DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This non-final office action is in response to the RCE and amendment filed 23 February 2026.
Claims 1-20 are pending. Claims 1, 10, and 16 are independent claims.
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 an abstract idea without significantly more.
When considering subject matter eligibility under 35 USC 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1; MPEP 2106.03). If the claim falls within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed toward a judicial exception (Step 2A; MPEP 2106.04). This step is broken into two prongs.
The first prong (Step 2A, Prong 1) determines whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined at Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2; MPEP 2106.04). The second prong (Step 2A, Prong 2) determines whether the claims integrate the judicial exception into a practical application. If the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determine whether the claim is a patent-eligible exception (Step 2B; MPEP 2106.05).
If an abstract idea is present int the claim, in order to recite statutory subject matter, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application or amounts to significantly more than the abstract idea itself (see: 2019 PEG).
Step 1:
According to Step 1 of the two Step analysis, claims 1-9 are directed toward a processor (machine). Claims 10-15 are directed toward a system (machine). Claims 16-20 are directed toward a method (process). Therefore, each of these claims falls within one of the four statutory categories.
Claim 1:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 1, the claims recite:
extract… from a dataset of synthetic data representing first object locations of one or more objects, first features corresponding to the first object locations and second features corresponding to second object locations represented by real-world data, the first object locations determined by sampling one or more probability distributions (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses observing first and second features corresponding to first and second locations in a synthetic data set using a probability distribution)
detect… a distributional discrepancy between the synthetic data and the real-world data based at least on spatial correspondences between the first features and the second features in the spatial map (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to detect a distributional discrepancy between the synthetic data and the real world data based on correspondences between the first and second features in the spatial map)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
at least one processor comprising: one or more circuits
These elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
using one or more machine learning models (MLMs)
detect, based at least on applying the first features and the second features to one or more discriminators
update one or more parameters of the one or more MLMs to reduce the detected distributional discrepancy
compute one or more control operations for an ego machine using the one or more MLMs having the updated one or more parameters
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Finally, the claim recites the additional element:
input data to the one or more discriminators
As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
at least one processor comprising: one or more circuits
These elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
using one or more machine learning models (MLMs)
detect, based at least on applying the first features and the second features to one or more discriminators
update one or more parameters of the one or more MLMs to reduce the detected distributional discrepancy
compute one or more control operations for an ego machine using the one or more MLMs having the updated one or more parameters
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Finally, the claim recites the additional element:
input data to the one or more discriminators
As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Additionally, the courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 2:
With respect to dependent claim 2, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 2, the claim recites the element:
wherein one or more probability distributions correspond to one or more spatial priors that are generated by sampling the first object locations proportionally to a longitudinal distance of one or more corresponding objects to a synthetic ego-machine represented by the synthetic data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses evaluating a probability distribution, the probability distribution corresponding to one or more spatial priors generated by sampling the first object locations proportionally to a longitudinal distance of one or more corresponding objects)
Claim 3:
With respect to dependent claim 3, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
wherein one or more discriminators include per-location domain classifiers of locations in the spatial map
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
wherein one or more discriminators include per-location domain classifiers of locations in the spatial map
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 4:
With respect to dependent claim 4, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
wherein the one or more parameters are further updated based at least on predictions made by the one or more MLMs using the first feature to reduce a loss between the predictions and labels corresponding to the predictions
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
wherein the one or more parameters are further updated based at least on predictions made by the one or more MLMs using the first feature to reduce a loss between the predictions and labels corresponding to the predictions
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 5:
With respect to dependent claim 5, the claim depends upon dependent claim 4. The analysis of claim 4 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
wherein the one or more parameters are further updated based at least on second predictions made by the one or more MLMs using the second features to reduce a second loss between the second predictions and pseudo labels corresponding to the second predictions using a different loss function than a loss function used to compute the loss, wherein one or more pseudo labels are generated during training of the one or more MLMs using outputs of the one or more MLMs that have a confidence above a confidence threshold
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
wherein the one or more parameters are further updated based at least on second predictions made by the one or more MLMs using the second features to reduce a second loss between the second predictions and pseudo labels corresponding to the second predictions using a different loss function than a loss function used to compute the loss, wherein one or more pseudo labels are generated during training of the one or more MLMs using outputs of the one or more MLMs that have a confidence above a confidence threshold
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 6:
With respect to dependent claim 6, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 6, the claim recites the element:
