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 .
2. This Office Action is sent in response to Applicant's Communication received on March 23, 2026.
Response to Arguments
Applicant’s arguments filed March 23, 2026, with respect to claims 1-7, 11-13 and 16 rejections under 35 U.S.C. 102(a)(1) have been fully considered and are persuasive. Accordingly, said claims 1-7, 11-13 and 16 rejections under 35 U.S.C. 102(a)(1) have been withdrawn.
Further on, claims 1-7 are allowable over the prior art.
Still further, claims 11-13 and 16 are objected as allowable subject matter.
Applicant's arguments filed March 23, 2026, with respect to claims 8-10, 14-15 and 17-20 rejections under 35 U.S.C. 102(a)(1) have been fully considered but they are not persuasive as explained below.
Applicant respectfully asserts that the cited prior art fails to meet the limitations of at least independent claims 8 and 18.
The Examiner respectfully submits that as discussed with Applicant’s Representative the Present Application distinguished over the closest prior art and is claimed in enough detail in independent claim 1. Accordingly, the Examiner based on that discussion and persuasive arguments by Applicant and Applicant’s Representative, indicated claims 1-7 allowable over the prior art and claims 11-13 and 16 objected as allowable subject matter. However, the Examiner respectfully further submits that independent claims 8 and 18 still reads on the cited prior art as discussed below in section 35 USC 102 as being anticipated by (Han – US 2021/0001877 A1).
Disposition of Claims
Claims 1-20 are pending in this application.
Claims 1-7 are allowable over the prior art.
Claims 11-13 and 16 are objected as allowable subject matter.
Claims 8-10, 14-15 and 17-20 are rejected.
Allowable Subject Matter
Claims 1-7 are allowable over the prior art
Claims 11-13 and 16 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.
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.
Claims 8-10, 14-15 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by (Han – US 2021/0001877 A1).
Regarding claim 8, Han discloses:
A system comprising:
one or more processors ([0235]: “processor 4302 (e.g., a central processing unit (CPU), a graphics processing unit (GPU)”) to:
classify one or more surface markings corresponding to one or more edges of one or more lanes along a path determined using map data ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]);
determine, based at least on the classification of the one or more surface markings, one or more behaviors associated with the one or more lanes ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]);
select, based at least on the one or more behaviors, at least a first lane of the one or more lanes for a machine to use to navigate the path ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]); and
cause the machine to perform one or more operations associated with using the first path to navigate the path ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]).
Regarding claim 18, Han discloses:
A machine (At least one of vehicles 150: Fig. 1) comprising:
a powertrain ([0245]: engine);
one or more central processing units (CPUs) ([0235]: “processor 4302 (e.g., a central processing unit (CPU), a graphics processing unit (GPU)”);
one or more graphics processing units (GPUs) ([0235]: “processor 4302 (e.g., a central processing unit (CPU), a graphics processing unit (GPU)”);
one or more hardware accelerators ([0003]: Autonomous vehicles inherently have any type of hardware accelerator. Otherwise, it will not move at all);
one or more internal sensors for monitoring one or more passengers in-cabin ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]); and
one or more external sensors for perceiving an environment outside of the machine ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]), wherein the machine is to perform one or more control operations within one or more lanes of the environment based at least on map data indicating a path through the environment and sensor data obtained using the one or more external sensors, wherein the sensor data is processed to determine ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]), based at least on behavior information associated with one or more lanes in the environment, a lane of the one or more lanes for the machine to occupy in order to navigate in accordance with the path ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]).
Regarding claim 9, Han discloses the system according to claim 8, and further on Han also discloses:
determine a location of the machine with respect to the one or more lanes based at least on the one or more behaviors associated with the one or more lanes ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]).
Regarding claim 10, Han discloses the system according to claim 8, and further on Han also discloses:
determine, based at least on sensor data obtained using one or more sensors of the machine, a geographic region the machine is operating in, wherein the classification of the one or more surface markings is further based at least on the geographic region (Figs. 6A-6B and [0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]).
Regarding claim 14, Han discloses the system according to claim 8, and further on Han also discloses:
determine one or more patterns associated with one or more classifications of the one or more surface markings, wherein the determination of the one or more behaviors is based at least on the one or more patterns ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]).
Regarding claim 15, Han discloses the system according to claim 8, and further on Han also discloses:
wherein the one or more surface markings correspond to one or more lane line markings associated with the one or more lanes ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]).
