DETAILED ACTION
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 .
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, 4, 8, 18, 21-22 and 24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Srikanth et bal. Titan:Future Forcast using action Priors 3/31/2020 NPL reference 2 of IDS filed 12/12/2024.
NPL reference 2 is applied to the limitations of claim 1 as follows it discloses a computer-implemented [section 9: 'TITAN framework is trained on a Tesla V100 GPU using PyTorch Framework'] method of predicting trajectories for agents of a scenario [abstract: 'to forecast future trajectory of agents and future ego-motion], the method comprising, for each agent: generating an agent feature vector based on one or more observed past states of the agent [section 4.2: we model the interactions using the past locations of agents conditioned on their actions', 'At each past time step t, the given bounding box x¹ₜ = {cui Cvi 1u; 1v}ₜ is concatenated with the multi-label action vector ait], computing a set of pairwise feature vectors, each pairwise feature vector computed as a combination of the agent feature vector for that agent with a respective one of the agent feature vectors generated for each other agent of the scenario [section 4.2: 'We model the pair-wise interactions between the target agent i and all other agents j through a¹ₜ MLP, vi⁺ = (x¹ x³ₜ a³ₜ ) where is a X X X concatenation operator], processing the pairwise feature vectors as independent inputs to one or more interaction layers of a trajectory prediction neural network to generate a pairwise output for each pairwise feature vector [section 4.2: 'The resulting interactions vij are evaluated through the dynamic RNN with GRUs to leave more important information with respect to the target agent, = GRU (vi⁺ₜ ; h¹³ₜ ; WINT) '], aggregating the pairwise outputs over the other agents of the scenario to generate an interaction-based feature representation for each agent [section4.2: Then, we aggregate the hidden states to generate interaction features vit = 1/n Σ hⁱ⁺ₜ for the target agent i, computed from all other agents in the scene at time t'], processing the interaction-based feature representation in one or more prediction layers of the trajectory prediction neural network [Figure 6, section 4.2 shows that is used to build an hidden state that is then input to the GRU], and generating, based on the output of the one or more prediction layers, at least one predicted trajectory for each agent [section 4.3:'It thus provides insight into assessment of perceived risk while predicting the future motion, 'Note that the predicted future ego- motion is deterministic in its process'].
updating one or more parameters of the trajectory prediction neural network so as to optimise a loss function based on the at least one predicted trajectory for each agent and the corresponding ground truth trajectory for that agent [section 4.3: 'For training, we use task dependent uncertainty with L2 loss for regressing both acceleration and angular velocity as shown below', Equation 4, being implied that the Loss is used to update parameters].
Claim 4: The additional feature of claim 4 is known from NPL reference 2 that discloses the use of yaw rate [section 4: 'for the ego-motion encoder where αₜ and Wt correspond to the acceleration and yaw rate of the ego- vehicle at time t'].
Claim 8: The additional feature of claim 8 is known from NPL referewnce 2 that discloses the concatenation operation [section 4.2: 'We model the pair-wise interactions between the target agent i and all other xˢₜ agents j through MLP, vij = Φᵣ (xⁱₜ a¹ₜ a³ₜ ) where is a concatenation operator].
Claim 18: The additional feature of claim 18 is known from NPL reference 2 that discloses at least one agent being an autonomous vehicle [Figure 1: ego-vehicle].
Claims 21 and 24 follow from the above rejection.
Claim 22: The additional feature of claim 22 is known from NPL reference 2 that discloses the use of a regression loss function [section 4.3: For training, we use task dependent uncertainty with L2 loss for regressing both acceleration and angular velocity as shown below', Equation 4].
Allowable Subject Matter
Claims 2, 5, 7, 9, 11-14, 16, 17, 19, 25 and 26 are 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD M CAMBY whose telephone number is (571)272-6958. The examiner can normally be reached M - F flex.
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/RICHARD M CAMBY/Primary Examiner, Art Unit 3661
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