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 .
Contents of this Office Action:
35 U.S.C 101 Interpretation
35 U.S.C. 112(b) rejections
35 U.S.C. 102 rejections
Relevant Prior Art
Election/Restrictions
Applicant’s election without traverse of claims 1-8 in the reply filed on 8/24/2026 is acknowledged.
Claim Rejections - 35 USC § 101
The present claims are not subject to a 35 U.S.C. 101 rejection because it uses machine learning concepts that cannot be performed mentally. Specifically, according to P31-38 of the specification, the machine learning includes at least specific loss function calculations and is based on at least imitation and reinforcement learning. The result of the DDP output is then used for developing an iterative trajectory optimization planner, where an output trajectory is determined. These are not mental processes, nor are they strictly mathematical concepts, and therefore the claims are compliant with 35 U.S.C. 101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1, and as such all dependent claims that depend from claim 1, are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Specifically, claim 1 recites a “local” scene, but it is not clear what local means. It is not clear what the metes and bounds of local are, and as such, the Examiner will interpret local scene data as any acquirable scene data.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Dally US20210124353A1.
Regarding claim 1, Dally discloses an autonomous vehicle (P16 discloses various approaches to navigating or maneuvering autonomous (or at least semi-autonomous) vehicle involve some type of path planning for the vehicle) comprising:
one or more processors and a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors (P48 discloses the vehicle typically will include one or more computer processors 608 and memory 610 including instructions executable by the processor(s) for purposes of making decisions about the vehicle and/or enacting those decisions to control the vehicle), cause the one or more processors to:
receive a driving preference (P16 discloses predicting an optimal or preferred trajectory of the car based on factors such as the current state of the environment, goals, and predicted reactions, then determined maneuvers that function to follow the optimal trajectory. The goals in this case are equivalent to a driving preference. P17 discusses explicit preferences as well);
receive local scene data (P17 discloses the vehicle will have to take into consideration that there are other objects in the environment that the vehicle must take into consideration. This can include, for example, considering other vehicles (e.g., vehicles 104, 106, among others) on the road 108, as well as their relative speeds and directions of travel, etc. In almost all situations it will be a goal of the vehicle to prevent collisions with the other vehicles while accomplishing its goal);
input the local scene data into a data-driven planner and an iterative trajectory optimization planner (P34 discloses the path planning benefits from prediction with respect to movement of the other vehicles. The reactions of these other vehicles can be predicted at each time step in order to compute an “optimal” path based on the currently available sensor data. A sequence of “moves” can be determined that will enable completion of the lane change. In this example it can be determined that the vehicle should decelerate, move to the right, and signal the intended move to the right);
generate, using the data-driven planner, a DDP output comprising a DDP trajectory based at least in part on the local scene data and the driving preference (P35 discloses during each tree search, a planner can generate a number of possible moves for the present car for each even level of the tree. In the example, from the initial position the present vehicle car could do nothing (hold speed), it could turn to the left, it could turn to the right while holding speed, or turn to the right while decelerating. A “move generator” deep neural network (DNN) can be used in some embodiments to generate the three or four “highest value” moves to be explored, as may be based upon training from many instances of vehicle motion data. Similarly each other vehicle or actor can respond in multiple ways. The “move generator” DNN can generate the most likely move for non-critical actors, and up to three or four most probable moves for critical actors (such as the actors directly adjacent the present vehicle). At each step of the tree a second DNN can be used to assign a value to the position. A large negative value can be associated with any contact, with a smaller negative value being associated with getting too close or failing to maintain at least a specified distance or separation from another actor or object. A positive value can be associated with achieving the respective goal, such as by successfully moving into the right lane);
input the DDP output into the iterative trajectory optimization planner, wherein the DDP output is used as a cost of a plurality of costs of a cost function that is minimized by the iterative trajectory optimization planner (P57 discloses the neural network does not correctly label the input, then errors between the correct label and the predicted label are analyzed, and the weights are adjusted for each feature during a backward propagation phase until the DNN correctly labels the input and other inputs in a training dataset. This is minimization of a cost function); and
generate, using the iterative trajectory optimization planner, an output trajectory (P30 discloses the optimizer can take the path data, smooth out the actions, and produce a trajectory for the vehicle with a much finer granularity).
Regarding claim 2, Dally discloses wherein the data-driven planner is trained using a plurality of sets of parameters of the iterative trajectory optimization planner (P17 discloses there may be various other goals as well, such as to maximize the comfort of the occupant of a vehicle, maximize the view or experience, avoid near-collisions or jarring actions, and the like).
Regarding claim 3, Dally discloses wherein each set of parameters of the plurality of sets of parameters corresponds with an individual driving preference (See claim 2 above).
Regarding claim 4 and 5, the Examiner notes that the only structural component of these claims is the display. As such, the specific information the display receives and outputs is non-functional descriptive material and is not afforded patentable weight. However, to expedite compact prosecution, the Examiner notes that P80 discloses the device typically will include some type of display element 906, such as a touch screen, organic light emitting diode (OLED) or liquid crystal display (LCD), although devices such as portable media players might convey information via other means, such as through audio speakers. This means there is a display that can input and output any piece of information, which as discussed in the rejection to claims 1-3, include individual driving preferences.
Regarding claim 6, Dally discloses wherein the driving preference is one of a sport preference, a comfort preference, and an eco-preference (P14 discloses actions can be considered based on factors such as the corresponding amount of risk or loss, favorability, occupant comfort, and the like).
Regarding claim 7, Dally discloses wherein the instructions further cause the one or more processors to receive a new driving preference such that the DDP output is based on the new driving preference (As discussed above, there are a plurality of goals, not just one piece of data fed into the algorithm. P14 the selected path and related data can be used to update one or more machine learning models that were used for the determination, such as by sending the relevant data to a remote server capable of further training the models).
Regarding claim 8, Dally discloses wherein the instructions further cause the one or more processors to update the parameters of the iterative trajectory optimization planner based on the new driving preference (P14 discloses the selected path and related data can be used to update one or more machine learning models that were used for the determination, such as by sending the relevant data to a remote server capable of further training the models).
Relevant Prior Art
US20220402485A1, directed towards techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle computing device can implement a model to output data indicating costs for potential intersection points between the object and the vehicle in the future. The model may employ a control policy and a time-step integrator to determine whether an object may intersect with the vehicle, in which case the techniques may include predicting vehicle actions by the vehicle computing device to control the vehicle.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARYAN E WEISENFELD whose telephone number is (571)272-6602. The examiner can normally be reached M-F 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached at 5712721206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ARYAN E. WEISENFELD
Primary Examiner
Art Unit 3689
/ARYAN E WEISENFELD/Primary Examiner, Art Unit 3663
Prosecution Insights