DETAILED ACTIONS
This Office Action is in response to the application 18/305,153 filed on 4/21/2023.
Claims 1-3, 5-12 and 14-22 have been examined are presently pending.
Definition of terms that may be used for citation purpose:
page = pg., paragraph = p., column = col., line = ln., for example page 5 = pg.5
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/24/2026 has been entered.
Response to Arguments
Applicant's arguments filed 02/24/2026 have been fully considered but they are not persuasive. Applicant’s argument regarding the 101 Analysis is not persuasive. Applicant asserts that independent claims 1, 9, and 19 are similar to Claim 3 from Example 47 from the July 2024 Subject Matter Eligibility Examples (referred to as "the Guidelines") provided by the Office, which the Office indicates as being eligible. Examiner respectfully disagree that this claimed invention is similar to claim 3 of example 47. In claim 3 provided detailed limitations of training the ANN suing a particular algorithm in a particular manner. Example 47, the analysis showed that the steps (d) through (f) provide improved network security using information from the detection (i.e. the mental process) to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. These steps reflect the improvement described in the background of the specification. In the instance application, the only additional element claimed is receiving sensor data obtained using one or more sensors of a machine, the sensor data representative of points located within an environment. This is not significant enough to integrate with the abstract ideas claimed and be eligible. Receiving sensor data is very well known and insignificant, as presented in the 101 analysis below. Therefore the 101 Subject Matter Eligibly rejection is maintained.
Applicant’s arguments with respect to claims 1-3, 5-12 and 14-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-3, 5-12 and 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: Yes
Independent claim 1 is directed toward a method and claims 9 and 18 are directed toward a system. Therefore, each of the independent claims 1, 9, and 18 along with the corresponding dependent claims are directed to a statutory category of invention under Step 1.
Step 2A, Prong I: Recites a Judicial Exception: Yes
Under Step 2A, Prong 1, the claims are analyzed to determine whether one or more of the claims recites subject matter that falls within one of the following groups of abstract ideas: (1) mental processes, (2) certain methods of organizing human activity, and/or (3) mathematical concepts. In this case, the independent claims 1, 9, and 18 are directed to an abstract idea without significantly more. Specifically, the claims, under their broadest reasonable interpretation cover certain mental processes. The language of independent claim 1 is used below for illustration. Please note that the abstract idea is in bold text.
Claim 1: A method comprising:
receiving sensor data obtained using one or more sensors of a machine, the sensor data representative of points located within an environment;
determining, based at least on the sensor data, that one or more first points of the points are associated with one or more static objects, [[and ]]one or more second points of the points are associated with one or more dynamic objects at a first instance in time, and one or more third points of the points are associated with the one or more dynamic objects at a second instance in time that is subsequent to the first instance in time; (*This limitation is not too complicated to be performed by the human mind. One could mentally determine points when analyzing sensor data. Also regarding claim 9 where “point clouds” are mentioned. It’s the examiner’s reasonable interpretation that one could mentally observe image data in the form of a point cloud and determine object that are static and dynamic in the environment. There is not complicated processing of the cloud data claimed, that couldn’t be practically performed by the mind in an observation. See the image below as an example.)
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generating map data representative of a map that indicates the one or more first points that indicate one or more first locations of the one or more static objects; (*This limitation is not too complicated to be performed by the human mind with the aid of a pen and paper. One could generate points that indicate the location of objects which could be considered as map data. Again, this is not too complicated to be performed in the human mind.)
updating the map data generate a first instance of the map that is associated with the first instance in time, the first instance of the map indicating the one or more second points representing one or more second locations of the one or more dynamic objects at the first instance in time; (*This limitation is not too complicated to be performed by the human mind with the aid of a pen and paper. One could mentally, with the aid of pen and paper, update the map data to represent both static and dynamic points points. Again, this is not too complicated to be performed in the human mind)
updating the map data to generate a second instance of the map that is associated with the second instance in time, the second instance of the map indicating the one or more third points representing one or more third locations of the one or more dynamic objects at the second instance in time; and (*This limitation is not too complicated to be performed by the human mind with the aid of a pen and paper. One could mentally with the aid of pen and paper, generate an update to the map data that is associated with any time period indicating motion of a dynamic object.)
generating one or more first annotations associated with the one or more dynamic objects as represented by one or more first images associated with the first instance in time using the first instance of the map and one or more second annotations associated with the one or more dynamic as represented by one or more second images associated with the second time instance using the second instance of the map. (*This limitation is not too complicated to be performed by the human mind with the aid of a pen and paper. One could mentally with the aid of pen and paper, generate associated annotations or labels for the objects in the environment.)
