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 § 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-9 are directed to a method, claim 10-18 are directed to system, and claims 19-20 are directed to one or more non-transitory computer-readable storage media storing instructions. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 1:
A method performed by one or more data processing apparatus, the method comprising:
obtaining a sequence of multiple point cloud frames, wherein each point cloud frame includes a respective plurality of laser sensor data points, and wherein the sequence of multiple point cloud frames comprise a target point cloud frame associated with a target timestamp and a plurality of other point cloud frames associated with other timestamps;
processing one or more other point cloud frames to generate one or more respective predicted locations at the target timestamp for each of one or more objects detected in the one or more other point cloud frames;
generating a synthetic point cloud frame that is associated with the target timestamp and that includes (i) a plurality of synthetic data points that represent the one or more respective predicted locations at the target timestamp for each of the one or more objects and (ii) a plurality of laser sensor data points included in the target point cloud frame; and
processing the synthetic point cloud frame to generate one or more outputs that characterize an environment at the target timestamp.
Step 2A Prong 1:
“obtaining a sequence of multiple point cloud frames, wherein each point cloud frame includes a respective plurality of laser sensor data points, and wherein the sequence of multiple point cloud frames comprise a target point cloud frame associated with a target timestamp and a plurality of other point cloud frames associated with other timestamps” is directed to math because each point cloud frame is a set of data points defined in a three-dimensional coordinate system (x,y,z). Mathematics is used to represent the external surface of objects as a discrete collection of these spatial measurements. To relate a target frame to other frames, must perform point cloud registration. This relies to on linear algebra to calculate for finding the optimal rotation matrix and translation vector to align different frames and moving points from a local sensor centric system to a global world coordinate system using matrix multiplication. By associating these frames with timestamps, the data becomes a sequence. Math is used to estimate motion: analyzing geometric changes between timestamps to understand velocity and trajectory.
“processing one or more other point cloud frames to generate one or more respective predicted locations at the target timestamp for each of one or more objects detected in the one or more other point cloud frames” is directed to math because objects in point clouds are represented by 3D coordinates (x,y,z). Predicting their movement involves rigid transformation (rotation and translation matrices) to align frames and update object positions. Predicting a predicted location at a target timestamp requires a motion model, which use derivatives (calculate) to estimate displacement over time.
“generating a synthetic point cloud frame that is associated with the target timestamp and that includes (i) a plurality of synthetic data points that represent the one or more respective predicted locations at the target timestamp for each of the one or more objects and (ii) a plurality of laser sensor data points included in the target point cloud frame” is directed to math because each point in s point cloud is a mathematical representation of a location in space, typically defined by a set of coordinates (x, y, z) within a 3D coordinate system. Calculating predicted location involves kinematic equations or machine learning models to estimate future positions based on velocity, acceleration, and time intervals. Creating a synthetic frame often requires affine transformations such as rotation, scaling, and translation to move objects from their original positions to their predicted locations at the target timestamp.
“processing the synthetic point cloud frame to generate one or more outputs that characterize an environment at the target timestamp” is directed to math because process relies on several mathematical domains to transform raw data points into meaningful environment model. Characterizing an environment involves understanding surface. Mathematics is used to calculate geometric quantities like mean and Gaussian curvatures, surface metrics, and normal vectors to identify shapes like planes or cylinders within the cloud. Determining the environment’s state at a target timestamp often requires point-set registration (aligning different frames).
