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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/25/2025 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Election/Restrictions
Claim 11-12 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention Group II, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 6/10/2026.
Conforms with 35 USC § 101
The presently examined claims were evaluated for a 101 Alice type rejection. The conclusion from going through the Alice/Mayo test is that the independent claims are integrated into a practical application (or cannot be performed merely with the human mind) and are therefore patent eligible under 35 U.S.C. 101. See MPEP §2106, subsection III and MPEP §2106.04, subsection II(A).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 1-3, 5-10, 13-15, and 17-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20260091792 A1 hereinafter TAO.
As to claim 1, TAO discloses A method of extracting a bird’s eye view (BEV) feature, [TAO: abstract] the method comprising:
generating, from a diffusion model, a driving scenario of a vehicle, based on guide information, the driving scenario comprising at least one piece of map data; [TAO: see portions related to perception 0018, 0047]
inferring, using a pre-trained neural network, [TAO: neural networks are models 0016, Fig. 1, 0026] at least one BEV feature corresponding to the at least one piece of map data by inputting the at least one piece of map data to the pre-trained neural network; and [TAO: see portions related to perception 0018, 0047]
setting a path of an autonomous driving model based on the at least one BEV feature, [TAO: 0048 “The planning model 412 can include route planning, trajectory planning, and decision-making for the ego vehicle to take to navigate relative to the other objects in the BEV, and turn those decisions into actions taken by the vehicle in real life.”] the autonomous driving model controlling a driving operation of the vehicle. [TAO: 0043, 0009]
As to claim 2 and 14, TAO discloses wherein the generating of the driving scenario of the vehicle comprises: obtaining, from the diffusion model for a predetermined time period, [TAO: predicted trajectories are time based and location based the predetermined time corresponds to the prediction.] movement data corresponding to at least one timestamp, [TAO: 0060, timestamp with a trajectory “The ego-car is turning left. Planned 3-timestamp future trajectory is (x1,y1), (x2,y2), and (x3,y3). ”] the movement data comprising a location of the vehicle, [TAO: GPS 0040] a speed of the vehicle, and a direction of the vehicle; [TAO: 0060, timestamp with a trajectory/speed/ direction “The ego-car is turning left. Planned 3-timestamp future trajectory is (x1,y1), (x2,y2), and (x3,y3). ”] and generating the driving scenario by synthesizing map data that displays the movement data on a map [TAO: may be on a display 0032-0033. See planning 412, 0058], based on the at least one timestamp. [TAO: 0060, timestamp with a trajectory/speed/ direction “The ego-car is turning left. Planned 3-timestamp future trajectory is (x1,y1), (x2,y2), and (x3,y3). ]
As to claim 3 and 15, TAO discloses wherein the generating of the driving scenario of the vehicle comprises: generating the driving scenario based on the guide information comprising at least one of a maximum speed limit of the vehicle, [TAO: 0068 “Success rate is proportion of routes that the ego vehicle can complete without any traffic infractions.”] a destination of the vehicle, [TAO; 0006 “execute a planning machine-learning model on the BEV to generate predicted trajectories to navigate an autonomous vehicle in the environment;”] or vehicle signal settings during driving of the vehicle. [TAO: 0064 traffic signals]
As to claim 5 and 17, TAO discloses wherein the inferring of the at least one BEV feature comprises: extracting the at least one BEV feature corresponding to at least one timestamp comprised in the driving scenario. [TAO: 0060, timestamp with a trajectory/speed/ direction “The ego-car is turning left. Planned 3-timestamp future trajectory is (x1,y1), (x2,y2), and (x3,y3). ”]
6 and 18, TAO discloses further comprising: obtaining the pre-trained neural network by training a neural network using a loss function based on a difference between a first BEV feature of the map data and a second BEV feature of a multi-view camera image corresponding to the map data. [TAO; uses contrastive learning model 0057 within the models and 0069 “aligning with the loss-based refinement process (e.g., minimizing the error between predicted and actual trajectories).”]