Therefore, as detailed in this analysis, the claimed invention does recite an abstract idea under Step 2A Prong I.
Step 2A, Prong II: Practical Application: No
Under Step 2A, Prong II, the claims are analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”; see at least MPEP 2106.04(d).
In this case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions represent the “abstract idea”):
The additional element of:
receiving sensor data obtained using one or more sensors of a machine, the sensor data representative of points located within an environment;
The receiving steps from the sensors and from the external source is recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Accordingly, even in combination, this additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore the claim is directed toward the abstract idea.
Step 2B: Is there an inventive concept: No
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than insignificant extra-solution activity.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B.
Here, the receiving step was considered to be extra-solution activity in Step 2A, and thus is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The specification recites conventional vehicle sensors and does not provide any indication that the processors receiving sensor data is anything other than a conventional computer within the vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer. The claim is ineligible.
The Analysis is the same for all independent claims. 1, 9 and 18.
Dependent claims 2, 3, 5-8, 10-12, 14-17 and 19-22 when analyzed both individually and in combination, are also patent ineligible under 35 U.S.C. § 101 based on same analysis as above. The additional limitations recited in the dependent claims fail to establish that the dependent claims are not directed to an abstract idea. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. Accordingly, claims 2, 3, 5-8, 10-12, 14-17 and 19-22 are patent ineligible. Therefore, claims 1-3, 5-12 and 14-22 are patent ineligible under 35 U.S.C. §101.
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)(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 1-3, 5-12 and 14-22 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Yang et al. US 12,116,015.
Regarding Claim 1. (Currently Amended) Yang teaches method comprising:
receiving sensor data obtained using one or more sensors of a machine, the sensor data representative of points located within an environment; (see at least col.8 ln.55, robotic platform 102 can include one or more sensor(s) 106, 108. The one or more sensors 106, 108 can be configured to generate or store data descriptive of the environment, The sensors 106, 108 can be configured to obtain object data (e.g., point cloud data))
determining, based at least on the sensor data, that one or more first points of the points are associated with one or more static objects, one or more second points of the points are associated with one or more dynamic objects (see at least col.8 ln.55, The one or more sensors 106, 108 can be configured to generate or store data descriptive of the environment 104 (e.g., one or more static or dynamic objects therein)) at a first instance in time, and one or more third points of the points are associated with the one or more dynamic objects at a second instance in time that is subsequent to the first instance in time; (see at least col.8 ln.45, The first object 112 can have a motion trajectory over a period of time that corresponds to a motion path of the first object 112. Similarly, the second object 114 can have a motion trajectory over a period of time that corresponds to a motion path of the second object 114. Additionally, in some instances, the first object 112 or the second object 114 can also be a static object, such as parked car or a stationary object)
generating map data representative of a map that indicates the one or more first points that indicate one or more first locations of the one or more static objects; (see at least col.13 ln.35, The object(s) can be static objects (e.g., not in motion) or dynamic objects/actors (e.g., in motion or likely to be in motion) in the vehicle's environment and col. ln., the initial object pose includes a center position of the object at the timestamp and an orientation of the object at the timestamp. In some implementations, determining the fixed value for the object size includes converting the sensor data into an object coordinate system by aligning the center position of the object and the orientation of the object over multiple timestamps in the plurality of initial object observations)
updating the map data to represent generate a first instance of the map that is associated with the first instance in time, the first instance of the map indicating the one or more second points that indicate representing one or more second locations of the one or more dynamic objects at the first instance in time;(see at least col.4 ln.30, determining the refined object trajectory includes providing the plurality of updated initial object observations to a path encoder and a path decoder. Additionally, the path encoder can extract spatial-temporal features from four-dimensional point clouds that are generated from the sensor data) updating the map data to generate a second instance of the map that is associated with the second instance in time, the second instance of the map indicating the one or more third points representing one or more third locations of the one or more dynamic objects at the second instance in time; (see at least col.5 ln.1, the initial object trajectory including the plurality of initial object observations to generate an updated initial object trajectory including a plurality of updated initial object observations.) and generating one or more first annotations associated with the one or more dynamic objects as represented by one or more first images associated with the first instance in time using the first instance of the map and one or more second annotations associated with the one or more dynamic as represented by one or more second images associated with the second time instance using the second instance of the map. (see at least col.6 ln.45, The annotation techniques automate the 4D labeling of objects by determining the object size in 3D that is fixed through time for objects and determining the object's pose through time to generate the motion path of the object.)
