DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This communication is a first office action, non-final rejection on the merits. Claims 1-21, as originally filed, are currently pending and have been considered below.
Claim Rejections - 35 USC § 103
3. 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4. Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Gogna et al. ( USP 2020/0409358) in view of Wright et al. ( USP 2022/0135085).
As Per Claim 1, Gogna et al. ( Gogna) teaches, a non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, [0054], [0069], [0080-0081], Fig.4), are configurable to cause the processors to: operate an autonomous vehicle (via vehicle 102 (1), 102 (2), Vehicle 402,[0053], Figs.1A, 1B,4) in a road setting in an operating environment having other road users (via yielding to other vehicle ,[0013]) Abstract), wherein the operation of the autonomous vehicle is based, at least in part, on one or more safety constraints providing limits on operation of the autonomous vehicle; (Gogna : ([0013]), [0016-0020],[0023],[0027]), detect the presence of a selected other road user from the other road users within the operating environment; (Gogna :[0029], [0030], [0033], [0034], Figs. 1A), evaluate one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user ([0037-0042]).
However, Gogna does not explicitly teach, evaluate one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user using at least a cost-based analysis having a machine learned model, wherein the evaluation is based on a hierarchy of costs corresponding to characteristics of maneuvers by the autonomous vehicle; and cause the autonomous vehicle to interact with the selected other road user by generating vehicle control signals based on cost-based analysis and within the one or more safety constraints.
In a related field of Art, Wright et al. ( Wright) teaches, autonomous vehicle controller (computing device) being equipped with routing system, evaluate one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user using at least a cost-based analysis ([0048]),
having a machine learned model ([0095]), wherein the evaluation is based on a hierarchy of costs corresponding to characteristics of maneuvers by the autonomous vehicle; and cause the autonomous vehicle to interact with the selected other road user by generating vehicle control signals based on cost-based analysis and within the one or more safety constraints. ( [0037], [0044], [0048], [0095], Figs., 2, 3A).
It would have been obvious to one of ordinary skill in the art, having the teachings of Gogna and Wright before him before the effective filing date of the claimed invention to modify the systems of Gogna to include the teachings (controller)
of Wright and configure with the system of Gogna in order to generate route using a cost-based analysis attempting to select a route to the destination with the lowest cost and developing machine learning model for pedestrian objects, and being used in real-time by self driving vehicle. Motivation to combine the two teachings is, developing machine learning model for vehicle and using cost-based analysis for route computation (i.e., cost saving).
As per Claim 2, Gogna as modified by Wright teaches the limitation of Claim 1. However, Gogna in view of Wright teaches, further comprising instructions that, when executed by the one or more processors, are configurable to cause the processors to translate yield probabilities between the autonomous vehicle and the other selected road user to costs to be utilized as part of the evaluation of the one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user. (Gogna :Abstract, [0013], [0017-0023], [0033], [0059],[0093], Fig.1A, 4, 6).
As per Claim 3, Gogna as modified by Wright teaches the limitation of Claim 1. However, Gogna in view of Wright teaches, wherein the other road users comprise one or more of: a human-operated vehicle, another autonomous vehicle, a pedestrian, a bicycle, and a streetcar. (Gogna: Abstract, [0013], [0017-0023], [0033], [0059],[0093], Fig.1A, 4, 6). Also See Wright : ( Wright : via vehicle may detect the presence of different objects on or adjacent to the roadway, including a stop sign 406 and a pedestrian crossing sign 408, as well as different people 410.sub.1, 410.sub.2 and 410.sub.3.”, [0068], Fig.4A) and ( Wright : via “nearby objects (e.g., cars or other vehicles on the roadway, pedestrians or bicyclists on sidewalks, etc.”, [0095]).
As per Claim 4, Gogna as modified by Wright teaches the limitation of Claim 1. However, Gogna in view of Wright further teaches, wherein the interaction between the autonomous vehicle and the selected other road user comprises a yield/assert decision (Gogna : Abstract, [0013], [0017-0023], [0033], [0059],[0093], Fig.1A, 4, 6).
As per Claim 5, Gogna as modified by Wright teaches the limitation of Claim 4. However, Gogna in view of Wright teaches, wherein a yield probability associated with the yield/assert decision is based on a non-linear relationship between an assert decision by the autonomous vehicle and one or more characteristics of the selected other road user (Gogna :Abstract, [0013], [0017-0023], [0033], [0059], [0093], Fig.1A, 4, 6).
