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 .
This action is made final.
Claims 1-3, 6-16 and 19-24 are pending. Claims 1, 10 and 14 are independent claims.
Specification
The amendment to the specification is accepted.
Response to Arguments
Applicant’s arguments, dated 4/6/2026, regarding the 35 U.S.C. 101 rejections of the previous office action have been fully considered but are unpersuasive. Due to the amendments, the scope of the claims has changed and new grounds of rejection have been applied – see the updated 101 rejection below.
Applicant argues that claims 1, 10 and 14 cannot be performed entirely in the human mind and therefore do not fall within the “mental process” grouping. Examiner argues that Step 2A prong 1 involves determining if any abstract idea limitations (i.e., mental process, mathematical calculation, etc.) are present within the claim, instead of determining if the entire claim is a mental process/mathematical calculation/other judicial exception category. See, e.g., MPEP 2106.04(a)(2)(III), Section C, example 2, which describes a mental process limitation performed in a computer environment.
Applicant argues that the claims recite a technical improvement. Examiner argues that the recited judicial exception alone cannot provide an improvement (MPEP 2106.05(a), paragraph 6), and that the improvement must be provided by the additional element(s) considered alone or in combination with the recited judicial exception. Performing a broadly recited “determination” of a clean trajectory from one or more noisy trajectories (described in the below rejection as a mental process) by a general trained model, without providing details on how the determination is performed, or how the model operates is an attempt to apply the judicial exception (see MPEP 2106.05(f)). Applicant is encouraged to specify determination details and/or model implementation details so the claims recite a technological improvement.
Applicant’s arguments, dated 4/6/2026, regarding the 35 U.S.C. 103 rejections of the previous office action have been fully considered but are unpersuasive. However, due to the amendments, the scope of the claims has changed and new grounds of rejection have been applied – see the updated 103 rejection below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 24 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 24 recites: to determine the clean trajectory without modifying, using the one or more values for the one or more criteria, any one or more intermediate noisy trajectories on a denoising schedule from the one or more instances of the noisy trajectory to the clean trajectory. Claim 24 is interpreted as denoising schedule that determines a clean trajectory without modifying any one or more intermediate noisy trajectories. The specification does not appear to describe an embodiment of the invention that follows a denoising schedule but does not alter a noisy trajectory.
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, 6-16, and 19-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. Claim 1 recites: A processor comprising one more circuits to… Claim 1 is directed to an apparatus (Step 1: YES).
Step 2A prong 1: Does the claim recite a judicial exception? Claim 1 recites: identify one or more values for one or more criteria corresponding to movement of a subject in an environment; (identifying criteria for movement of a subject in an environment is a mental process, i.e., identifying obstacles, identifying time constraints); determine, based at least on… one or more instances of a noisy trajectory, a clean trajectory of the subject (determining a trajectory of a subject based on noisy trajectories is a mental process)… modify the clean trajectory according to the one or more values of the one or more criteria to generate a modified trajectory (modifying a trajectory based on some criteria is a mental process)... These steps can be performed mentally or are mathematical calculations (Step 2A prong 1: YES).
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, considered individually and in combination, integrate the judicial exception into a practical application? Claim 1 recites: a denoising network processing... the denoising trained using training data representing subject trajectories… and at least one of: (i) update a representation of movement of the subject in the environment according to the modified trajectory; or (ii) present, using a display, the modified trajectory of the subject in the environment. Using a denoising network and training the denoising process with trajectory training data are an attempt to use the network or model by merely applying the abstract idea (i.e., perform the math) without placing any limits on how the network model operates. Further, the claim omits any details as to how the network solves a technical problem and instead recites only the idea of a solution or outcome. See MPEP 2106.05(f). Thus, the limitation represents no more than mere instructions to implement the abstract idea which is equivalent to adding the words “apply it” to the recited judicial exception. Updating a representation of the trajectory of the subject or presenting the trajectory are insignificant extra-solution activity of data outputting that does not add a meaningful limitation to the trajectory determining apparatus (Step 2A prong 2: NO).
Step 2B: These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since they provide nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)) or only amount to data outputting without significantly more (MPEP 2106.05(g)). These limitations, taken either alone or in combination, fail to provide an inventive concept (Step 2B: NO). Thus, the claim is not patent eligible.