As to claim 7 and 19, TAO discloses further comprising: training, by using the at least one BEV feature, at least one neural network from among a plurality of neural networks [TAO: neural networks are models 0016], comprising a first neural network configured to detect movement of a surrounding vehicle around the vehicle, [TAO: perception model, Fig. 4] a second neural network configured to predict an occupancy of a surrounding road and an operation of the surrounding vehicle, [TAO: prediction model, Fig. 4] and a third neural network configured to determine a next moving path of the vehicle. [TAO: planning model, Fig. 4]
As to claim 8 and 20, TAO discloses further comprising: training the third neural network using a loss function based on a difference between the at least one BEV feature, a first BEV feature of a multi-view camera image, and a second BEV feature of map data corresponding to the multi-view camera image. [TAO: uses contrastive learning model 0057 within the models]
As to claim 9, TAO discloses further comprising: training the first neural network using a first loss function based on a first difference between a first BEV feature of a multi-view camera image and a second BEV feature of map data corresponding to the multi-view camera image; [TAO: uses contrastive learning model 0057 within the models] and training the second neural network using a second loss function based on a second difference between the first BEV feature, the second BEV feature, and a third BEV feature of generated map data corresponding to the multi-view camera image. [TAO; uses contrastive learning model 0057 within the models and 0069 “aligning with the loss-based refinement process (e.g., minimizing the error between predicted and actual trajectories).”]
As to claim 10, TAO discloses wherein the training of the second neural network comprises: obtaining the at least one piece of map data by converting a coordinate system around the vehicle; [TAO: Fig. 4 arrow going from 408 to 410. The map data is obtained in the perception model using GPS in localization with sensor fusion and object detection 0018, that data is sent to prediction and planning when created.] and inputting the at least one converted piece of map data to the second neural network. [TAO: Fig. 4 arrows leaving #408]
As to claim 13, TAO discloses A device for extracting a bird’s eye view (BEV) feature, the device comprising: one or more processors comprising processing circuitry; and memory storing instructions, wherein the instructions, when executed by the one or more processors individually or collectively, cause the device to: [TAO: 0006] The remaining limitations mirror those of claim 1 and are similarly rejected. See claim 1 for citations.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim 4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over TAO in view of US 20200201356 A1 hereinafter Schuh.
As to claim 4 and 16, TAO discloses wherein the generating of the driving scenario of the vehicle comprises: generating the driving scenario based on the guide information comprising a weight setting for positioning the vehicle on a road within a map [TAO: 0037, 0026, 0036] .
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240193793 A1 The present application provides a method and a unified framework system for full-stack autonomous driving planning. The method comprises: acquiring an image of a scene and converting the image into an image feature, and converting the image feature into a bird's-eye view feature map; detecting agents from the bird's-eye view feature map through track query vectors, and continuously tracking the agents; segmenting different types of map elements from the bird's-eye view feature map through map query vectors, and continuously updating the map elements; predicting a future trajectory of each agent using an interaction between the agents and the different map elements; predicting, according to the predicted future trajectory of each agent, an occupancy grid map over multi-steps into the future; and decoding an ego-vehicle query vector to generate a planned path of an ego-vehicle, and optimizing the planned path using the predicted future multi-step occupancy grid map.
US 11521396 B1 Systems and methods are described that probabilistically predict dynamic object behavior. In particular, in contrast to existing systems which attempt to predict object trajectories directly (e.g., directly predict a specific sequence of well-defined states), a probabilistic approach is instead leveraged that predicts discrete probability distributions over object state at each of a plurality of time steps. In one example, systems and methods predict future states of dynamic objects (e.g., pedestrians) such that an autonomous vehicle can plan safer actions/movement.
The examiner has pointed out particular references contained in the prior art of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. Applicant should consider the entire prior art as applicable as to the limitations of the claims. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDERICK M BRUSHABER whose telephone number is (313)446-4839. The examiner can normally be reached Monday-Friday 8am-5pm.
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/FREDERICK M BRUSHABER/
Primary Examiner
Art Unit 3665
/FREDERICK M BRUSHABER/Primary Examiner, Art Unit 3665
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