Regarding claim 17, Han discloses the system according to claim 8, and further on Han also discloses:
wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine ([0052]: autonomous vehicle);
a perception system for an autonomous or semi-autonomous machine ([0052]: autonomous vehicle);
a system for performing one or more simulation operations ([0239]: “Techniques displayed herein can also be applied for displaying maps for purposes of computer simulation, for example, in computer games, and so on”);
a system for performing one or more digital twin operations (A digital twin operation refers to the use of a virtual, real-time replica of a physical asset, system, or process to monitor, analyze, optimize, and manage its real-world counterpart throughout its entire lifecycle. Accordingly, Han discloses that the disclosure can be used for displaying maps for purposes of computer simulation, for example, in computer games, and so on: [0239]);
a system for performing light transport simulation ([0003, 0239]: “Techniques displayed herein can also be applied for displaying maps for purposes of computer simulation, for example, in computer games, and so on” and “Autonomous vehicles, also known as self-driving cars, driverless cars, or robotic cars, may drive from a source location to a destination location without requiring a human driver to control or navigate the vehicle. Automation of driving may be difficult for several reasons. For example, autonomous vehicles may use sensors to make driving decisions on the fly, or with little response time, but vehicle sensors may not be able to observe or detect some or all inputs that may be required or useful to safely control or navigate the vehicle safely in some instances”);
a system for performing collaborative content creation for 3D assets (Han discloses that their invention can be used for computer games: [0239]);
a system for performing one or more deep learning operations (image segmentation deep learning model: [0127]);
a system implemented using an edge device (Edge devices are hardware endpoints (like sensors, cameras, smartphones, routers) located at the network's edge, near data sources, that collect, process, and transmit data locally rather than sending everything to a central cloud: [0072]: “The vehicle sensors 105 may comprise a camera, a light detection and ranging sensor (LIDAR), a global navigation satellite system (GNSS) receiver, for example, a global positioning system (GPS) navigation system, an inertial measurement unit (IMU), and others. The vehicle sensors 105 may include one or more cameras that may capture images of the surroundings of the vehicle. A LIDAR may survey the surroundings of the vehicle by measuring distance to a target by illuminating that target with a laser light pulses and measuring the reflected pulses. The GPS navigation system may determine the position of the vehicle 150 based on signals from satellites. The IMU may include an electronic device that may be configured to measure and report motion data of the vehicle 150 such as velocity, acceleration, direction of movement, speed, angular rate, and so on using a combination of accelerometers and gyroscopes or other measuring instruments”);
a system implemented using a robot (robotic cars: [0003]);
a system for performing one or more generative AI operations (Artificial Intelligence operations include image segmentation deep learning model: [0127]);
a system for performing operations using a large language model (Artificial Intelligence operations include image segmentation deep learning model: [0127]);
a system for performing operations using one or more vision language models (VLMs) (Vision Language Models (VLMs) are advanced AI systems that merge computer vision and language understanding, allowing them to process and connect images/videos with text to perform tasks like describing pictures (image captioning), answering visual questions (VQA), generating images from text, and even understanding documents, essentially giving language models "eyes" to interpret the visual world: [0127, 0132, 0152-0153, 0195]);
a system for performing operations using one or more multi-modal language models (A multimodal language model (MLLM) is an AI system that understands, processes, and generates content across multiple data types (modalities) like text, images, audio, and video, moving beyond traditional LLMs limited to text to offer a more holistic, human-like comprehension by fusing these inputs for richer, context-aware interactions: [0127, 0132, 0152-0153, 0195]);
a system for performing one or more conversational AI operations ([0127, 0132, 0152-0153, 0195]);
a system for generating synthetic data ([0127, 0132, 0152-0153, 0195]);
a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content (Han discloses that their invention can be used for computer games: [0239]);
a system incorporating one or more virtual machines (VMs) (Han discloses that their invention can be used for computer games: [0239]);
a system implemented at least partially in a data center (cloud-based service: [0059]); or
a system implemented at least partially using cloud computing resources (cloud-based service: [0059]).
Regarding claim 19, Han discloses the machine according to claim 18, and further on Han also discloses:
a human-machine interface (HMI) ([0056]: “user interface”), and the path is selected based at least on one or more user inputs to the HMI ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]).
Regarding claim 20, Han discloses the machine according to claim 18, and further on Han also discloses:
wherein the map data corresponds to a navigation map or a standard definition (SD) map, and does not include lane information corresponding to the one or more lanes ([0077, 0081, 0105, 0115, 0117, 0120, 0127, 0132, 0152, 0153, 0195]).
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 Ruben Picon-Feliciano whose telephone number is (571)-272-4938. The examiner can normally be reached on Monday-Thursday within 11:30 am-7:30 pm ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lindsay M. Low can be reached on (571)272-1196. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RUBEN PICON-FELICIANO/Examiner, Art Unit 3747
/GRANT MOUBRY/Primary Examiner, Art Unit 3747
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