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 rejections
35 U.S.C. 102 rejections
35 U.S.C. 103 rejections
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 and 15-18, 20-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1-10 are held to claim an unpatentable abstract idea, and are therefore rejected as ineligible subject matter under 35 U.S.C. § 101.
The limitations of the independent claims of receiving an observed state of each of the plurality of agents, and map data of the mapped area; generating an initial estimated trajectory foreach of the plurality of agents based on the observed state of each agent and the map data; performing a first collision assessment to determine a likelihood of collision between the first agent and each other agent, based on the initial estimated trajectory for the first agent and the initial estimated trajectory for each other agent; and generating a second estimated trajectory for the first agent based on the observed states of each of the plurality of agents, the map data, and at least one result of the first collision assessment; determine a planned trajectory for an autonomous vehicle derived from the second estimated trajectory; and determine a control signal to be executed by the autonomous vehicle based on the planned trajectory covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the application of the steps by a generic processor nothing is being recited that could not be performed mentally. The new limitations do not add any steps that are not mental steps because they are simply determining a trajectory and determining (not executing) a control signal. The control signal is not even defined and could be anything, including merely displaying information.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the ‘Mental Processes’ grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the elements of a processor to perform the listed steps. The processor is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Looking at claim 16, while this claim does recite training data and a loss function, this is recited at such a high level of generality that this could be performed mentally, without machine learning. A person can take in training data and calculate a loss function.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the listed steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Turning to the dependent claims, the claims either recite repeated mental steps of the independent claim or what the trajectories comprise. Though some claims recite the use of a neural network, this is again recited at a high level of generality that does not integrate the claim into a practical application. Thus, all dependent claims are additionally rejected under 35 U.S.C. 101. Particularly, newly amended claim 20, similar to claim 16, for the recitation of a third neural network, computing parameters based on a loss function, and penalizing deviations this is recited at such a high level of generality that this could be performed mentally, without machine learning.
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.
Claim(s) 1, 5, 8, 12-13, 15-16, and 22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SafetyNet (https://arxiv.org/abs/2109.13602, included on a provided IDS).
Regarding claim 1, SafetyNet discloses a computer-implemented method of generating a trajectory for a first agent of a plurality of agents navigating a mapped area, the method comprising:
receiving an observed state of each of the plurality of agents (Section III.A discloses the current and past poses of the SDV and the current and past poses of perceived agents), and map data of the mapped area (Section III.A discloses static and dynamic map elements);
generating an initial estimated trajectory for each of the plurality of agents based on the observed state of each agent and the map data (Section III.B discloses that the ML planning component of our system takes the input capturing the states around the SDV and outputs the trajectory to be executed. Section III.C then discloses this is with predicted poses of other agents from an in-house prediction module);
performing a first collision assessment to determine a likelihood of collision between the first agent and each other agent, based on the initial estimated trajectory for the first agent and the initial estimated trajectory for each other agent (Section III.C discloses that we check each state in the given trajectory for collisions with predicted poses of other agents from an in-house prediction module and if any of the collision likelihood checks fail, the trajectory is labeled as infeasible); and
generating a second estimated trajectory for the first agent based on the observed states of each of the plurality of agents, the map data, and at least one result of the first collision (Section III.C discloses if the trajectory is labeled as infeasible, we select a feasible fallback trajectory as close as possible to the ML trajectory, and each of the generated trajectories is checked for feasibility as described above);
determining a planned trajectory for an autonomous vehicle derived from the second estimated trajectory (Section III.C discloses if the trajectory is labeled as infeasible, we select a feasible fallback trajectory as close as possible to the ML trajectory, and each of the generated trajectories is checked for feasibility as described above. This is a determined planned trajectory, which is (for claim 22) the second estimated trajectory); and
determining a control signal to be executed by the autonomous vehicle based on the planned trajectory (Page 4, Experiments section discloses performance of SafetyNet in the real world when controlling a real vehicle).
Regarding claim 5, SafetyNet discloses each of the estimated trajectories comprises a series of states for the respective agent, the states defining position and motion of the agent (Section III.A discloses we definite a trajectory as a sequence of T discrete states and where x y o correspond to the pose of the rear axle of the SDV w/r/t a fixed coordinate frame at time t and v, a, k, j correspond to the velocity longitudinal acceleration, curvature and jerk respectively).