As per Claim 6, Gogna as modified by Wright teaches the limitation of Claim 1. However, Gogna in view of Wright teaches, wherein the evaluation of the one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user is performed (Abstract, [0013], [0017-0023], [0033], [0059], [0093], [0094],Fig.1A, 4, 6).
However, Gogna in view of Wright does not explicitly teach, potential trajectories of the autonomous vehicle with respect to the selected other road user is performed utilizing a cost-based analysis considering at least safety factors and comfort factors.
However, Gogna teaches, vehicle is getting instruction from teleoperator not to yield and continue navigation, ([0093-0094]).
Therefore, it would have been obvious to one ordinary skill in the art to recognize that, not yielding to obstruction and navigate around the obstruction is time saving ( cost based analysis) and not being stuck into congestion and getting out from congestion provides comfort to the rider. Therefore, Gogna has such teachings.
As per Claim 7, Gogna as modified by Wright teaches the limitation of Claim 6. However, Gogna in view of Wright does not explicitly teach, wherein the comfort factors comprise at least induced kinematic discomfort and post-encroachment time.
However, it is obvious to one ordinary skill in the art to recognize that , when the autonomous vehicle is navigating on the streets, it is subject to encounter other obstructions, cyclist, pedestrians, user, other vehicles, in the environment, induced kinematic discomfort and post-encroachment is obviously present there.
However, incorporating in vehicle design induced kinematic discomfort and post-encroachment time into comfort factors, would be an obvious matter of design choice, In re Kuhle, 526 F.2d 553, 188 USPQ 7 (CCPA 1975).
As Per Claim 8, Gogna et al. (Gogna ) teaches, an autonomous vehicle (via vehicle 102 (1), 102 (2), Vehicle 402,[0053], Figs.1A, 1B,4) comprising: sensor systems to detect characteristics of an operating environment (via sensor systems 406, Fig.4); kinematic control systems to provide kinematic controls to the autonomous vehicle (via computing device 404, drive module 414, [0068], [0078]); a vehicle control system coupled with the sensor systems and with the kinematic control systems, ( via vehicle computing device 404 being coupled with sensors systems 406, Fig.4); the vehicle control system configured to: operate the autonomous vehicle in a road setting in an operating environment having other road users (via yielding to other vehicle , [0013],Abstract), wherein the operation of the autonomous vehicle is based, at least in part, on one or more safety constraints providing limits on operation of the autonomous vehicle, (Gogna : ([0013]), [0016-0020],[0023],[0027]), detect the presence of a selected other road user from the other road users within the operating environment, (Gogna :[0029], [0030], [0033], [0034], Figs. 1A), evaluate one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user ([0037-0042]).
However, Gogna does not explicitly teach, evaluate one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user using at least a cost- based analysis having a machine learned model, wherein the evaluation is based on a hierarchy of costs corresponding to characteristics of maneuvers by the autonomous vehicle, and cause the autonomous vehicle to interact with the selected other road user by generating vehicle control signals based on cost-based analysis and within the one or more safety constraints.
In a related field of Art, Wright et al. ( Wright) teaches, autonomous vehicle controller (computing device) being equipped with routing system, wherein, evaluate one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user using at least a cost- based analysis ([0048]) having a machine learned model ([0095]), wherein the evaluation is based on a hierarchy of costs corresponding to characteristics of maneuvers by the autonomous vehicle, and cause the autonomous vehicle to interact with the selected other road user by generating vehicle control signals based on cost-based analysis and within the one or more safety constraints ( [0037], [0044], [0048], [0095], Figs., 2, 3A).
It would have been obvious to one of ordinary skill in the art, having the teachings of Gogna and Wright before him before the effective filing date of the claimed invention to modify the systems of Gogna to include the teachings (controller) of Wright and configure with the system of Gogna in order to generate route using a cost-based analysis attempting to select a route to the destination with the lowest cost and developing machine learning model for pedestrian objects, and being used in real-time by self driving vehicle. Motivation to combine the two teachings is, developing machine learning model for vehicle and using cost-based analysis for route computation (i.e., cost saving).
As per Claim 9, Gogna as modified by Wright teaches the limitation of Claim 8. However, Gogna in view of Wright teaches, wherein the vehicle control system is further configured to translate yield probabilities between the autonomous vehicle and the other selected road user to costs to be utilized as part of the evaluation of the one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user. (Gogna :Abstract, [0013], [0017-0023], [0033], [0059],[0093], Fig.1A, 4, 6).