Regarding Claim 2. (Currently Amended) Yang teaches all the limitations of the method of claim 1. Yang teaches further, wherein the generating the one or more first annotations comprises: the one or more second locations of the one or more dynamic objects are associated with one or more first instances in time; and the method further comprises updating the map data to represent one or more third points that indicate one or more third locations of the one or more dynamic objects, the one or more third locations of the one or more dynamic objects being associated with one or more second instances in time projecting, using the first instance of the map, the one or more second points from the first instance of the map onto the one or more first images, the one or more first images represented by image data obtained using one or more images sensors of the machine; and generating the one or more first annotations based at least on the one or more second points as projected. (see at least col.23 ln.65, Once the new size estimate for the object size 286B is determined, the annotation system 400 updates all detections over the trajectory to leverage the constant size constraint of objects. For example, the annotation system 400 generates an updated initial object trajectory 435 by updating the width and length of the bounding boxes while retaining the original object center and orientation in the center-aligned orientation implementation.)
Regarding Claim 3. (Currently Amended) Yang teaches all the limitations of the method of claim 1. Yang teaches, further comprising: determining, based at least on the one or more second points, one or more tracks associated with the one or more dynamic objects, the one or more tracks being associated with the one or more dynamic objects moving from the one or more second locations to the one or more third locations, wherein at least one of the updating the map data to represent the one or more second points generate the first instance of the map or the updating the map data to represent the one or more third points generate the second instance of the map is based at least on the one or more tracks associated with the one or more dynamic objects. (see at least col.23 ln.65, Once the new size estimate for the object size 286B is determined, the annotation system 400 updates all detections over the trajectory to leverage the constant size constraint of objects. For example, the annotation system 400 generates an updated initial object trajectory 435 by updating the width and length of the bounding boxes while retaining the original object center and orientation in the center-aligned orientation implementation.)
Regarding Claim 6. (Previously Presented) Yang teaches all the limitations of the method of claim 1. Yang teaches, further comprising: determining at least one of one or more first classifications associated with the one or more static objects or one or more second classifications associated with the one or more dynamic objects, wherein the determining that the one or more first points are associated with the one or more static objects and the one or more second points are associated with the one or more dynamic objects is based at least on the one or more first classifications or the one or more second classifications. (see at least col.26 ln.30, the annotation system 280 applies the motion path determinator 282C to the entire trajectory in a sliding window fashion. The annotation system 280 can be applicable to both static and moving objects. To avoid small motions for static objects, the annotation system 280 can add a classification output to the pre-trained object detector to determine whether the object is a static object or a moving object.)
Regarding Claim 7. (Original) Yang teaches all the limitations of the method of claim 6. Yang teaches further, wherein the determining the at least one of the one or more first classifications associated with the one or more static objects or the one or more second classifications associated with the one or more dynamic objects includes at least one of: processing the sensor data using one or more machine learning models; or receiving input data representative of the at least one of the one or more first classifications or the one or more second classifications. (see at least col.26 ln.30, the annotation system 280 applies the motion path determinator 282C to the entire trajectory in a sliding window fashion. The annotation system 280 can be applicable to both static and moving objects. To avoid small motions for static objects, the annotation system 280 can add a classification output to the pre-trained object detector to determine whether the object is a static object or a moving object.)