Regarding claims 2, 3, 6-9 and 21-24, they recite limitations which further narrow the abstract idea by specifying more details of the mental and mathematical process that occurs (Claim 2, describing that the trajectory is determined by subject position, motion of one or more second subjects, and a map representing environmental features is insignificant extra-solution activity of selecting particular types of data to be manipulated (see, e.g., Electric Power Group, LLC v. Alstom S.A.), and the denoising network is still an attempt to apply the abstract idea on a generic computer; Claim 3, describing that the criteria is collision avoidance and/or maintaining a distance from other subjects still results in the trajectory determination being a mental process; Claim 6, describing two types of input training data is insignificant extra-solution activity of selecting particular types of data to be manipulated (see, e.g., Electric Power Group, LLC v. Alstom S.A.); Claim 7, using the network to determine a clean trajectory is still mere instructions to implement an abstract idea on a generic computer, the trajectory being comprised of multiple locations, i.e., waypoints, does not impact the fact that it is still a mental process, and identifying environmental features at the multiple locations is a mental process; Claim 8, using circuits to operate an autonomous vehicle is mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)); Claim 9, describing that the processor is included in various systems is an additional element specifying a field of use without significantly more; Claim 21, predicting a clean trajectory from one or more noisy trajectories is a mental process, and stating that the denoising is performed by a denoising network is still an attempt to implement the abstract idea on a generic computer; Claim 22, determining a modified trajectory based on a past trajectory and a feature of a noisy trajectory is a mental process; Claim 23, determining one or more features from a map and determining a trajectory based on the features is a mental process, and using a “featurizer network“ is mere instructions to implement the abstract idea; Claim 24, determining a clean trajectory based on a noisy trajectory or multiple noisy trajectories is a mental process).
Regarding claim 10:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. Claim 10 recites: A processor comprising: one or more circuits to… Claim 10 is directed to an apparatus (Step 1: YES).
Step 2A prong 1: Does the claim recite a judicial exception? Claim 10 recites: determine… based at least on applying noise to a trajectory of a subject of a training data instance, a noisy trajectory (applying noise to a trajectory is a mathematical calculation, i.e., adding Gaussian noise to coordinate information); predict, based at least on… processing the noisy trajectory, a clean trajectory of the subject… (determining a clean trajectory of a subject based on a noisy trajectory is a mental process). These steps can be performed mentally or are mathematical calculations (Step 2A prong 1: YES).
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, considered individually and in combination, integrate the judicial exception into a practical application? Claim 10 recites: using a neural network and... the neural network… and update one or more parameters of the neural network according to the trajectory and the clean trajectory. Using and updating a neural network using trajectory information is an attempt to use the neural network model by merely applying the abstract idea (i.e., perform the math) without placing any limits on how the neural network model operates. Further, the claim omits any details as to how the neural network model solves a technical problem and instead recites only the idea of a solution or outcome. See MPEP 2106.05(f). Thus, the limitation represents no more than mere instructions to implement the abstract idea which is equivalent to adding the words “apply it” to the recited judicial exception (Step 2A prong 2: NO).
Step 2B: These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since they provide nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)). These limitations, taken either alone or in combination, fail to provide an inventive concept (Step 2B: NO). Thus, the claim is not patent eligible.
Regarding claims 11-13, they recite limitations which further narrow the abstract idea by specifying more details of the mental and mathematical process that occurs (Claim 11, comparing trajectories is a mental process or mathematical calculation, and updating a neural network based on the comparison is again an attempt to use the neural network model by merely applying the abstract idea (i.e., perform the math) without placing any limits on how the neural network model operates. Further, the claim omits any details as to how the neural network model solves a technical problem and instead recites only the idea of a solution or outcome, similar to adding the words “apply it” (MPEP 2106.05(f)) to the recited judicial exception; Claim 12, describing two types of input training data is insignificant extra-solution activity of selecting particular types of data to be manipulated (see, e.g., Electric Power Group, LLC v. Alstom S.A.); Claim 13, describing that the processor is included in various systems is an additional element specifying a field of use without significantly more).
Regarding claim 14, it is a method that recites similar limitations to the apparatus of claim 1 and is rejected on the same grounds – see above.
