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 .
The application filed on May 15, 2024.
Response to Arguments
Applicant's arguments filed 05/19/2026 have been fully considered and are moot in view new grounds of rejection. Applicant's arguments with respect to the claims have been considered. The arguments do not apply to any of the references being used in the current rejection.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kusano (20190077398) in view of Goyal (20250296604).
With regard to claim 1, Kusano discloses a method comprising:
obtaining sensor data obtained using one or more sensors of a machine, the sensor data representative of at least a first lane and a second lane of a multi-lane road within an environment for which the machine may be located (a monitoring module 220 monitoring the sensor data for indicators of a presence of the nearby vehicles of a road with multiple lanes, see Fig. 3 & [0055]-[0056] ),
The structural RNN 250, recurrent units, such as models are used to represent each of the factors as shown in Fig.5 for computing the vehicle interaction factor and vehicle dynamic factor for a given lane (left, right or same), see [0047]- [0052] & [0062]-[0064]. Wherein the vehicle 100 includes an autonomous driving modules 160 which determines its location, velocity, locations of obstacles, and other environmental features such as traffic signs, trees, and etc., see [0093]-[0094] which meet the scope of “computing, using one or more machine learning models and based at least on the sensor data, the vehicle’s feature, location, velocity, and etc. representing, from a left side of the multi- lane road, at least a first probability that the machine is located within the first lane and a second probability that the machine is located within the second lane ; computing, using the one or more machine learning models and based at least on the sensor data, the vehicle’s feature, location, velocity, and etc. representing, from a right side of the driving surface multi-lane road different from the left side of the multi-lane road, a third probability that the machine is located within the second lane and a fourth probability that the machine is located within the first lane;
localizing, based at least on the first vector and the second vector, the machine to one of the first lane or the second lane (localizes the vehicle 100 on the roadway to determine a present lane of travel, see [0058], see [0058]-[0061]+); and
controlling the machine to navigate within the environment based at least on the machine being localized to the one of the first lane or the second lane (control the vehicle to execute of maneuvers through efficient planning, see [0051]+).
Kusono is silent about using the machine learning models to compute 1st and 2nd vectors which representing the vehicle located on the left and/or right side of the multiple lanes’ road based on the sensors data.
Goyal discloses a vehicle’s computing system which uses the machine learning models to determine, predict the vehicle and other vehicles, and generate lane driving situations, see [0028]+. The lane driving situations are generated with the vehicle’s trajectories (as same as vectors) which representing the vehicle probability proceed straight, turn left or turn right, see [0068]+.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Kusono by including using the machine learning models to compute 1st and 2nd vectors which representing the vehicle located on the left and/or right side of the multiple lanes’ road based on the sensors data as taught by Goyal for improving the autonomous vehicle traveling performance.
With regard to claims 2-3, Goyal teaches that the method of claim 1, wherein the 1st vector includes a element (probabilities) associated with the first lane followed by another element (probabilities) associated with the second lane; and the second vector includes at least the element associated with the second lane followed the other element associated with the first lane (method at block 610, likelihood estimation is performed, the system will assign likelihoods/probabilities to the prediction which indicates the probability of lane driving happening, see [0081]+ & [0083]+);
With regard to claims 4-5, Goyal teaches that the process 100 that is a method of operating vehicle in which the vehicle continuously receives sensor data, using other trained machine learning model to generate a continuing lane driving behavior prediction for the road, see [0101]-[0105]+, and assign likelihood/probabilities to the predictions, see [0081]+ which meets the scope of the claim.
With regard to claim 6, Goyal teaches that the method of claim 1, further comprising:
determining, based at least on the one or more machine learning models processing the sensor data, a first output indicating at least one of one or more lane boundaries or one or more road boundaries and a second output indicating one or more locations of at least one of the first lane or the second lane (define the boundary of the lane, see [0049]+),
wherein the computing the first vector and the computing the second vector are based at least on the first output and the second output (predicted trajectories are generated, see [0072]+).
With regard to claim 7, Kusano discloses a system comprising: one or more processors to:
determine, based at least on sensor data obtained using one or more sensors of a machine, a first output indicating, from a first side of a driving surface, one or more first probabilities that the machine is located within lanes and a second output indicating, from a second side of the driving surface, second probabilities that the machine is located within the lanes (a monitoring module 220 monitoring the sensor data for indicators of a presence of the nearby vehicles of a road with multiple lanes, see Fig. 3 & [0055]-[0056]. Using RNN, providing the three-lane structure of the target neighborhood, the factor graph 400 (Fig.4) representing the probability of the future lane change label, see [0042]+),
control, based at least on the first output and the second output, the machine to navigate along the driving surface (the prediction module 230 provides/communicates the electronic outputs to cause the vehicle 100 operation, see [0065]+).