Regarding Claim 8. (Currently Amended) Yang teaches all the limitations of the method of claim 1. Yang teaches further comprising: determining, based at least on the one or more second points, one or more three- dimensional (3D) shapes associated with the one or more dynamic objects, wherein the updating the map data to generate the first instance of the map is further based at least on the one or more 3D shapes associated with the one or more dynamic objects. (see at least col.1 ln.40, determining the object size in 3D that is fixed through time for objects, and (2) determining the object's pose through time to generate a refined trajectory (e.g., motion path) of the object. Instead of generating a series of labels in one iteration as done in conventional systems, the disclosed annotation system adopts an iterative refinement process where object detections are tracked and updated through time)
Regarding Claim 9 and 18. (Currently Amended) Yang discloses system comprising: one or more processors (see at least col.5 ln.20, executed by the one or more processors cause the computing system to perform operations) to: determine, based at least on sensor data representative of a point cloud (see at least col.1 ln.35, the sensor data can include Light Detection and Ranging (LiDAR) point cloud data.), that one or more first points from the point cloud are associated with one or more static objects (see at least col.8 ln.55, The one or more sensors 106, 108 can be configured to generate or store data descriptive of the environment 104 (e.g., one or more static or dynamic objects therein); generate map data representative of the one or more first points that indicate one or more first locations of the one or more static objects (see at least col.4 ln.10, determining the fixed value for the object size includes converting the sensor data into an object coordinate system by aligning the center position of the object and the orientation of the object over multiple timestamps in the plurality of initial object observations); determine, based at least on the sensor data, that one or more second points from the point cloud are associated with one or more dynamic objects at one or more instances a first instance in time and one or more third points from the point cloud are associated with the one or more dynamic objects at a second instance in time that is subsequent to the first instance in time; (see at least col.4 ln.30, by converting the sensor data into a world coordinate system, a motion of the object can be determined independent of a movement associated with the AV) update the map data to represent the one or more second points that indicate one or more second locations of the one or more dynamic objects at the one or more instances first instance in time; update the map data to represent the one or more third points that indicate one or more third locations of the one or more dynamic objects at the second instance in time; (see at least col.4 ln.30, determining the refined object trajectory includes providing the plurality of updated initial object observations to a path encoder and a path decoder. Additionally, the path encoder can extract spatial-temporal features from four-dimensional point clouds that are generated from the sensor data.) and perform one or more operations with respect to at least one of the one or more static objects or the one or more dynamic objects using the map data as updated (see at least col.18 ln.40, the annotation system 280 can identify one or more objects that are within the surrounding environment of the vehicle 212 based at least in part on the sensor data 255. The initial object trajectory 285A can be determined based on the obtained sensor data 255 or the map data 260. The objects perceived within the surrounding environment can be those within the field of view of the sensor(s) 235 or predicted to be occluded from the sensor(s) 235. This can include object(s) not in motion or not predicted to move (static objects) or object(s) in motion or predicted to be in motion (dynamic objects/actors)) *Examiner interprets annotating is an example of an operation performed. update one or more parameters associated with one or more machine learning models during training based on at least one of the one or more second points represented by the map data or the one or more third points represented by the map data. (see at least col.19 ln.25, annotation system 280 can utilize one or more algorithms or machine-learned model(s), such as the object size reasoning model 285B and the trajectory refinement model 285C, that are configured to identify the size and motion path of an object based at least in part on the sensor data)
Regarding Claim 10. (Currently Amended) Yang discloses all the limitations of the system of claim 9. Yang discloses further, wherein the map data, as updated, represents at least: that the one or more dynamic objects were located at one or more third locations, from the one or more second locations, at a first instance in time of the one or more instances in time; and that the one or more dynamic objects were located at one or more fourth locations, from the one or more second locations, at a second instance in time of the one or more instances in time the determination that the one or more second points are associated with the one or more dynamic objects at the first instance in time is based at least on the one or more second points being associated with a first spin of a sensor that obtained the sensor data; and the determination that the one or more third points are associated with the one or more dynamic objects at the second instance in time is based at least on the one or more third points being associated with a second spin of the sensor. (see at least col.18 ln.60, The timestamp can be associated with a specific timestamp corresponding to when the sensor data 255 was obtained by the sensor(s) 235. As previously mentioned, the object size for each initial object observation can be different, because it is generated from the sensor data 225. The pose for each initial object observation can include the center location of the object or a corner location of the object at the specific timestamp.) *Examiner interprets the timestamps for the sensor data (i.e point cloud) will inherently include various instances
