection parameter and the at least one baseline detection parameter, and determining whether the difference exceeds a threshold difference value.”( [0089] As shown in FIG. 12, the adverse environment level determination unit F62 may evaluate a level of the adverse environment, in other words, a level of deterioration in object recognition performance executed based on the image frame. For example, the adverse environment level may be classified in four level 0 to level 3. A higher level indicates a highly adverse environment. The level of adverse environment can be evaluated based on the effective recognition distance. For example, the adverse environment level determination unit F62 may determine the adverse environment as level 1 when the effective recognition distance is less than a predetermined first distance and equal to or greater than a second distance. The adverse environment level determination unit F62 may determine the adverse environment as level 2 when the effective recognition distance is less than the predetermined second distance and equal to or greater than a third distance. When the effective recognition distance is less than the third distance, the level of adverse environment may be determined as level 3. When the effective recognition distance is equal to or greater than the first distance, the level of adverse environment is determined as level 0, that is, the environment is not adverse.” Here teaches comparing the current detection limit to various thresholds; based on this it exceeding certain thresholds )
Regarding Claim 9, Terazawa teaches “The method of claim 1, further comprising modifying, based on the analysis. a control strategy for the vehicle: wherein modifying the control strategy comprises at least one of: adjusting a speed of the vehicle, modifying a following distance to other objects, or changing a navigation path based on the analysis.”( [0100] When the adverse environment determination unit F6 determines the adverse environment, the deceleration request unit F8 outputs a predetermined deceleration request signal to the driving assist ECU 30. For example, the deceleration request signal may be a signal that requests a decrease of the set value of the target speed by a predetermined amount from the current value. The deceleration amount requested by the deceleration request signal may be a fixed amount, such as 3 km/h, 5 km/h, or 10 km/h. Alternatively, the deceleration amount may be a value correlated to the target speed, such as 10% of the target speed. Alternatively, the deceleration request signal may request deceleration of the current speed to a preset restrict speed. The restrict speed may be set according to the road type. For example, the restrict speed may be set to 60 km/h for expressways, and may be set to 40 km/h for general roads.
[0101] The deceleration request signal may request a decrease of current set value of the target speed as described above, or may request a decrease of ACC upper limit speed. When the current target speed reaches the ACC upper limit speed, the vehicle speed can be indirectly suppressed by lowering the ACC upper limit speed.)
Regarding Claim 10, Terazawa teaches “wherein modifying the configuration of the at least one sensor comprises triggering at least one of: a calibration process for the at least one sensor, or an adjustment of sensor parameters.”([0162] For example, when the adverse environment determination unit F6 determines that the environment is adverse, the weight of the radar detection result used in calculation of the landmark position may be increased compared to the weight used in normal environment. Since the millimeter wave radar 12 is not easily affected by rain, fog, or the like, it is possible to maintain the estimation accuracy of the distance to landmark at a high level by increasing the weight of radar detection result. Alternatively, when the position of the landmark is calculated by the sensor fusion of the front camera 11 and the millimeter wave radar 12, the weight of the image recognition result may be decreased (for example, set to zero). For a predetermined time period after the successful sensor fusion, as long as the millimeter wave radar 12 can track the landmark, the position of the landmark is calculated and updated based on the radar detection result. In adverse environment, such as rainy weather condition, the recognition ability of front camera 11 is deteriorated. According to above configuration, the occurrence frequency of an event in which the position of landmark cannot be specified can be suppressed. As a result, it is possible to suppress deterioration in localization process accuracy.” Based on the detected adverse environment the sensor weighting is changed for localization (adjustment of sensor parameters))