wherein the first object locations are sampled independently of a structure of a path of a synthetic ego-machine through a simulated environment (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation of a sampled location of a first object location)
Claim 7:
With respect to dependent claim 7, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 7, the claim recites the element:
wherein one or more simulator parameters are sampled to generate the synthetic data, the one or more simulator parameters including at least one of a number of objects of one or more objects, one or more poses of the one or more objects, one or more colors of the one or more objects, one or more colors of one or more environmental features, or one or more weather conditions of a virtual environment (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation of parameters including at least one of a number of objects of one or more objects, one or more poses of the one or more objects, one or more colors of the one or more objects, one or more colors of one or more environmental features, or one or more weather conditions of a virtual environment)
Claim 8:
With respect to dependent claim 8, the claim depends upon independent claim 1. The analysis of claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 8, the claim recites the element:
wherein the one or more probability distributions correspond to a target prior generated based at least on a distribution of a real-world data set (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses evaluating a probability distribution, the probability distribution corresponding to a target prior generated based on least on a distribution of a real-word data set)
Claim 9:
With respect to dependent claim 9, the claim depends upon independent claim 1. The analysis of claim 9 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
wherein the at least one processor is comprised in at least one of:
a control system for 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 on an edge device
a system implementing using a robot
a system for performing conversational AI operations
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
As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
wherein the at least one processor is comprised in at least one of:
a control system for 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 on an edge device
a system implementing using a robot
a system for performing conversational AI operations
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
As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 10:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 10, the claims recite:
extract… from a dataset of synthetic data representing first object locations of one or more objects, first features corresponding to the first object locations and second features corresponding to second object locations represented by real-world data, the first object locations determined based at least on sampling one or more probability distributions (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses observing first and second features corresponding to first and second locations in a synthetic data set using a probability distribution)
detect… a distributional discrepancy between the synthetic data and the real-world data based at least on spatial correspondences between the first features and the second features in the spatial map (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation to detect a distributional discrepancy between the synthetic data and the real world data based on correspondences between the first and second features in the spatial map)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
one or more processing units comprising processing circuitry
These elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
using one or more machine learning models (MLMs)
detect, based at least on applying the first features and the second features corresponding to second object locations represented by real-world data to one or more discriminators
update one or more parameters of the one or more MLMs to reduce the detected distributional discrepancy
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Finally, the claim recites the additional element:
input data to the one or more discriminators
As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
one or more processing units comprising processing circuitry
These elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
using one or more machine learning models (MLMs)
detect, based at least on applying the first features and the second features corresponding to second object locations represented by real-world data to one or more discriminators
update one or more parameters of the one or more MLMs to reduce the detected distributional discrepancy
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Finally, the claim recites the additional element:
input data to the one or more discriminators
As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Additionally, the courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 11:
With respect to dependent claim 11, the claim depends upon independent claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
wherein the one or more parameters are updated based at least on using one or more pseudo labels determined based at least on one or more outputs of the one or more MLMs having a confidence above a confidence threshold
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
wherein the one or more parameters are updated based at least on using one or more pseudo labels determined based at least on one or more outputs of the one or more MLMs having a confidence above a confidence threshold
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 12:
With respect to dependent claim 12, the claim depends upon independent claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 12, the claim recites the element:
wherein the one or more probability distributions correspond to a spatial prior that is agnostic to a structure of a path of an ego-machine represented using one or more simulators used to generate the dataset (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses evaluating a probability distribution, the probability distribution corresponding to one or more spatial priors)
Claim 13:
With respect to dependent claim 13, the claim depends upon independent claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 13, the claim recites the element:
wherein one or more probabilities of the one or more probability distributions decrease proportionally to a longitudinal distance from an ego-machine represented using one or more simulators used to generate the dataset (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses evaluating a probability distribution, the probability distribution, wherein the one or more probability distributions decrease proportionally to a long longitudinal distance)
Claim 14:
With respect to dependent claim 14, the claim depends upon independent claim 10. The analysis of claim 10 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
wherein the one or more MLMs comprise a neural network
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
wherein the one or more MLMs comprise a neural network
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 15:
With respect to claim 15, the claim recites the elements similar to those in claim 9. Claim 15 is rejected under similar rationale.