Regarding Claim 11. (Currently Amended) Yang discloses all the limitations of the system of claim 9. Yang discloses, further comprising wherein the one or more processors are further to: determining, based at least on the one or more second points, the one or more third locations of the one or more dynamic objects at the first instance in time; determining, based at least on the one or more second points, the one or more fourth locations of the one or more dynamic objects at the second instance in time; and determining one or more tracks associated with the one or more dynamic objects, the one or more tracks indicating that the one or more dynamic objects moved from the one or more third second locations to the one or more fourth third locations, wherein the map data is updated to represent the one or more third points based at least on the one or more tracks. (see at least col.22 ln.5, The annotation system 400 first obtains initial object trajectory 285A by applying a pre-trained 3D object detector followed with a discrete tracker (e.g., object detector and tracker 282A)
Regarding Claim 12. (Currently Amended) Yang discloses all the limitations of the system of claim 9. Yang discloses further wherein the one or more processors are further to: determine, based at least on the sensor data, that a first portion of the sensor data is associated with the one or more static objects and a second portion of the sensor data is associated with the one or more dynamic objects, wherein: the determination of the one or more first points associated with the one or more static objects is based at least on the first portion of the sensor data; and the determination of the one or more second points associated with the one or more dynamic objects at the first instance in time and the one or more third points associated with the one or more dynamic objects at the second instance in time is based at least on the second portion of the sensor data. (see at least col.20 ln.20, The training data can include sequential multi-modal sensor data indicative of a plurality of environments at different interval of time. In some implementations, the training data can include sensor data 255, initial object trajectory 285A, object size 286B (e.g., bounding box), refined object trajectory 286C, motion plan 285D, object data, traffic element data, and implementation plan associated with a trajectory of an object.)
Regarding Claim 14. (Previously Presented) Yang discloses all the limitations of the system of claim 9. Yang discloses further wherein the one or more processors are further to: determine one or more first classifications associated with the one or more static objects and one or more second classifications associated with the one or more dynamic objects; and cause the one or more first points to be annotated using the one or more first classifications and the one or more second points to be annotated using the one or more second classifications. (see at least col.26 ln.30, the annotation system 280 applies the motion path determinator 282C to the entire trajectory in a sliding window fashion. The annotation system 280 can be applicable to both static and moving objects. To avoid small motions for static objects, the annotation system 280 can add a classification output to the pre-trained object detector to determine whether the object is a static object or a moving object.)
Regarding Claim 15. (Previously Presented) Yang discloses all the limitations of the system of claim 14. Yang discloses further wherein the determination of the one or more first classifications associated with the one or more static objects or the one and more second classifications associated with the one or more dynamic objects includes at least one of: processing the sensor data using one or more machine learning models; or receiving input data representative of at least one of the one or more first classifications or the one or more second classifications. (see at least col.26 ln.30, the annotation system 280 applies the motion path determinator 282C to the entire trajectory in a sliding window fashion. The annotation system 280 can be applicable to both static and moving objects. To avoid small motions for static objects, the annotation system 280 can add a classification output to the pre-trained object detector to determine whether the object is a static object or a moving object.)
Regarding Claim 16. (Currently Amended) Yang discloses all the limitations of the system of claim 9. Yang discloses further wherein the one or more processors are further to: determine, based at least on the sensor data, one or more three-dimensional (3D) shapes associated with the one or more dynamic objects; and wherein update the map data is further updated based at least on to indicate the one or more 3D shapes associated with the one or more dynamic objects. (see at least col.1 ln.35, determining the object size in 3D that is fixed through time for objects, and (2) determining the object's pose through time to generate a refined trajectory (e.g., motion path) of the object. Instead of generating a series of labels in one iteration as done in conventional systems, the disclosed annotation system adopts an iterative refinement process where object detections are tracked and updated through time)
Regarding Claim 17. (Original) Yang discloses all the limitations of the system of claim 9. Yang discloses further, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational Al operations; a system implementing one or more large language models (LLMs);a system for generating synthetic data; a system incorporating one or more virtual machines (VMs);a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. (see at least col.4 ln.50, aspects of the present disclosure describe an autonomous vehicle (AV) control system of an AV.)