Regarding Claim 11, Terazawa teaches “1, further comprising determining environmental conditions of the surroundings, wherein the at least one baseline detection parameter is selected or adjusted based on the environmental conditions, the environmental conditions including at least one of weather conditions, lighting conditions, or road condition”( [0077] The effective recognition distance of the landmark may be reduced by other factors, such as weather, occlusion by a preceding vehicle. Thus, when a preceding vehicle is present within the predetermined distance, the calculation of the effective recognition distance may be omitted. Alternatively, when a preceding vehicle is present, the effective recognition distance may be provided to the adverse environment determination unit F6 by adding data indicating the presence of the preceding vehicle (for example, a preceding vehicle flag). When the road ahead the subject vehicle is not a straight road, that is, when the road ahead is a curved road, the effective recognition distance may also be decreased. Therefore, when the road ahead is the curved road, calculation of the effective recognition distance may be omitted. Further, when the road ahead is the curved road, the effective recognition distance may be provided to the adverse environment determination unit F6 in association with data (for example, a curve flag) indicating that the road ahead is curved. A road having a curvature equal to or greater than a threshold value may be determined as the curved road.” Here teaches that based on road conditions (road shape) is used to adjusted the expected effective recognition distance (for determining if there is an adverse environment) for adverse environment detection )
Regarding Claim 12, Terazawa teaches “. The method of claim 1, wherein the at least one sensor comprises multiple sensor types including at least two of: a camera, a lidar sensor, a radar sensor, or an ultrasonic sensor, and wherein the at least one detection parameter is determined based on sensor fusion of data from the multiple sensor types.”( [0160] According to the above configuration, the localization unit F0 executes the localization process without using the detection result of the millimeter wave radar 12 when the environment is determined to be not adverse (that is, normal environment). Thus, processing load in the processing unit 21 can be reduced. Under adverse environment, localization process is performed using the observation coordinates of landmarks calculated by the sensor fusion, that is, combination of the detection results of millimeter wave radar and camera. Thus, it is possible to suppress the deterioration in position estimation accuracy caused by rainfall or the like.” Here teaches detection (of landmark/distance) includes fusion of both a camera and radar)
Regarding Claim 16, Terazawa teaches “The method of claim 1, wherein analyzing the at least one detection parameter comprises weighting the analysis based on the reliability of the sensor data used for detection.”([0161]-[0162] in these paragraphs the weighting of sensor data (for determining the effective recognition distance) is changed based on the detected environment with lower reliability sensor data being weight less compared to higher reliability sensor data)
Regarding Claim 17, Terazawa does not teach storing of historical parameters, identifying patterns in the detection performance overtime and triggering a calibration in response to that over time pattern.
Kaiser teaches a similar autonomous vehicle localization and sensor performance determination and calibration system which includes storing of historical detection parameters, identifying of a pattern in these detections overtime and then triggering of a calibration (of the sensor) in response to the detected pattern (Kaiser [0060] “The distances 252 and 256 can be determined and tracked over time. For example, distances can be determined for sensor data captured over a period of time such as seconds, hours, days, weeks, etc. The distance values can be averaged or otherwise statistically accumulated to determine an error over time.” Here the historical distances (detection parameters) are stored, and then averaged or otherwise statistically accumulated (determine a pattern overtime) to detect an error over time + Kaiser [0061]-[0062] in response to the error overtime reaching a threshold then a calibration response is initiated. )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application to further modify Terazawa to include a error over-time average detection as taught by Kaiser in addition to in the instantaneous sensor performance (effective sensing distance) as taught in Terazawa. One would be motivated to implement the error over time calculations to improve the ability of the system to detect if/when sensors are out of calibration by allowing for an additional way of calculating error. (Kaiser [0061], [0094])
Claims 19-20 are system and non-transitory computer readable medium equivalents to the method claim 1, it has the same grounds of rejection.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Terazawa in view of Kaiser as applied to claim 1 above, and further in view of US 20240092391 A1, “METHOD FOR IMPROVING SAFETY PRECAUTIONS FOR VEHICLES MOVING IN AN AT LEAST PARTIALLY AUTOMATED MANNER”, Ghanbari Milani et al
Regarding Claim 3, Terazawa while teaching a calculation/determination of an adverse environment; it does not specifically teach recognizing/utilizing recognizing such based on the time duration between an initial detection of an object and the determination of the object’s classification.