Although Kusano’s disclosure is not described as same world languages but Examiner interprets the vehicle’s system for vehicle lane change prediction using structural recurrent neural networks pointed out above are equivalent as the scope of the claim. For this reason, Kusano is obvious suggestively, if not anticipatory, of the claimed subject matter.
With regard to claim 8, Kusano teaches that the system of claim 7, wherein the one or more processors are further to:
determine, based at least on the first output and the second output, that the machine is located within a lane of the lanes (the lane prediction system 170 using HD map along with GPS measurement to localize the vehicle on the lane, see [0030]+),
wherein the machine is caused to navigate along the lane of the driving surface based at least on the machine being located within the lane (the processor 110 can operatively connected to communicate with the various vehicle system 140, actuators 150 to control movement, speed, maneuvering, see [0086]-[0087]+).
With regard to claims 9-10, Kusano teaches that the system of claim 7, wherein:
the first probabilities indicated by the first output are indexed starting at a first lane of the lanes that is located proximate to the first side of the driving surface and ending at a second lane of the lanes that is located proximate to the second side of the driving surface; and the second probabilities indicated by the second output are indexed starting at the second lane and ending at the first lane (the factor graph 400 representing the probability of the future lane change label using edges that represent the interaction between vehicles in each lane (Left, right, no change), see [0042]+)
With regard to claims 11-12, Goyal teaches that the process 100 that is a method of operating vehicle in which the vehicle continuously receives sensor data, using other trained machine learning model to generate a continuing lane driving behavior prediction for the road, see [0101]-[0105]+, and assign likelihood/probabilities to the predictions, see [0081]+ which meets the scope of the claim.
With regard to claim 13-14, Kusano teaches that the system of claim 7, wherein the one or more processors are further to:
determine that the machine has switched lanes with probability; and determine, based at least on the machine switching lanes, a third output by updating the first probabilities to include third probabilities and a fourth output by updating the one or more second probabilities to include one or more fourth probabilities (see [0047]-[0050]+).
With regard to claim 15, Goyal teaches that the system of claim 7, wherein the one or more processors are further to:
determine, based at least on the one or more machine learning models processing the sensor data, a third output indicating at least one of one or more lane boundaries or one or more road boundaries and a fourth output indicating one or more locations of the one or more lanes, wherein the first output and the second output are determined based at least on the third output and the fourth output (see, [0049]-[0050]+).
With regard to claim 16, Goyal teaches that the system of claim 7, wherein the one or more processors are further to: determine, based at least on a map associated with an environment that includes the driving surface, a type of road associated with the driving surface, wherein the first output and the second output are determined further based at least on the type of road (see [0052]+).
With regard to claim 17, Kusano teaches that the system of claim 7, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine (see [0007]+);
With regard to claim 18, Kusano discloses one or more processors comprising: processing circuitry to:
determine, based at least on sensor data obtained using one or more sensor of a machine (a monitoring module 220 monitoring the sensor data for indicators of a presence of the nearby vehicles of a road with multiple lanes, see Fig. 3 & [0055]-[0056] ), at least:
a first output indicating one or more first probability that the machine is located within one or more lanes of a driving surface, the first output being indexed starting at a first side of the driving surface and ending at a second side of the driving surface (left lane edge RNN 510, see [0047]+); and
a second output indicating one or more second probabilities that the machine is located within the one or more lanes of the driving surface, the second output being indexed starting at the second side of the driving surface and ending at the first side of the driving surface (right lane Edge RNN 530, see [0047]+); and
control the machine to navigate based at least on localizing the machine using the first output and the second output (localizes the vehicle 100 on the roadway to determine a present lane of travel, see [0058] & control the vehicle to improve safety and navigation through movements of the nearby vehicles, see [0065]+).
Although Kusano’s disclosure is not described as same world languages but Examiner interprets the vehicle’s system for vehicle lane change prediction using structural recurrent neural networks pointed out above are equivalent as the scope of the claim. For this reason, Kusano is obvious suggestively, if not anticipatory, of the claimed subject matter.
With regard to claim 19, Goyal teaches that the one or more processors of claim 18, wherein:
the first output includes a first probability vector that is indexed starting from the first side of the driving surface and ending at the second side of the driving surface; and
the second output includes a second probability vector that is indexed starting from the second side of the driving surface and ending at the second side of the driving surface (using other trained machine learning model to generate a continuing lane driving behavior prediction for the road, see [0101]-[0105]+, and assign likelihood/probabilities to the predictions, see [0081]+)
With regard to claim 20, Kusano teaches that the one or more processors of claim 18, wherein the one or more processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine (see [0047]+)
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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/NGA X NGUYEN/Primary Examiner, Art Unit 3662
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