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind but for the recitation of a generic “sensor, timestamp, and measurement” which is a mere indication of the field of use. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of " obtaining a sequence of multiple point cloud frames, wherein each point cloud frame includes a respective plurality of laser sensor data points, and wherein the sequence of multiple point cloud frames comprise a target point cloud frame associated with a target timestamp and a plurality of other point cloud frames associated with other timestamps;
processing one or more other point cloud frames to generate one or more respective predicted locations at the target timestamp for each of one or more objects detected in the one or more other point cloud frames; generating a synthetic point cloud frame that is associated with the target timestamp and that includes (i) a plurality of synthetic data points that represent the one or more respective predicted locations at the target timestamp for each of the one or more objects and (ii) a plurality of laser sensor data points included in the target point cloud frame; and processing the synthetic point cloud frame to generate one or more outputs that characterize an environment at the target timestamp” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“A method performed by one or more data processing apparatus, the method comprising” recited in the preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“obtaining a sequence of multiple point cloud frames, wherein each point cloud frame includes a respective plurality of laser sensor data points, and wherein the sequence of multiple point cloud frames comprise a target point cloud frame associated with a target timestamp and a plurality of other point cloud frames associated with other timestamps” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“processing one or more other point cloud frames to generate one or more respective predicted locations at the target timestamp for each of one or more objects detected in the one or more other point cloud frames” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“generating a synthetic point cloud frame that is associated with the target timestamp and that includes (i) a plurality of synthetic data points that represent the one or more respective predicted locations at the target timestamp for each of the one or more objects and (ii) a plurality of laser sensor data points included in the target point cloud frame” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“processing the synthetic point cloud frame to generate one or more outputs that characterize an environment at the target timestamp” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
The claim is merely selecting data, manipulating or analyzing the data using math and mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“A method performed by one or more data processing apparatus, the method comprising” recited in the preamble does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“obtaining a sequence of multiple point cloud frames, wherein each point cloud frame includes a respective plurality of laser sensor data points, and wherein the sequence of multiple point cloud frames comprise a target point cloud frame associated with a target timestamp and a plurality of other point cloud frames associated with other timestamps” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“processing one or more other point cloud frames to generate one or more respective predicted locations at the target timestamp for each of one or more objects detected in the one or more other point cloud frames” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“generating a synthetic point cloud frame that is associated with the target timestamp and that includes (i) a plurality of synthetic data points that represent the one or more respective predicted locations at the target timestamp for each of the one or more objects and (ii) a plurality of laser sensor data points included in the target point cloud frame” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“processing the synthetic point cloud frame to generate one or more outputs that characterize an environment at the target timestamp” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
The claim is therefore ineligible under 35 USC 101.
Claim 10 is similar to claim 1 but recites a system comprising: one or more computers; and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations the steps as in claim 1. These additional elements fail to integrate the abstract idea into a practical application. These limitations are recited at a high level of generality and do not add significantly more to the judicial exception. These elements are generic computing devices that perform generic functions. Using generic computer elements to perform an abstract idea does not integrate an abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Moreover, “the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 223; see also FairWarninglP, LLCv. latric SysInc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (citation omitted) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter”).
On the record before us, we are not persuaded that the hardware of claim 10 integrates the abstract idea into a practical application. Nor are we persuaded that the additional elements are anything more than well-understood, routine, and conventional so as to impart subject matter eligibility to claim 10.
Claim 19 cites one or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations the steps as in claim 1. This amounts to nothing more than instructions to implement the abstract idea on a computer, which fails to integrate the abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Additionally, using instructions to implement an abstract idea on a generic computer “is not ‘enough’ to transform an abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 226. Therefore, the rejection of claim 19 for the same reason discussed above with regard to the rejection of claim 1.
