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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/04/2025, 10/27/2025, and 12/04/2025 have been considered and are in compliance with the provisions of 37 CFR 1.97.
Status of Claims
This communication is a second office action, final rejection on the merits. Claims 1-16, and 18-20, as originally filed, remain pending and have been considered below. Claim 17 as amended, is currently pending and have been considered below.
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 (i.e., changing from AIA to pre-AIA ) 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)(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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being clearly anticipated by Phillips et al. (USP 10,730,531; hereinafter Phillips).
Regarding Claim 1:
Phillips discloses a method, comprising: generating, using one or more neural networks, one or more probabilities that correspond to a plurality of lateral positions of a vehicle with respect to a path based, at least in part, on one or more images that indicate an environment of the vehicle (Phillips, Column 14 Lines 4-20, Phillips discloses determining the potential risk (i.e. probabilities) of a vehicle collision with an object based on the vehicle telematics (i.e. position within the lane, see at least Column 3 Lines 5-44) and object telematics (i.e. environmental data proximate to the vehicle), see at least Column 3 Lines 45-67 and Column 8 Lines 25-45); and
causing the vehicle to use a modified path to proceed to a waypoint based, at least in part, on the one or more probabilities (Phillips, Fig. 4C, Column 12 Lines 33-64, Phillips discloses controlling the vehicle based on the determined risk associated with the object within the path of the vehicle).
Regarding Claim 2:
Phillips discloses the method of claim 1.
Phillips further discloses using at least one of the plurality of lateral positions to identify a location of the vehicle (Phillips, Column 17 Lines 30-43, Phillips discloses determining the current position of the vehicle is based on at least collected vehicle telematic data).
Regarding Claim 3:
Phillips discloses the method of claim 2.
Phillips further discloses controlling the vehicle by positioning the vehicle to the identified location based, at least in part, on steering directions received from a controller, wherein the controller uses at least one of the plurality of lateral positions to generate steering directions (Phillips, Fig. 4C, Column 12 Lines 33-64, Phillips discloses controlling the vehicle based on the determined risk associated with the object within the path of the vehicle, with controlling the vehicle including at least instructions for steering angle of the vehicle, see at least Column 14 Lines 21-33).
Regarding Claim 4:
Phillips discloses the method of claim 1.
Phillips further discloses computing a turn angle for the vehicle based, at least in part, on the one or more probabilities (Phillips, Fig. 4C, Column 12 Lines 33-64, Phillips discloses controlling the vehicle based on the determined risk associated with the object within the path of the vehicle, with controlling the vehicle including at least calculating instructions for steering angle of the vehicle, see at least Column 14 Lines 21-33).
Regarding Claim 5:
Phillips discloses the method of claim 1.
Phillips further discloses wherein the vehicle is an amphibious vehicle (Phillips, Column 7 Lines 53-65, Phillips discloses the autonomous vehicle includes one or any combination of ground based vehicle, air based vehicle, or water based vehicle).
Regarding Claim 6:
Phillips discloses the method of claim 1.
Phillips further discloses wherein the one or more neural networks comprise a deep neural network (DNN) (Phillips, Column 18 Lines 45-57, Phillips discloses the neural network comprises deep neural network).
Regarding Claim 7:
Phillips discloses the method of claim 1.
Phillips further discloses sending the one or more images, received at the vehicle, to a remote location; and obtaining, from the remote location using the one or more neural networks to process the one or more images, the one or more probabilities (Phillips, Column 20 Lines 39-58, Phillips discloses the environmental data (i.e. images) collected by the vehicle may be analyzed by the neural network for object risk prediction at either the vehicle or at a remote location or any combination thereof).
Regarding Claim 8:
Phillips discloses the method of claim 4.
Phillips further discloses determining the waypoint based, at least in part, on the turn angle (Phillips, Column 12 Lines 65 – Column 13 Lines 14, Phillips discloses the steering direction of the vehicle affects the passing distance margin (i.e. minimum distance from object to host vehicle)).
Regarding Claim 9:
The claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise.
Regarding Claim 10:
Phillips discloses the system of claim 9.