Regarding claims 15, 16, 19 and 20, they recite limitations which further narrow the abstract idea by specifying more details of the mental and mathematical process that occurs (Claim 15, determining a modified trajectory by taking into consideration subject position, motion of one or more second subjects, and a map representing environmental features is a mental process, and the denoising network is still an attempt to apply the abstract idea on a generic computer; Claim 16, describing that the criteria is collision avoidance and/or maintaining a distance from other subjects still results in the trajectory determination being a mental process; Claim 19, describing two types of input training data is insignificant extra-solution activity of selecting particular types of data to be manipulated (see, e.g., Electric Power Group, LLC v. Alstom S.A.); Claim 20, using the network to determine a modified trajectory is still mere instructions to implement an abstract idea on a generic computer – that the trajectory is comprised of multiple locations, i.e., waypoints, does not impact the fact that the trajectory determination is still a mental process, and identifying environmental features at the multiple locations is a mental process).
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) 1, 2, 3, 7, 8, 9, 14, 15, 16, 20, 21, 22 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (US 20200387156 A1), herein Xu, in view of Janner et al. (“Planning with Diffusion for Flexible Behavior Synthesis”, 2022), herein Janner.
Regarding claim 1, Xu teaches: A processor comprising: one more circuits to: identify one or more values for one or more criteria corresponding to movement of a subject in an environment (¶28, the autonomous driving system 14 can receive the sensed data and information from the vehicle sensors 12, access other data, such as map data, about the environment and location of the vehicle 10, and determine a current trajectory to be driven by the vehicle 10 based on the sensed data and information)… determine…. a clean trajectory of the subject… using training data representing subject trajectories; modify the clean trajectory according to the one or more values of the one or more criteria to generate a modified trajectory (¶28, The autonomous driving system 14 iteratively and continually receives the sensed data and information from the vehicle sensors 12 and iteratively and continually updates the current trajectory and/or determines a new trajectory for the vehicle 10); and at least one of: (i) update a representation of movement of the subject in the environment according to the modified trajectory; (¶28, The autonomous driving system 14 iteratively and continually receives the sensed data and information from the vehicle sensors 12 and iteratively and continually updates the current trajectory and/or determines a new trajectory for the vehicle 10) or (ii) present, using a display, the modified trajectory of the subject in the environment.
Xu fails to teach: based at least on a denoising network processing one or more instances of a noisy trajectory… the denoising trained…
However, in the same field of endeavor, Janner teaches: based at least on a denoising network processing one or more instances of a noisy trajectory (pg. 3, Section 3, ¶4, In this section, we describe Diffuser, a diffusion model designed for learned trajectory optimization – see pg. 6, fig. 4 for denoising of sampled candidate trajectories)… the denoising trained (pg. 4, section 3.2, We first train a diffusion model pθ(τ) on the states and actions of all available trajectory data)…
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use denoising diffusion models to determine trajectories as disclosed by Janner in the processor disclosed by Xu to predict trajectories effectively (pg. 2, Section 1, we demonstrate that Diffuser has a number of useful properties and is particularly effective in control settings that require long-horizon reasoning and test-time flexibility).
Regarding claim 2, Xu further teaches: The processor of claim 1, wherein the subject is a first subject, and the one or more circuits are used to determine, using the denoising network, the clean trajectory further according to a position of the subject, motion of one or more second subjects, and a map representing features of the environment (¶35, For example, the prediction module 34 may receive information from the perception module 36 indicating that an object, such as another vehicle, a pedestrian, or an animal, exists laterally to the side of the vehicle 10. Based on information from the perception module 36 over time and localization information 44 from the localization module 30 over time, the prediction module 34 may determine a current trajectory of the object – and – ¶37, The motion planning module 40 receives the localization information 44… the obstacle information 52… and determines an optimal current trajectory for the vehicle 10 to follow).
Regarding claim 3, Xu further teaches: The processor of claim 1, wherein the subject is a first subject, and the one or more criteria correspond to at least one of collision avoidance or distance to maintain with respect to one or more second subjects (¶78, For example, at 1110, the teaching module 70 can control the steering system 18, the braking system 22, and/or the throttle system 20 to avoid a collision).