Regarding Claim 19. (Currently Amended) Yang discloses all the limitations of the one or more processors of claim 18. Yang discloses further, wherein: the one or more second locations of the one or more dynamic objects are associated with one or more first instances in time, and wherein the map data further represents one or more third locations of the one or more dynamic objects (see at least col.3 Ln.35, The sensor data being sequential over a period of time. The method further includes generating an initial object trajectory for an object, using the sensor data.), the one or more third locations associated with one or more second instances in time the update to the map data generates a first instance of a map associated with the first instance in time, the first instance of the map representing the one or more first locations associated with the one or more static objects and the one or more second locations associated with the one or more dynamic objects; (see at least col.3 ln.45, the method includes determining, based on the sensor data and the updated initial object trajectory, a refined object trajectory including a plurality of refined object observations respectively including an updated object pose of the object for the plurality of refined object observations) and the further update to the map data generates a second instance of the map associated with the second instance in time, the second instance of the map representing the one or more first locations associated with the one or more static objects and the one or more third locations associated with the one or more dynamic objects. (see at least col.5 ln.5, including a plurality of updated initial object observations.)
Regarding Claim 20. (Previously Presented) Yang discloses all the limitations of the one or more processors of claim 18. Yang discloses further, wherein the one or more processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational Al operations; a system implementing one or more large language models (LLMs);a system for generating synthetic data; a system incorporating one or more virtual machines (VMs);a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. (see at least col.4 ln.50, aspects of the present disclosure describe an autonomous vehicle (AV) control system of an AV.)
Regarding Claim 21. (Previously Presented) Yang teaches all the limitations of the method of claim 1. Yang teaches further, wherein the determining the one or more first points that are associated with the one or more static objects and the one or more second points that are associated with the one or more dynamic objects comprises: determining that the one or more first points are associated with the one or more static objects that include no motion over a period of time; and determining that the one or more second points are associated with the one or more dynamic objects that include motion over the period of time. (see at least col.3 ln.35, The sensor data being sequential over a period of time. The method further includes generating an initial object trajectory for an object, using the sensor data. The initial object trajectory includes a plurality of initial object observations respectively associated)
Regarding Claim 22. (Currently Amended) Yang discloses all the limitations of the one or more processors of claim 18. Yang discloses further wherein the one or more operations include at least determining that at least a dynamic object of the one or more dynamic objects at least partially occludes a static object of the one or more static objects processing circuitry is further to: project at least one of the one or more second locations associated with the one or more dynamic objects or the one or more third locations associated with the one or more dynamic objects onto one or more images; (see at least col.12 ln.55, at least one first sensor can include one or more image capturing device(s) (e.g., one or more cameras, RGB cameras, etc.). In addition, or alternatively, the at least one second sensor can include one or more depth capturing device(s) (e.g., LiDAR sensor, etc.). The at least two different types of sensor(s) can obtain object data, traffic element data, or multi-modal sensor data indicative of one or more static or dynamic objects within an environment of the vehicle) and train one or more machine learning models using the one or more images as ground (see at least col.26 ln.15, the annotation system 280 trains the object size determinator 282B and the motion path determinator 282C sequentially)
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 5 is rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. US 12,116,015; in view of Kubiak US 2019/0226853.
Regarding Claim 5. (Currently Amended) The combination of Yang and Kubiak teaches all the limitations of the method of claim 1. Kubiak teaches further comprising: updating, based at least on removing the one or more second points and the one or more third points from a point cloud that includes the points, the point cloud to include the one or more first points without the one or more second point and the one or more third points, wherein the generating the map data representing the one or more first points that indicate the one or more first locations of the one or more static objects representing the map is based at least on the point cloud as updated. (see at least p.207, analysing one or more of the groups of data points, i.e. the pre-classified portions of the point cloud, to recognise one or more candidate objects. Additionally, or alternatively, the method may further comprise removing points from the point cloud that are in the road corridor, and using this “cleaner” point cloud as part of the real time scan data when performing the next positioning iteration). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the automatic annotation of object trajectories as taught by Yang with the method of removing data points from a point cloud as taught by Kubiak to improve position determination. (see p.207)
Conclusion
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JELANI A. SMITH
Supervisory Patent Examiner
Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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