Claim 16:
With respect to independent claim 16, the claim recites the elements similar to those in claim 1. Claim 16 is rejected under similar rationale.
Claim 17:
With respect to dependent claim 17, the claim recites the elements similar to those in claim 2. Claim 17 is rejected under similar rationale.
Claim 18:
With respect to dependent claim 18, the claim depends upon independent claim 16. The analysis of claim 16 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
wherein the one or more discriminators classify, per-location in the spatial map, wherein the first features and the second features correspond to a synthetic domain or a real-world domain
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
wherein the one or more discriminators classify, per-location in the spatial map, wherein the first features and the second features correspond to a synthetic domain or a real-world domain
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 19:
With respect to dependent claim 19, the claim depends upon independent claim 16. The analysis of claim 16 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim discloses the following additional elements:
wherein the one or more MLMs were trained using one or more pseudo labels, the one or more pseudo labels being generated during training of the one or more MLMs using outputs of the one or more MLMs that have a confidence above a confidence threshold
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claim discloses the following additional elements:
wherein the one or more MLMs were trained using one or more pseudo labels, the one or more pseudo labels being generated during training of the one or more MLMs using outputs of the one or more MLMs that have a confidence above a confidence threshold
These elements are recited at a high-level of generality with no detail of the training process and/or implementation of the machine learning models and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 20:
With respect to dependent claim 20, the claim recites the elements similar to those in claim 8. Claim 20 is rejected under similar rationale.
Claim Rejections - 35 USC § 103
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 6-9, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (Spatially-Aware Domain Adaptation For Semantic Segmentation Of Urban Scenes, 2019, hereafter Xin, provided via IDS filed 9 January 2023) and further in view of Granieri et al (US 2021/0300438, filed 30 March 2020, hereafter Granieri) and further in view of Graf et al. (US 2009/0020284, published 22 January 2009, hereafter Graf) and further in view of Iqbal et al. (MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling, 1 March 2020, hereafter Iqbal, provided via IDS filed 9 January 2023).
As per independent claim 1, Lin discloses a processor comprising:
extract, using one or more machine learning models (MLMs) and from a data of synthetic data representing first object locations of one or more objects, first features corresponding to the first object locations (Section 3.1: Here, a machine learning model is trained using a synthetic dataset. This synthetic dataset comprises images from the game Grand Theft Auto 5 (GTA5). This data includes features corresponding to first locations, such as poles, lights, signs people, riders, cars, and trucks)
detect, based at least one applying the first features and the second features to one or more discriminators, a distributional discrepancy based at least on spatial correspondences between the first features and the second features in the spatial map of input data to the one or more discriminators (Figure 2; Sections 2: Here, a Discriminator Network consists of the Spatial Adaptation Module and Domain Adaptation Module are discriminators. The discriminator network classifies every pixel in the input image into a class (Figure 3). Then the discriminator attempts to determine whether the predicted semantic label belongs to the source or target domains using an adversarial training, and a loss (discrepancy) is calculated (Equation 1))
update one or more parameters of the one or more MLMs to reduce the detected distribution discrepancy (Section 3.1: Here, the model is trained using a Stochastic Gradient Descent optimizer on a synthetic dataset of images from Grand Theft Auto 5 (GTA5). This data includes images with semantic labels for 19 categories)
compute one or more control operations for an ego-machine using the one or more MLMs having the updated one or more parameters (Section 1: Here, a machine learning model, such as a model for semantic segmentation and spatial awareness are applied via a generative adversarial network)
Lin fails to specifically disclose one or more circuits. However, Granieri, which is analogous to the claimed invention because it is directed toward making control decisions for an ego-machine (paragraph 0056), discloses an integrated circuit device (paragraph 0107: Here, the processor may be implemented as an application-specific integrated circuit). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Granieri with Lin, with a reasonable expectation of success, as it would have allowed for implementing the machine learning model in an on-board computing circuit (Granieri: paragraph 0107). This would have facilitated processing of data at the mobile device to improve the speed of communication.