Regarding claim 7, Xu further teaches: The processor of claim 1, wherein the trajectory comprises a plurality of locations (¶31, In other words, the trajectory can consist of a sequence of waypoints for the vehicle to follow or traverse over the predetermined time period), and the one or more circuits are used to determine, using the denoising network, the clean trajectory further by identifying one or more features of the environment at the plurality of locations (¶27, The vehicle sensors 12 can also include sensors to determine a light level of the environment of the vehicle 10, e.g., whether it is daytime or nighttime, to determine or receive weather data, e.g., whether it is a sunny day, raining, cloudy, etc., to determine the current temperature, to determine the road surface status, e.g., dry, wet, frozen, number of lanes, types of lane marks, concrete surface, asphalt surface, etc., to determine traffic conditions for the current path or route of the vehicle 10, and/or other applicable environmental information).
Regarding claim 8, Xu further teaches: The processor of claim 1, wherein the one or more circuits are used to operate a controller of an autonomous vehicle in the environment, according to the trajectory of the subject (¶29, The autonomous driving system 14 controls the vehicle actuation systems 16 to operate and drive the vehicle 10 to follow the determined current trajectory).
Regarding claim 9, Xu further teaches: The processor of claim 1, wherein the processor 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 for performing generative Al operations using a large language model (LLM); 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 (¶29, The autonomous driving system 14 controls the vehicle actuation systems 16 to operate and drive the vehicle 10 to follow the determined current trajectory – Xu teaches the processor being in a control system for an autonomous or semiautonomous machine).
Regarding claim 14, it is a method that recites similar limitations to claim 1 and is rejected on the same grounds – see above.
Regarding claim 15, Xu further teaches: The method of claim 14, wherein the subject is a first subject, and the method comprises determining, using the one or more processors and the denoising network, the modified trajectory further according to a position of the subject, motion of one or more second subjects, and a map representing features of the environment (¶35, For example, the prediction module 34 may receive information from the perception module 36 indicating that an object, such as another vehicle, a pedestrian, or an animal, exists laterally to the side of the vehicle 10. Based on information from the perception module 36 over time and localization information 44 from the localization module 30 over time, the prediction module 34 may determine a current trajectory of the object – and – ¶37, The motion planning module 40 receives the localization information 44… the obstacle information 52… and determines an optimal current trajectory for the vehicle 10 to follow).
Regarding claims 16 and 20, they recite similar limitations to claims 3 and 7 respectively and are rejected on the same grounds – see above.
Regarding claim 21, Xu fails to explicitly teach: The processor of claim 1, wherein the one or more circuits are to determine the clean trajectory based at least on predicting, from a given instance of the one or more instances of the noisy trajectory, the clean trajectory as a not noisy trajectory corresponding to a final step of a denoising schedule of the denoising network.
However, in the same field of endeavor, Janner teaches: wherein the one or more circuits are to determine the clean trajectory based at least on predicting, from a given instance of the one or more instances of the noisy trajectory, the clean trajectory as a not noisy trajectory corresponding to a final step of a denoising schedule of the denoising network (pg. 3, Section 3, ¶4, In this section, we describe Diffuser, a diffusion model designed for learned trajectory optimization – see pg. 1, fig. 1, which displays an iterative denoising process that results in a not noisy trajectory at the end).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a clean trajectory from a noisy trajectory as disclosed by Janner in the processor disclosed by Xu to predict trajectories effectively (pg. 2, section 1, we demonstrate that Diffuser has a number of useful properties and is particularly effective in control settings that require long-horizon reasoning and test-time flexibility).
Regarding claim 22, Xu fails to explicitly teach: The processor of claim 1, wherein the one or more circuits are to determine the modified trajectory using a past trajectory of the subject and one or more features at one or more positions of the noisy trajectory.
However, in the same field of endeavor, Janner teaches: wherein the one or more circuits are to determine the modified trajectory using a past trajectory of the subject (pg. 4, Section 3.2, The first action of a sampled trajectory… may be executed in the environment, after which the planning procedure begins again in a standard receding-horizon control loop – i.e., conditioning the trajectory determination process on past actions that are determined in an iterative process) and one or more features at one or more positions of the noisy trajectory (pg. 4, Section 3.2, We then train a separate model Jφ to predict the cumulative rewards of trajectory samples τi. The gradients of Jφ are used to guide the trajectory sampling procedure by modifying the means µ of the reverse process according to Equation 3. The first action of a sampled trajectory τ ∼ p(τ | O1:T = 1) may be executed in the environment, after which the planning procedure begins again in a standard receding-horizon control loop. Pseudocode for the guided planning method is given in Algorithm – and – pg. 5, fig. 3, item d, which shows one example reward function that involves different reward values at different positions – the reward at a specific location in the trajectory can be interpreted as a feature of a noisy trajectory involved in the trajectory determination process).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine a modified trajectory based on a past trajectory and a feature of the noisy trajectory as disclosed by Janner in the processor disclosed by Xu to predict trajectories effectively (pg. 2, section 1, we demonstrate that Diffuser has a number of useful properties and is particularly effective in control settings that require long-horizon reasoning and test-time flexibility).