Regarding claim 8, SafetyNet discloses the first or second collision assessment comprises computing a collision probability defining a probability that an agent following the estimated trajectory collides with any other agent of the plurality of agents following any of the agent’s respective estimated trajectories (Section III.C discloses after generating an ML trajectory, our system evaluates it along several dimensions for dynamic feasibility, legality, and collision probability. Section III.C further teaches checking each state in the given trajectory for collisions with predicted poses of other agents from an in-house prediction module).
Regarding claims 12 and 13, the additional features consist of generating a trajectory being applied for an ego agent and generating a candidate planned trajectory for the ego agent (claim 12) and that the candidate trajectory is used to generate a final planned trajectory (claim 13). Generating a trajectory for an ego agent navigating a mapped area is simply a mathematical model which is equivalent to the model disclosed in claim 1.
Claims 15 and 16 recite substantially similar subject matter as claim 1. Though claim 16 recites different terms such as ground truth and loss function, this is equivalent to modifying trajectories based on feasible/infeasible as above. Further Section III.B recites minimizing the L1 loss between the poses generated by the model and the ground truth poses.
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) 2, 3, 4, 6, 7, 9-11, 17-18, and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over SafetyNet.
Regarding claims 2 and 6, SafetyNet discloses wherein the initial estimated trajectory is generated by a first neural network configured to receive the observed state of each agent and the map data as inputs, and wherein the second estimated trajectory is generated by a second neural network, wherein the second neural network is configured to receive the observed state of each agent (Section III.B discloses that our model is built on a hierarchical graph network-based architecture. A neural network consists of a mathematical model which is equivalent to the disclosed hierarchical graph network). Therefore, it would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to substitute the model of SafetyNet with the neural network to achieve predictable results.
Regarding claims 3 and 4, the limitations of claim 3 amounts to simply repeating the steps of claim 1 a second and third time. Therefore, it would be obvious to a person having ordinary skill in the art to simply duplicate the steps to achieve additional refined results.
Regarding claim 7, SafetyNet discloses wherein generating each of the estimated trajectories comprises directly generating, by the respective neural network, a time sequence of states defining the trajectory of the agent (Section III.B discloses a kinematic layer takes the predictions as well as the current ego state to roll out the next state of the ego).
Regarding claims 9-11, the trajectory is based on an ML trajectory including weights and motions related to an observed state, which reads on each of these claims.
Claims 17-18, and 21 are mirror claims.
Regarding claim 20, the rejection for claim 16 already discloses loss function and updated parameters. Using a third neural network, instead of a first or a second as discussed in the rejections to claim 1 and claim 2 amounts to merely duplicating parts. The steps are repeated in the exact same way, just for a third neural network.
Response to Arguments
Applicant’s arguments are fully considered but are deemed moot. Regarding the prior art rejections, Applicant argues that the new rejections are not taught by the prior art. The Examiner disagrees. The prior art teaches:
determining a planned trajectory for an autonomous vehicle derived from the second estimated trajectory (Section III.C discloses if the trajectory is labeled as infeasible, we select a feasible fallback trajectory as close as possible to the ML trajectory, and each of the generated trajectories is checked for feasibility as described above. This is a determined planned trajectory, which is (for claim 22) the second estimated trajectory); and
determining a control signal to be executed by the autonomous vehicle based on the planned trajectory (Page 4, Experiments section discloses performance of SafetyNet in the real world when controlling a real vehicle).
Regarding the 35 U.S.C. 101 rejections, again the new amendments do not overcome the prior rejections. The new limitations do not add any steps that are not mental steps because they are simply determining a trajectory and determining (not executing) a control signal. The control signal is not even defined and could be anything, including merely displaying information.
Further, newly amended claim 20, similar to claim 16, for the recitation of a third neural network, computing parameters based on a loss function, and penalizing deviations this is recited at such a high level of generality that this could be performed mentally, without machine learning.
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.
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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ARYAN E. WEISENFELD
Primary Examiner
Art Unit 3689
/ARYAN E WEISENFELD/Primary Examiner, Art Unit 3663
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