Additionally, Lin fails to specifically disclose:
the first object location determined by sampling one or more probability distributions
a distributional discrepancy between the synthetic data and the real-world data
However, Graf, which is analogous to the claimed invention because it is directed toward comparing probability distribution of synthetic and real-world data, discloses:
the first object location determined by sampling one or more probability distributions (Figure 7A; paragraphs 0094-0095: Here, populated data is compared to estimated data using a probabilistic relationship such as a probability distribution)
detect, a distributional discrepancy between the synthetic data and the real-world data (Figure 7A; paragraphs 0094-0095: Here, populated data is compared to estimated data using a probabilistic relationship such as a probability distribution)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Graf with Lin-Granieri, with a reasonable expectation of success, as it would have allowed for improved population of data based upon real world and synthetic data (Graf: paragraph 0094).
Finally, Lin fails to specifically disclose second features corresponding to second object locations represented by real-world data. However, Iqbal, which is analogous to the claimed invention because it is directed toward training machine learning models, discloses second features corresponding to second object locations represented by real-world data (Section 4.1.1: Here, the experiment uses both synthetic data (GTA5) and real-world data (Cityscapes) to extract location data for 19 categories of data supported by Cityscapes dataset. These include features corresponding to first locations, such as poles, lights, signs people, riders, cars, and trucks (Table 1)).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Iqbal with Lin-Granieri-Graf, with a reasonable expectation of success, as it would have allowed for comparing real-world and simulated datasets to improve labeling of data (Iqbal: Section 4).
As per dependent claim 3, Lin, Granieri, Graf, and Iqbal disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Lin discloses wherein the one or more discriminators include per-location domain classifiers of locations in the spatial map (Section 1: Here, a spatially-aware domain classifier model is used to identify objects within the image data).
As per dependent claim 6, Lin, Granieri, Graf, and Iqbal disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Lin discloses wherein the first object locations are sampled independently of a structure of a path of a synthetic ego-machine through a simulated environment (Section 1: Here, the image data may be processed to apply spatial priors, such as the sky will always be at the top portion of the image while the roads will always be on the bottom part of the image, through the environment).
As per dependent claim 7, Lin, Granieri, Graf, and Iqbal disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Lin discloses wherein one or more simulator parameters are sampled to generate the synthetic data (Section 3.1: Here, the synthetic data includes a plurality of labels), the one or more simulator parameters including at least one of a number of objects of one or more objects, one or more poses of the one or more objects, one or more colors of the one or more objects, colors of one or more environmental features, or one or more weather conditions of a virtual environment (Table 1: Here, at least one of a number of objects of the one or more objects, such as road, sidewalk, building, wall, fence, within the synthetic data are identified).
As per dependent claim 8, Lin, Granieri, Graf, and Iqbal disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Xin discloses wherein the one or more probability distributions correspond to a target prior generated based at least on a distribution of a real-world data set (Section 3.1: Here, the spatial priors are applied to the real-world data to identify objects).