Regarding claim 24, Xu further teaches: The processor of claim 1, wherein the one or more circuits are to determine the clean trajectory without modifying, using the one or more values for the one or more criteria, any one or more intermediate noisy trajectories on a denoising schedule from the one or more instances of the noisy trajectory to the clean trajectory (¶97, and select an optimal trajectory from the plurality of possible trajectories for each of a plurality of time periods, the optimal trajectory having a least associated estimated cost out of the plurality of possible trajectories – selecting a particular trajectory out of multiple trajectories is determining a clean trajectory without modifying intermediate noisy trajectories).
Claim(s) 6 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Janner as applied to claims 1 and 14 above, and further in view of Rezagholizadeh et al. (US 20180336471 A1), herein Rezagholizadeh.
Regarding claim 6, Xu in view of Janner fails to teach: The processor of claim 1, wherein the training data comprises a first subset of training data having a first type of annotation and a second set of training data having a second type of annotation.
However, in the same field of endeavor, Rezagholizadeh teaches: wherein the training data comprises a first subset of training data having a first type of annotation and a second set of training data having a second type of annotation (¶11, In some examples, the labelled training samples include a series of front camera image samples for a moving vehicle that are each labelled with a steering angle – two different steering angles of the possible steering angles may be interpreted as the two types of annotations).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use labelled training data as disclosed by Rezagholizadeh in the proecessor disclosed by Xu in view of Janner to ensure successful training (¶85, It will be appreciated that training data is of paramount importance for machine learning tasks. For supervised learning algorithms, training data requires appropriate labelling for quality training. Lack of enough labelled training samples for supervised training leads to poor learning).
Regarding claim 19, it recites similar limitations to claim 6 and is rejected on the same grounds – see above.
Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Janner in view of Xu.
Regarding claim 10, Janner teaches: A processor comprising: one or more circuits to (pg. 1, footnote, Code and visualizations of the learned denoising process are available – i.e., code that is implemented on a computer): determine, using a neural network and based at least on applying noise to a trajectory of a subject of a training data instance, a noisy trajectory (pg. 4, Section 3.1, We use Diffuser to parameterize a learned gradient θ(τi,i) of the trajectory denoising process… τi is the trajectory τ0 corrupted with noise); predict, based at least on the neural network processing the noisy trajectory, a clean trajectory of the subject; and update one or more parameters of the neural network according to the trajectory (pg. 4, Section 3.1, We use Diffuser to parameterize a learned gradient θ(τi,i) of the trajectory denoising process… τi is the trajectory τ0 corrupted with noise – see the objective function which is conditioned on the original trajectory τ0 as seen in the following equation
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Janner fails to explicitly teach: and update one or more parameters of the neural network according to the trajectory and the clean trajectory.
However, in the same field of endeavor, Xu teaches: and the clean trajectory (¶56, learning module 68 compares the demonstrated actual trajectory, based on the input of the expert driver 66, with the calculated optimal trajectory, as generated by the motion planning module 40. At 820, the learning module 68 updates the cost weights – i.e., comparison of two clean trajectories to inform a model)…
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to update network parameters based on a trajectory and a generated trajectory as disclosed by Xu in the processor disclosed by Janner to create optimized trajectories (¶21, so that the behavior of the autonomous coach vehicle more closely mimics the behavior of the vehicle when it is driven by the expert driver).
Regarding claim 11, Janner fails to explicitly teach: The processor of claim 10, wherein the one or more circuits are used to update the one or more parameters of the neural network responsive to a comparison of the trajectory and the clean trajectory.