Regarding claims 2 and 11, “wherein processing the one or more point cloud frames to generate the one or more respective predicted locations at the target timestamp for each of the one or more objects comprises: performing object detection on a particular point cloud frame associated with a particular timestamp of the other timestamps to detect one or more objects that are depicted in the particular point cloud frame; generating one or more predicted trajectories of each of the one or more objects that each span a time period including the target timestamp; and determining, from the one or more predicted trajectories of each of the one or more objects, one or more respective predicted locations of each of the one or more objects at the target timestamp” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 3 and 12, “wherein the particular timestamp precedes the target timestamp, and wherein generating the one or more predicted trajectories of each of one or more objects comprises: applying an object tracker to generate a past trajectory of each of the one or more objects that ends before the target timestamp; and generating, based on the past trajectory of each of the one or more objects, the one or more predicted trajectories of each of one or more objects that each span the time period including the target timestamp” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 4 and 13, “wherein generating the one or more predicted trajectories comprises generating a plurality of waypoints” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 5 and 14, “wherein generating the one or more predicted trajectories comprises: processing, using a trajectory prediction neural network, an input comprising data derived from the previous trajectory of each of the one or more objects to generate the one or more predicted trajectories in parallel” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 6 and 15, “wherein the target timestamp precedes the particular timestamp, and wherein generating the one or more predicted trajectories of the particular object comprises: generating the one or more predicted trajectories of each of the one or more objects by using a reverse motion forecasting model” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 7 and 16, “wherein each synthetic data point has a plurality of features, the plurality of features comprising one or more of: a type of the object represented by the synthetic data point, a dimension of the object represented by the synthetic data point, an estimated heading of the object represented by the synthetic data point, or a confidence score representing a level of confidence that the object represented by the synthetic data point will locate at the predicted location” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 8 and 17, “wherein the one or more outputs comprise one of: an object detection output, a trajectory prediction output, or a motion state prediction output” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 9 and 18, “providing the one or more outputs that characterize the environment at the target timestamp to a planning system of a vehicle to generate planning decisions that plan a future trajectory of the vehicle” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Hence the claims 1-20 are treated as ineligible subject matter under 35 U.S.C. § 101.
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)(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) 1-3, 5, 7-8, 10-12, 14, 16-17, and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li (WO 2022/016311 A1).
Regarding claims 1, 10, and 19, Li discloses a computer system and method performed by one or more data processing apparatus, the method comprising:
obtaining a sequence of multiple point cloud frames, wherein each point cloud frame includes a respective plurality of laser sensor data points (page 3: The point cloud data may include multiple frames of point cloud data, laser sensor 104), and wherein the sequence of multiple point cloud frames comprise a target point cloud frame associated with a target timestamp and a plurality of other point cloud frames associated with other timestamps (page 7: Since there may be targets in the point cloud data that affect the generation of the point cloud map data, the targets refer to living or non living objects in the environment around the vehicle…. the computer device may determine the trajectory data corresponding to each frame of point cloud data in the point cloud data in the vehicle trajectory data according to the timestamp of the point cloud data);
processing one or more other point cloud frames to generate one or more respective predicted locations at the target timestamp for each of one or more objects detected in the one or more other point cloud frames (page 7: The point cloud data may include multiple frames of point cloud data. Each frame of point cloud data has a corresponding time stamp, so that the time sequence between multiple frames of point cloud data can be determined. Specifically, the computer device may determine the trajectory data corresponding to each frame of point cloud data in the point cloud data in the vehicle trajectory data according to the timestamp of the point cloud data);
generating a synthetic point cloud frame that is associated with the target timestamp and that includes (i) a plurality of synthetic data points that represent the one or more respective predicted locations at the target timestamp for each of the one or more objects (page 7) and (ii) a plurality of laser sensor data points included in the target point cloud frame (page 3); and
processing the synthetic point cloud frame to generate one or more outputs that characterize an environment at the target timestamp (page 7: Since there may be targets in the point cloud data that affect the generation of the point cloud map data, the targets refer to living or non living objects in the environment around the vehicle…. the computer device may determine the trajectory data corresponding to each frame of point cloud data in the point cloud data in the vehicle trajectory data according to the timestamp of the point cloud data).
Regarding claims 2, 11, and 20, Li discloses wherein processing the one or more point cloud frames to generate the one or more respective predicted locations at the target timestamp for each of the one or more objects comprises: performing object detection on a particular point cloud frame associated with a particular timestamp of the other timestamps to detect one or more objects that are depicted in the particular point cloud frame ; generating one or more predicted trajectories of each of the one or more objects that each span a time period including the target timestamp; and determining, from the one or more predicted trajectories of each of the one or more objects, one or more respective predicted locations of each of the one or more objects at the target timestamp (page 7).