As per Claim 10, Gogna as modified by Wright teaches the limitation of Claim 8. However, Gogna in view of Wright teaches, wherein the other road users comprise one or more of: a human-operated vehicle, another autonomous vehicle, a pedestrian, a bicycle, and a streetcar. ( Gogna: Abstract, [0013], [0017-0023], [0033], [0059],[0093], Fig.1A, 4, 6). Also See Wright : ( Wright : via vehicle may detect the presence of different objects on or adjacent to the roadway, including a stop sign 406 and a pedestrian crossing sign 408, as well as different people 410.sub.1, 410.sub.2 and 410.sub.3.”, [0068], Fig.4A) and ( Wright : via “nearby objects (e.g., cars or other vehicles on the roadway, pedestrians or bicyclists on sidewalks, etc.”, [0095]).
As per Claim 11, Gogna as modified by Wright teaches the limitation of Claim 8. However, Gogna in view of Wright further teaches, wherein the interaction between the autonomous vehicle and the selected other road user comprises a yield/assert decision. (Gogna : Abstract, [0013], [0017-0023], [0033], [0059],[0093], Fig.1A, 4, 6).
As per Claim 12, Gogna as modified by Wright teaches the limitation of Claim 11. However, Gogna in view of Wright teaches, wherein a yield probability associated with the yield/assert decision is based on a non-linear relationship between an assert decision by the autonomous vehicle and one or more characteristics of the selected other road user. (Gogna :Abstract, [0013], [0017-0023], [0033], [0059], [0093], Fig.1A, 4, 6).
As per Claim 13, Gogna as modified by Wright teaches the limitation of Claim 8. However, Gogna in view of Wright teaches, wherein the evaluation of the one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user is performed (Abstract, [0013], [0017-0023], [0033], [0059], [0093], [0094],Fig.1A, 4, 6).
However, Gogna in view of Wright does not explicitly teach, potential trajectories of the autonomous vehicle with respect to the selected other road user is performed utilizing a cost-based analysis considering at least safety factors and comfort factors.
However, Gogna teaches, vehicle is getting instruction from teleoperator not to yield and continue navigation, ([0093-0094]).
Therefore, it would have been obvious to one ordinary skill in the art to recognize that , not yielding to obstruction and navigate around the obstruction is time saving ( cost based analysis) and not being stuck into congestion and getting out from congestion provides comfort to the rider. Therefore, Gogna has such teachings.
As per Claim 14, Gogna as modified by Wright teaches the limitation of Claim 13. However, Gogna in view of Wright does not explicitly teach, wherein the comfort factors comprise at least induced kinematic discomfort and post-encroachment time.
However, it is obvious to one ordinary skill in the art to recognize that , when the autonomous vehicle is navigating on the streets, it is subject to encounter other obstructions, cyclist, pedestrians, user, other vehicles, in the environment, induced kinematic discomfort and post-encroachment is obviously present there.
However, incorporating in vehicle design induced kinematic discomfort and post-encroachment time into comfort factors, would be an obvious matter of design choice, In re Kuhle, 526 F.2d 553, 188 USPQ 7 (CCPA 1975).
As Per Claim 15, Gogna et al. (Gogna) teaches, a system comprising: a memory system; and one or more hardware processors coupled with the memory system, the one or more processors ( via vehicle computing device 404, comprising processor 416, memory 418, of vehicle 402, 102 (1), 102(2) Figs.4,1A,1B) configured to: operate an autonomous vehicle in a road setting in an operating environment having other road users (via yielding to other vehicle ,[0013]) Abstract), wherein the operation of the autonomous vehicle is based, at least in part, on one or more safety constraints providing limits on operation of the autonomous vehicle (Gogna : ([0013]), [0016-0020],[0023],[0027]), detect the presence of a selected other road user from the other road users within the operating environment, (Gogna :[0029], [0030], [0033], [0034], Figs. 1A), evaluate one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user ([0037-0042]).
However, Gogna does not explicitly teach, evaluate one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user using at least a cost- based analysis having a machine learned model, wherein the evaluation is based on a hierarchy of costs corresponding to characteristics of maneuvers by the autonomous vehicle, and cause the autonomous vehicle to interact with the selected other road user by generating vehicle control signals based on cost-based analysis and within the one or more safety constraints.