Ghanbari Milani et al teaches a vehicle perception system which includes determining of a safety metric which indicate how the vehicle is currently performing compared to a given threshold/value for a certain scenario/environment ([0006] in which the safety metric includes detecting of a object classification delays ([0039] and table 1; from classification delay (the time between detecting an object and classifying it (i.e. point of time where confidence in a classification exceeds a threshold level) it exceeded (is delayed) is compared to an expected time; from the term “delay” it is implicit that there is an expected (i.e. baseline) speed/time which a classification should occur at/within).)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to modify Terazawa to include the recognition of object classification delays as another input for determining an adverse environment. One would be motivated to implement such in order to improve the safety of operation of the vehicle by increasing the ways it can recognize adverse environments/dangerous operating conditions and/or to identify if there is a fault in the vehicle’s systems. This motivation is taught in Terazawa (0005] In particular, the present invention relates to improving (or increasing) safety precautions for vehicles moving in an at least partially automated manner, viz, based on trigger events and safety metrics…. [0006] A safety metric can define a first metric that, in a vehicle moving in an at least partially automated manner, during operation of the vehicle, determines values for one or more vehicle and/or environmental parameters (or measures them; this can be the same and/or different or further vehicle and/or environmental parameters than for the scenario) and compares them to one or more threshold values (e.g., upper and/or lower threshold value). The safety metric is deemed to be fulfilled if at least one of the values of the first metric exceeds or falls below the one or at least one of the plurality of threshold values. In this context, the safety metrics are also referred to as so-called continuous safety metrics
+
[0025] However, it may now be the case that scenario information and safety metric information will still be transmitted. According to an example embodiment of the present invention, in this respect, it is expedient to check, e.g., at the evaluation site, whether a trigger event has already been defined for the scenario present at that time point. If this is the case, and the information should therefore not have actually been obtained at all, the information, i.e., e.g., the scenario information and safety metric information, obtained from the respective vehicle can be provided for checking the vehicle. It can then be checked, for example, whether there is a fault in the vehicle.
[0026] It can also be provided that the faulty vehicle is automatically brought into a safe state or that a safe state is established (i.e., for example, the vehicle can be stopped in an automated manner). Moreover, a user or driver can be informed of the fault or that the vehicle is faulty. Further sensor and system self-tests may likewise be initiated, for example.)
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Terazawa in view of Kaiser as applied to claim 1 above, and further in view of ADAPTIVE POSITIONING SYSTEM, US 20170023659 A1, Bruemmer et al
Regarding Claim 4, Terazawa while teaching a determination of adverse environments based in part on sensor information it does not specifically teach that the detected parameters (For adverse environment detection) includes the determining the accuracy of an objects position relative to the vehicles based on multiple sensors overtime.
Bautista teaches an autonomous vehicle control system which includes determining localization accuracy of objects based on sensor data from multiple sensors over time in order to determine sensor faults/degredation (An Adaptive Positioning System provides a method for directing and tracking position, motion and orientation of mobile vehicles, people and other entities using multiple complementary positioning components to provide seamless positioning and behavior across a spectrum of indoor and outdoor environments. The Adaptive Positioning System (APS) provides for complementary use of peer to peer ranging together with map matching to alleviate the need for active tags throughout an environment. Moreover, the APS evaluates the validity and improves the effective accuracy of each sensor by comparing each sensor to a collaborative model of the positional environment. The APS is applicable for use with multiple sensors on a single entity (i.e. a single robot) or across multiple entities (i.e. multiple robots) and even types of entities (i.e. robots, humans, cell phones, cars, trucks, drones, etc.). + [0093] “0IGS. 4A and 4B provides a simple rendition of the multimodal estimator's ability to continually assess and resolve sensor failure. FIG. 4 shows historical path of an object 410. The object's position is identified in 4 discrete positions by a plurality of sensors. Initially the position of the object 410 is estimated by the unimodal sensors to be within the depicted circle 415. Along the path exists landmarks or other features that enable one or more sensors to determine the object's position.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to modify Terazawa to include the sensor object location accuracy assessment as taught by Bautista in order to allow for individualized sensor failure detection and subsequent adjusting/correcting of object position determination by filtering out the failed sensor data from the position estimation thereby improving localization accuracy. Buatista teaches this improvement in ([0117] Responsive to a determination of the failure of a particular sensor 1060 the Adaptive Positioning System of the present invention filters out that positional determination and eliminates its contribution to the overall assessment as to the object's estimated position. With the failed sensor removed from consideration the process begins anew and gain makes a determination as to whether this or other positional sensors have failed.)