As per dependent claim 9, Lin, Granieri, Graf, and Iqbal disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Lin discloses wherein the processor is comprised in at least one of: a control system for an autonomous or semi-autonomous vehicle, 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, system for performing conversational AI operations, 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 (Section 1: Here, a semi-autonomous machine for detecting pedestrians in urban scenes is disclosed).
With respect to independent claim 16, the claim recites limitations substantially similar to those in claim 1. Claim 16 is similarly rejected.
As per dependent claim 20, Lin, Granieri, and Graf disclose the limitations similar to those in claim 16, and the same rejection is incorporated herein. Lin discloses wherein the one or more probability distributions correspond to a target prior generated based at least in part on a distribution of real-world data set (Section 3: Here, the implementation includes a dataset of synthetic data, GTA5 data, and real-world data, Cityscapes dataset).
Claims 2, 10, 12-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lin, Granieri, Graf, and Iqbal and further in view of Chen et al. (ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes, 7 April 2018, hereafter Chen).
As per dependent claim 2, Lin, Granieri, Graf, and Iqbal disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Lin discloses sample one or more object locations as a distance from a location of an ego-machine represented by synthetic data (Section 2.3: Here, the Spatial Aware Adaptation Module computes a probability of an object being at a specified location of the ego-machine).
However, Lin fails to specifically disclose wherein the one or more probability distributions correspond to one or more spatial priors that are generated by sampling the first object locations proportionally to a longitudinal distance.
However, Chen, which is analogous to the claimed invention because it is directed toward sampling object locations of objects in relation to an ego-machine, discloses:
wherein the one or more probability distributions correspond to one or more spatial priors that are generated by sampling the first object locations proportionally to a longitudinal distance (Section 3.2)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Chen with Lin-Granieri-Graf-Iqbal, with a reasonable expectation of success, as it would have allowed for discriminating objects based upon the distance from the ego-machine (Chen: Section 3.2).
As per independent claim 10, Xin discloses a system comprising:
extract, using one or more machine learning models (MLMs) and from a data of synthetic data representing first object locations of one or more objects, first features corresponding to the first object locations (Section 3.1: Here, a machine learning model is trained using a synthetic dataset. This synthetic dataset comprises images from the game Grand Theft Auto 5 (GTA5). This data includes features corresponding to first locations, such as poles, lights, signs people, riders, cars, and trucks)
detect, based at least one applying the first features and the second features to one or more discriminators, a distributional discrepancy based at least on spatial correspondences between the first features and the second features in the spatial map of input data to the one or more discriminators (Figure 2; Sections 2: Here, a Discriminator Network consists of the Spatial Adaptation Module and Domain Adaptation Module are discriminators. The discriminator network classifies every pixel in the input image into a class (Figure 3). Then the discriminator attempts to determine whether the predicted semantic label belongs to the source or target domains using an adversarial training, and a loss (discrepancy) is calculated (Equation 1))
update one or more parameters of the one or more MLMs to reduce the detected distribution discrepancy (Section 3.1: Here, the model is trained using a Stochastic Gradient Descent optimizer on a synthetic dataset of images from Grand Theft Auto 5 (GTA5). This data includes images with semantic labels for 19 categories)
compute one or more control operations for an ego-machine using the one or more MLMs having the updated one or more parameters (Section 1: Here, a machine learning model, such as a model for semantic segmentation and spatial awareness are applied via a generative adversarial network)
Lin fails to specifically disclose one or more circuits. However, Granieri, which is analogous to the claimed invention because it is directed toward making control decisions for an ego-machine (paragraph 0056), discloses an integrated circuit device (paragraph 0107: Here, the processor may be implemented as an application-specific integrated circuit). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Granieri with Lin, with a reasonable expectation of success, as it would have allowed for implementing the machine learning model in an on-board computing circuit (Granieri: paragraph 0107). This would have facilitated processing of data at the mobile device to improve the speed of communication.