However, in the same field of endeavor, Xu teaches: wherein the one or more circuits are used to update the one or more parameters of the neural network responsive to a comparison of the trajectory and the clean trajectory (¶56, learning module 68 compares the demonstrated actual trajectory, based on the input of the expert driver 66, with the calculated optimal trajectory, as generated by the motion planning module 40. At 820, the learning module 68 updates the cost weights – i.e., comparison of two clean trajectories to inform a model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to update network parameters based on a comparison between a trajectory and a generated trajectory as disclosed by Xu in the processor disclosed by Janner to create optimized trajectories (¶21, so that the behavior of the autonomous coach vehicle more closely mimics the behavior of the vehicle when it is driven by the expert driver).
Claim(s) 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Janner in view of Xu as applied to claim 10 above, and further in view of Rezagholizadeh.
Regarding claim 12, Janner in view of Xu fails to teach: The processor of claim 10, wherein the one or more circuits are used to configure the neural network using a plurality of training data instances comprising the training data instance, wherein the plurality of training data instances comprises a first subset having a first type of annotation and a second subset having a second type of annotation different from the first type.
However, in the same field of endeavor, Rezagholizadeh teaches: wherein the one or more circuits are used to configure the neural network using a plurality of training data instances comprising the training data instance, wherein the plurality of training data instances comprises a first subset having a first type of annotation and a second subset having a second type of annotation different from the first type (¶47, Since whether the input trajectory (e.g., a human-like trajectory generated by the generator 202 or a labeled trajectory from the labeled sample 205) is a real human-driven trajectory is known to the system, the discrimination result may be compared with that known trajectory status (e.g., real or fake) and may be fed back to the discriminator 206 through the corresponding loss function).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use labelled training data as disclosed by Jain in the processor disclosed by Janner in view of Xu to generate improved trajectories (¶23, the AV may provide better riding experience (e.g., a higher comfort level) that is more similar to vehicle driven by humans).
Regarding claim 13, Janner further teaches: The processor of claim 12, wherein the processor is comprised in at least one of: a control system for an autonomous or semi-autonomous machine (pg. 8, fig. 6, automatic control for locomotion); a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations (pg. 6, fig. 4, a simulated maze navigation task); 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 (pg. 7, fig. 5, a robot performs a block stacking task); a system for performing conversational Al operations; a system for performing generative Al operations using a large language model (LLM); 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.
Claim(s) 23 is rejected under 35 U.S.C. 103 as being unpatentable over Janner in view of Xu as applied to claim 1 above, and further in view of Gao et al. (US 20210191395 A1), herein Gao.
Regarding claim 23, Xu further teaches: The processor of claim 1, wherein the one or more circuits are to: determine one or more features from a map… the map comprising one or more trajectories of one or more remote subjects; and determine the clean trajectory using the one or more features (¶35, Based on the received information from the localization module 30 and the perception module 36, the prediction module 34 determines and predicts obstacle information 52 about potential obstacles in the surrounding environment of the vehicle 10, including, for example, the predicted trajectories of obstacles in the surrounding environment of the vehicle).
Xu in view of Janner fails to teach: using a featurizer network…
However, in the same field of endeavor, Gao teaches: using a featurizer network (¶95, The network 300 includes a feature assembler neural network 314 that assembles an appearance feature map 305, a trajectory feature map 310, and a context feature map 312 for each vehicle in the environment. The feature assembly neural network 314 can be implemented as a concatenation or addition layer. For example, for a vehicle 330 in the environment, the feature assembler neural network 314 can concatenate or average the appearance feature map corresponding to the vehicle, the trajectory feature for the vehicle, and the context feature to generate a single tensor)…
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a featurizer network as disclosed by Gao in the processor disclosed by Xu in view of Janner to improve trajectory determination accuracy (¶36, More generally, by being able to accurately predict nearby vehicles' intents and the trajectories that those vehicles would likely follow given a predicted intent, the autonomous vehicle can make better autonomous driving decisions or can provide better semi-autonomous driving recommendations for the operator of the vehicle).
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 HARRISON CHAN YOUNG KIM whose telephone number is (571)272-0713. The examiner can normally be reached Monday - Friday 10:00 am - 6:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HARRISON C KIM/ Examiner, Art Unit 2145
/CESAR B PAULA/ Supervisory Patent Examiner, Art Unit 2145
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