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Terazawa et al in view of Kaiser as applied to claim 1 above, and further in view of US 20180211176 A1, “Blended IoT Device Health Index”, Khurshudov et al.
Regarding Claim 13, Terazwa does not suggest updating a baseline detection parameter by replacing older baselines with new baselines to maintain relevance to current operating conditions.
Khurshudov et al teaches a sensor health monitoring system in which an expected sensor parameter baseline is established and then updated (i.e. older baseline is replaced) with a new baseline in order to improve the ability to detect sensor degredation ([0007] The present invention is directed to solving disadvantages of the prior art. In accordance with embodiments of the present invention, a method is provided for a device not having an available history of either failures or degraded performance. The method includes establishing, by a computer coupled to the device, an initial baseline of sensor data from the device, receiving new sensor data after establishing the initial baseline, creating an updated baseline based on the new sensor data, evaluating, by the computer, the new sensor data compared to the updated baseline based on a plurality of different time scales, and determining whether the device is indicating an increased probability of failure or degraded performance based on the evaluated sensor data.)
It would have been obvious to one of ordinary skill in the art to substitute the static (designed) baseline for performance with the updating baseline for sensing as taught by Khurshudov et al in order to allow for a system which can determine current performance trends and predict future failure of the sensors. Khurshudov et al teaches this in ([0007]… evaluating, by the computer, the new sensor data compared to the updated baseline based on a plurality of different time scales, and determining whether the device is indicating an increased probability of failure or degraded performance based on the evaluated sensor data.”)
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over modified Terazawa in view of Kaiser as applied to claim 1 above, and further in view of JP2016070796A, MORIYA SHOZO, “OBSTACLE DETERMINATION DEVICE AND OBSTACLE DETERMINATION METHOD”.
Regarding Claim 21, while modified Terazawa teaches cleaning the sensors (from the combination with Kaiser in claim 1); it does not specifically teach determining that the sensor aperture is fouled in particular.
Shozo teaches a sensor performance analysis system which includes determining if the sensor aperture in particular is fouled and thus should have the dirt removed form it. (Shozo [0009]-[0011] here teaches detecting of sensor fouling in particular as opposed to +[0006] here teaches that dirt should be removed in response to aperture fouling)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application to further modify Terazawa to include the sensor fouling detecting and analysis as taught by Shozo, one would be motivated to implement this analysis of distance as taught by Shozo to allow for the system to better identify the root cause of degradation and thereby improve operational response while also not increasing the amount of component of the sensor housing (Shozo [0007]-[0008])
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1-10, 12, and 19-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5, 7, 11-12, 14, 17, 19-20 of U.S. Patent No. 12528482. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent claims represent a more specific implementation (i.e. species) equivalent to the generalized (genus) claims of the application. The claiming of a species renders obvious the claiming of its equivalent genus.