Regarding claims 3 and 12, Li discloses wherein the particular timestamp precedes the target timestamp, and wherein generating the one or more predicted trajectories of each of one or more objects comprises: applying an object tracker to generate a past trajectory of each of the one or more objects that ends before the target timestamp; and generating, based on the past trajectory of each of the one or more objects, the one or more predicted trajectories of each of one or more objects that each span the time period including the target timestamp (page 7).
Regarding claims 5 and 14, Li discloses generating the one or more predicted trajectories comprises: processing, using a trajectory prediction neural network, an input comprising data derived from the previous trajectory of each of the one or more objects to generate the one or more predicted trajectories in parallel (pages 14, 16-19).
Regarding claims 7 and 16, Li discloses wherein each synthetic data point has a plurality of features, the plurality of features comprising one or more of: a dimension of the object represented by the synthetic data point (page 7: Point cloud data records objects within the visible range in the form of points, a collection of point data corresponding to multiple points on the surface of the object. Plural can refer to two or more. The point cloud data may be three-dimensional point cloud data, and each frame of point cloud data may include point data corresponding to multiple points).
Regarding claims 8 and 17, Li discloses wherein the one or more outputs comprise one of: an object detection output, a trajectory prediction output, or a motion state prediction output (page 8: The computer equipment inputs the feature matrix into the deep learning model, performs prediction operation on the feature matrix through the deep learning model, and outputs the geometric information and semantic information corresponding to each object in the point cloud map data.).
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) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (WO 2022/016311 A1) in view of Kumar et al. (US 2021/0096568 A1).
Regarding claims 4 and 13, Li fails to disclose generating the one or more predicted trajectories comprises generating a plurality of waypoints.
Kumar et al. teach generating the one or more predicted trajectories comprises generating a plurality of waypoints (paras. [0005]-[0007], [0025], [0029]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to incorporate generating the one or more predicted trajectories comprises generating a plurality of waypoints of Kumar et al. with the computer system and method of Li for the purposes of providing a method and system for dynamically modifying navigation trajectory of an autonomous ground vehicle (AGV) (Kumar et al., abstract).
Claim(s) 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (WO 2022/016311 A1) in view of Zhou et al. (CN 108805075 A).
Regarding claims 6 and 15, Li discloses wherein the target timestamp precedes the particular timestamp ().
Li fail to disclose wherein generating the one or more predicted trajectories of the particular object comprises: generating the one or more predicted trajectories of each of the one or more objects by using a reverse motion forecasting model.
Zhou et al. teach wherein generating the one or more predicted trajectories of the particular object comprises: generating the one or more predicted trajectories of each of the one or more objects by using a reverse motion forecasting model (page 6: obtain reverse forecasted trajectory .... according to the vehicle motion model). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to incorporate wherein generating the one or more predicted trajectories of the particular object comprises: generating the one or more predicted trajectories of each of the one or more objects by using a reverse motion forecasting model of Zhou et al. with the computer system and method of Li for the purposes of providing provide a running track line acquisition method, device and electronic device for solving the problem that the predicted running track accuracy is low in the prior art (Zhou et al., abstract).
Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (WO 2022/016311 A1) in view of Kabkab et al. (US 2023/0377164 A1).
Regarding claims 9 and 18, Li fails to discloses providing the one or more outputs that characterize the environment at the target timestamp to a planning system of a vehicle to generate planning decisions that plan a future trajectory of the vehicle.
Kabkab et al. teach providing the one or more outputs that characterize the environment at the target timestamp to a planning system of a vehicle to generate planning decisions that plan a future trajectory of the vehicle (para. [0038]-[0040]: When the planning system 160 receives the detection outputs 152, the planning system 160 can use the detection outputs 152 to generate planning decisions that plan a future trajectory of the vehicle, i.e., to generate a new planned vehicle path). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to incorporate providing the one or more outputs that characterize the environment at the target timestamp to a planning system of a vehicle to generate planning decisions that plan a future trajectory of the vehicle of Kabkab et al. with the computer system and method of Li for the purposes of providing a computer system and method for generating label data for one or more target objects in an environment (Kabkab et al., abstract).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN H LE/Primary Examiner, Art Unit 2857