Phillips further discloses wherein the one or more processors are further to: train the one or more neural networks using one or more additional images; and wherein the one or more additional images comprise one or more labels indicating one or more positions (Phillips, Column 7 Lines 20-25, Phillips discloses the machine learned models trained and revised in training based on the newly collected data of the vehicle).
Regarding Claim 11:
Phillips discloses the system of claim 9.
Phillips further discloses wherein the one or more processors are further to train the one or more neural networks to perform object detection based on the one or more additional received images (Phillips, Column 7 Lines 20-25, Phillips discloses the machine learned models trained and revised in training based on the newly collected data of the vehicle).
Regarding Claim 12:
Phillips discloses the system of claim 10.
Phillips further discloses wherein the one or more processors are further to send object data to a controller to calculate a size of an object in the one or more additional images (Phillips, Column 9 Lines 15-29, Phillips discloses the collected environmental data is processed to determine upcoming object’s properties, such properties include at least size of the object).
Regarding Claim 13:
Phillips discloses the system of claim 10.
Phillips further discloses wherein the one or more processors are further to train the one or more neural networks to perform obstacle detection based on the one or more additional received images (Phillips, Column 7 Lines 20-25, Phillips discloses the machine learned models trained and revised in training based on the newly collected data of the vehicle).
Regarding Claim 14:
The claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise.
Regarding Claim 15:
The claim recites analogous limitations to claim 2 above, and is therefore rejected on the same premise.
Regarding Claim 16:
Phillips discloses the processors of claim 15.
Phillips further discloses receive the one or more images from one or more infrared imaging devices; and use information from the one or more images to generate the one or more probabilities (Phillips, Column 14 Lines 4-20, Phillips discloses determining the potential risk (i.e. probabilities) of a vehicle collision with an object based on the vehicle telematics (i.e. position within the lane, see at least Column 3 Lines 5-44) and object telematics (i.e. environmental data proximate to the vehicle), see at least Column 3 Lines 45-67 and Column 8 Lines 25-45).
Regarding Claim 17:
Phillips discloses the processors of claim 14.
Phillips further discloses wherein the plurality of orientations of the vehicle comprises a position of the vehicle with respect to a centerline of the path (Phillips, Column 13 Lines 5-35, Phillips discloses the autonomous vehicle includes determining the orientation of the vehicle with respect to the center of the current lane of travel).
Regarding Claim 18:
The claim recites analogous limitations to claim 4 above, and is therefore rejected on the same premise.
Regarding Claim 19:
Phillips discloses the processors of claim 14.
Phillips further discloses convert at least one of the plurality of lateral positions of the vehicle, at a controller, to steering instructions (Phillips, Column 16 Lines 2-30, Phillips discloses determining vehicle motion plan based on the environmental data and vehicle telematic data);
convert the steering instructions to a set of instructions executable by the vehicle; and send the steering instructions to vehicle (Phillips, Column 16 Lines 2-30, Phillips discloses implementing the calculated motion plan).
Regarding Claim 20:
The claim recites analogous limitations to claim 8 above, and is therefore rejected on the same premise.
Response to Arguments
Applicant's arguments filed 8/27/2025 have been fully considered but they are not persuasive.
In particular the applicant argues the cited art of Phillips fails to disclose “generating, using one or more neural networks, one or more probabilities that correspond to a plurality of orientations of a vehicle with respect to a path”. However the examiner respectfully disagrees. Phillips clearly discloses determining upcoming object in the path of the vehicle and generating probabilities (i.e. confidence scores) that the object would collide or not based on the object location/trajectory and the vehicle telematics data, based on collected image data, see at least Phillips, Column 14 Lines 4-20, Column 3 Lines 5-67, and Column 8 Lines 25-45. Therefore the argued limitations is disclosed by the cited art.
Additionally, the applicant argues the cited art of Phillips fails to disclose a “neural network generating a probability”. However Phillips clearly discloses the machine learned model which determines the confidence score (i.e. probability) includes various machine learned models such as neural networks, see at least Phillips Column 17 Lines 44-54. Therefore the argued limitations is disclosed by the cited art.
Conclusion
THIS ACTION IS MADE FINAL. 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 ZACHARY JOSEPH WALLACE whose telephone number is (469)295-9087. The examiner can normally be reached 7:00 am - 5:00 pm, Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached at (571) 270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Z.J.W./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656