More specifically:
Regarding Application claim 1, 482 claims “A method comprising: receiving, at a computing system, sensor data from at least one sensor associated with a vehicle, the sensor data representing surroundings of the vehicle; detecting, based on the sensor data, one or more objects in the surroundings; determining at least one detection parameter associated with at least one object;”(Claim 1 “A method comprising: receiving, at a computing system and from a sensor coupled to a vehicle, sensor data representing an environment of the vehicle as the vehicle navigates a path; detecting, based on the sensor data, an object in the environment; responsive to detecting the object, determining a detection distance between the object and the sensor” determining a detection distance is a specific form of at least one detection parameter associated with the object );” analyzing the at least one detection parameter relative to at least one baseline detection parameter, wherein the at least one baseline detection parameter is associated with at least one characteristic of the at least one object;”( claim 1” performing, by the computing system, a comparison between the detection distance and a baseline detection distance, wherein the baseline detection distance depends on one or more prior detections of given objects that are in a classification group comprising the object;” here comparing detection distance to baseline detection distance is a species equivalent to comparing a detection parameter to a baseline parameter);”and modifying, based on the analysis, a configuration of the at least one sensor, wherein modifying the configuration of the at least one sensor comprises triggering a cleaning process for the at least one sensor..”(Claim 1 “and adjusting, based on the comparison, a control strategy for the vehicle by performing a cleaning operation at the sensor or adjusting a speed of the vehicle.” Here is a more specific implementation of a control strategy/configuration of at least one sensor)
Regarding claim 2, 482 claims “The method of claim 1, wherein the at least one detection parameter comprises a detection distance between the at least one object and the at least one sensor, and wherein the at least one baseline detection parameter comprises a baseline detection distance based on prior detections of objects in a same classification group as the at least one object.”( claim 1” performing, by the computing system, a comparison between the detection distance and a baseline detection distance, wherein the baseline detection distance depends on one or more prior detections of given objects that are in a classification group comprising the object;”)
Regarding claim 3, 482 claims “The method of claim 1, wherein determining the at least one detection parameter comprises determining a detection time representing a duration between an initial detection of the at least one object and a subsequent detection when a classification confidence exceeds a threshold confidence level.”(Claim 11, ‘responsive to detecting the object, determining a detection time associated with detecting the object, wherein the detection time represents a duration between an initial detection of the object and a subsequent detection of the object when a classification confidence for the object exceeds a threshold confidence;…”)
Regarding Claim 4, 482 claims “The method of claim 1, wherein the at least one detection parameter comprises a localization accuracy representing how accurately a position of the at least one object is determined relative to the vehicle, and wherein the localization accuracy is determined using sensor data from multiple sensors over a duration of time.”(Claim 12, “further comprising: responsive to detecting the object, determining a localization accuracy associated with detecting the object, wherein the localization accuracy depends on at least one of: (i) subsequent sensor data from the sensor or (ii) sensor data from a second sensor coupled to the vehicle; and performing a second comparison between the localization accuracy associated with detecting the object and a baseline localization accuracy, wherein the baseline localization accuracy depends on one or more prior localization accuracies associated with detecting given objects in the classification group comprising the object.”)
Regarding claim 5, 482 claims “The method of claim 1, wherein the at least one detection parameter comprises a classification confidence representing a confidence level assigned to a classification of the at least one object, and wherein the at least one baseline detection parameter comprises a baseline classification confidence based on prior classifications of objects in a same classification group.”(Claim 5, “The method of claim 1, further comprising: based on detecting the object, determining a detection confidence for the object exceeds a threshold confidence; and based on determining the detection confidence for the object exceeds the threshold confidence, determining the detection distance between the object and the sensor.”)
Regarding Claim 6, 482 claims “The method of claim 1, wherein analyzing the at least one detection parameter comprises: determining a difference between the at least one detection parameter and the at least one baseline detection parameter, and determining whether the difference exceeds a threshold difference value.”(Claims 13 “determining, based on the comparison, a difference between the detection distance and the baseline detection distance; and based on the difference exceeding a threshold difference, causing the vehicle to decrease speed.”+14 “determining, based on the comparison, a difference between the detection distance and the baseline detection distance; and based on the difference exceeding a threshold difference, triggering at least a cleaning process or a calibration process for the sensor.”)