In a related field of Art, Wright et al. ( Wright) teaches, autonomous vehicle controller (computing device) being equipped with routing system, evaluate one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user using at least a cost- based analysis ([0048]) having a machine learned model ([0095]),, wherein the evaluation is based on a hierarchy of costs corresponding to characteristics of maneuvers by the autonomous vehicle, and cause the autonomous vehicle to interact with the selected other road user by generating vehicle control signals based on cost-based analysis and within the one or more safety constraints. ( [0037], [0044], [0048], [0095], Figs., 2, 3A).
It would have been obvious to one of ordinary skill in the art, having the teachings of Gogna and Wright before him before the effective filing date of the claimed invention to modify the systems of Gogna to include the teachings (controller ) of Wright and configure with the system of Gogna in order to generate route using a cost-based analysis attempting to select a route to the destination with the lowest cost and developing machine learning model for pedestrian objects, and being used in real-time by self driving vehicle. Motivation to combine the two teachings is, developing machine learning model for vehicle and using cost-based analysis for route computation (i.e., cost saving).
As per Claim 16, Gogna as modified by Wright teaches the limitation of Claim 15. However, Gogna in view of Wright further teaches, wherein the one or more hardware processors are further configured to translate yield probabilities between the autonomous vehicle and the other selected road user to costs to be utilized as part of the evaluation of the one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user. (Abstract, [0013], [0017-0023], [0033], [0059],[0093], Fig.1A, 4, 6).
As per Claim 17, Gogna as modified by Wright teaches the limitation of Claim 15. However, Gogna in view of Wright further teaches, wherein the other road users comprise one or more of: a human- operated vehicle, another autonomous vehicle, a pedestrian, a bicycle, and a streetcar. ( Gogna: Abstract, [0013], [0017-0023], [0033], [0059],[0093], Fig.1A, 4, 6). Also See (Wright : via vehicle may detect the presence of different objects on or adjacent to the roadway, including a stop sign 406 and a pedestrian crossing sign 408, as well as different people 410.sub.1, 410.sub.2 and 410.sub.3.”, [0068], Fig.4A)( Wright : via “nearby objects (e.g., cars or other vehicles on the roadway, pedestrians or bicyclists on sidewalks, etc.”, [0095]).
As per Claim 18, Gogna as modified by Wright teaches the limitation of Claim 15. However, Gogna in view of Wright further teaches, wherein the interaction between the autonomous vehicle and the selected other road user comprises a yield/assert decision. (Gogna : Abstract, [0013], [0017-0023], [0033], [0059],[0093], Fig.1A, 4, 6).
As per Claim 19, Gogna as modified by Wright teaches the limitation of Claim 18. However, Gogna in view of Wright further teaches, wherein a yield probability associated with the yield/assert decision is based on a non-linear relationship between an assert decision by the autonomous vehicle and one or more characteristics of the selected other road user. (Abstract, [0013], [0017-0023], [0033], [0059], [0093], Fig.1A, 4, 6).
As per Claim 20, Gogna as modified by Wright teaches the limitation of Claim 15. However, Gogna in view of Wright further teaches, wherein the evaluation of the one or more potential trajectories for the autonomous vehicle within the operating environment with respect to the selected other road user is performed (Abstract, [0013], [0017-0023], [0033], [0059], [0093], [0094],Fig.1A, 4, 6).
However, Gogna in view of Wright does not explicitly teach, potential trajectories of the autonomous vehicle with respect to the selected other road user is performed utilizing a cost-based analysis considering at least safety factors and comfort factors.
However, Gogna teaches, vehicle is getting instruction from teleoperator not to yield and continue navigation, ([0093-0094]).
Therefore, it would have been obvious to one ordinary skill in the art to recognize that , not yielding to obstruction and navigate around the obstruction is time saving ( cost based analysis) and not being stuck into congestion and getting out from congestion provides comfort to the rider. Therefore, Gogna has such teachings.
As per Claim 21, Gogna as modified by Wright teaches the limitation of Claim 20. However, Gogna in view of Wright does not explicitly teach, wherein the comfort factors comprise at least induced kinematic discomfort and post-encroachment time.
However, it is obvious to one ordinary skill in the art to recognize that , when the autonomous vehicle is navigating on the streets, it is subject to encounter other obstructions, cyclist, pedestrians, user, other vehicles, in the environment, induced kinematic discomfort and post-encroachment is obviously present there.
However, incorporating in vehicle design induced kinematic discomfort and post-encroachment time into comfort factors, would be an obvious matter of design choice, In re Kuhle, 526 F.2d 553, 188 USPQ 7 (CCPA 1975).
Conclusion
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/MUHAMMAD SHAFI/Primary Examiner, Art Unit 3663