Regarding Claim 7, 482 claims “The method of claim 1, wherein the at least one baseline detection parameter is obtained from a reference table storing baseline parameters for multiple classification groups, each classification group corresponding to different types of objects encountered during vehicle navigation.”( The method of claim 6, wherein identifying the baseline detection distance comprises: obtaining the baseline detection distance from a reference table, wherein the reference table comprises at least a first baseline detection distance corresponding to a first classification group and a second baseline detection distance corresponding to a second classification group, and wherein objects in the first classification group differ from objects in the second classification group.)
Regarding Claim 8, 482 claims “The method of claim 1, wherein the at least one baseline detection parameter is generated using a machine learning model trained on sensor data from the vehicle or other vehicles, the machine learning model configured to output baseline parameters based on object classification groups and environmental conditions.”(Claim 18 “The method of claim 1, further comprising: receiving sensor data from a plurality of sensors coupled to the vehicle, wherein the plurality of sensors includes the sensor; generating a machine learning model based on the sensor data, wherein the machine learning model uses the sensor data to group objects according to classification groups and generates a baseline detection distance corresponding to each classification group; and wherein identifying the baseline detection distance comprises: obtaining the baseline detection distance based on the machine learning model.”)
Regarding Claim 9, 482 claims “wherein modifying the control strategy comprises at least one of: adjusting a speed of the vehicle, modifying a following distance to other objects, or changing a navigation path based on the analysis”(Claim 1 “…and adjusting, based on the comparison, a control strategy for the vehicle by performing a cleaning operation at the sensor or adjusting a speed of the vehicle.”)
Regarding Claim 10, 482 claims “wherein modifying the configuration of the at least one sensor comprises triggering at least one of: a cleaning process for the at least one sensor, a calibration process for the at least one sensor, or an adjustment of sensor parameters” (Claim 1 “…and adjusting, based on the comparison, a control strategy for the vehicle by performing a cleaning operation at the sensor or adjusting a speed of the vehicle.”)
Regarding Claim 12, 482 claims “The method of claim 1, wherein the at least one sensor comprises multiple sensor types including at least two of: a camera, a lidar sensor, a radar sensor, or an ultrasonic sensor, and wherein the at least one detection parameter is determined based on sensor fusion of data from the multiple sensor types.”(Claim 1The method of claim 1, further comprising: receiving sensor data from a plurality of sensors coupled to the vehicle, wherein the plurality of sensors includes the sensor; detecting a plurality of objects based on the sensor data from the plurality of sensors; determining, for each detected object, a given detection distance between the detected object and the vehicle; and generating a plurality of baseline detection distances based on respective detection distances associated with detecting the plurality of objects, wherein the plurality of baseline detection distances comprises at least a first baseline detection distance corresponding to a first classification group and a second baseline detection distance corresponding to a second classification group, and wherein objects in the first classification group differs from objects in the second classification group.” Here claims using a plurality of sensors based on their data to detect objects/their associated detection distances (i.e. the fusion of multiple sensors) while this claim does not recite the specific type of sensors as recited in the applications claims, official notice is taken that cameras, lidar, radar, and ultrasonic are all WURC sensor in the automotive field as such one of ordinary skill in the art would understand the “sensors” of 482’s claim to include these recited types of sensors)
Regarding Claim 19-20, 482’s claims 19-20 recite system and non-transitory computer readable medium equivalents to the method claim 1, they have the same overall double patenting analysis as claim 1’s double patenting above.
Allowable Subject Matter
Claim 18 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.
The following is a statement of reasons for the indication of allowable subject matter: No prior art was found to teach or render obvious the baseline parameter being obtained from a plurality of vehicles within a threshold distance of the vehicle and this baseline parameter is shared amongst the plurality as recited in amended claim 18. It is also noted that claim 18 is not rejected under double patenting grounds, as such should it be integrated into the independent claims the above double patenting rejections stemming from U.S. Patent No. 12528482 would no longer apply.
Conclusion
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.
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/KENNETH M DUNNE/ Primary Examiner, Art Unit 3669
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