Additionally, Lin fails to specifically disclose:
the first object location determined by sampling one or more probability distributions
a distributional discrepancy between the synthetic data and the real-world data
However, Graf, which is analogous to the claimed invention because it is directed toward comparing probability distribution of synthetic and real-world data, discloses:
the first object location determined by sampling one or more probability distributions (Figure 7A; paragraphs 0094-0095: Here, populated data is compared to estimated data using a probabilistic relationship such as a probability distribution)
detect, a distributional discrepancy between the synthetic data and the real-world data (Figure 7A; paragraphs 0094-0095: Here, populated data is compared to estimated data using a probabilistic relationship such as a probability distribution)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Graf with Lin-Granieri, with a reasonable expectation of success, as it would have allowed for improved population of data based upon real world and synthetic data (Graf: paragraph 0094).
Additionally, Lin fails to specifically disclose second features corresponding to second object locations represented by real-world data. However, Iqbal, which is analogous to the claimed invention because it is directed toward training machine learning models, discloses second features corresponding to second object locations represented by real-world data (Section 4.1.1: Here, the experiment uses both synthetic data (GTA5) and real-world data (Cityscapes) to extract location data for 19 categories of data supported by Cityscapes dataset. These include features corresponding to first locations, such as poles, lights, signs people, riders, cars, and trucks (Table 1)).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Iqbal with Lin-Granieri-Graf, with a reasonable expectation of success, as it would have allowed for comparing real-world and simulated datasets to improve labeling of data (Iqbal: Section 4).
Finally, Lin fails to specifically disclose determining based at least on sampling one or more probability distributions. Further, Chen, which is analogous to the claimed invention because it is directed toward sampling object locations of objects in relation to an ego-machine, discloses determining based at least on sampling one or more probability distributions (Section 3.2).
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Chen with Lin-Granieri, with a reasonable expectation of success, as it would have allowed for discriminating objects based upon the distance from the ego-machine (Chen: Section 3.2).
As per dependent claim 12, Lin, Granieri, Graf, Iqbal, and Chen disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Lin discloses wherein the one or more probability distributions correspond to a spatial prior that is agnostic to a structure path of the ego-machine represented using the one or more simulators used to generate the dataset (Section 1: Here, the image data may be processed to apply spatial priors, such as the sky will always be at the top portion of the image while the roads will always be on the bottom part of the image, through the environment).
As per dependent claim 13, Lin, Granieri, Graf, Iqbal, and Chen disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Chen discloses wherein one or more probabilities of the one or more probability distributions decrease proportionally to the longitudinal distance (Section 3.2)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Chen with Xin-Granieri, with a reasonable expectation of success, as it would have allowed for discriminating objects based upon the distance from the ego-machine (Chen: Section 3.2).
As per dependent claim 14, Lin, Granieri, Graf, and Chen disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Lin discloses wherein the one or more MLMs comprise a neural network (Figure 2; Section 2.s: Here, the domain adaptation module uses a discriminator).
As per dependent claim 15, Lin, Granieri, Graf, Iqbal, and Chen disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Lin discloses wherein the processor is comprised in at least one of: a control system for an autonomous or semi-autonomous vehicle, 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, system for performing conversational AI operations, 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 (Section 1: Here, a semi-autonomous machine for detecting pedestrians in urban scenes is disclosed).
With respect to dependent claim 17, the claim recites limitations substantially similar to those in claim 2. Claim 17 is similarly rejected.
Claims 4 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lin, Granieri, Graf, and Iqbal and further in view of Olarig et al. (US 2021/0256311, filed 20 March 2020, hereafter Olarig).
As per dependent claim 4, Lin, Granieri, Graf, and Iqbal disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Lin fails to specifically disclose wherein the one or more parameters are further updated based at least on predictions made by the one or more MLMs using the first features to reduce a loss between the predictions and labels corresponding to the predictions.
However, Olarig, which is analogous to the claimed invention because it is directed toward identifying objects using a confidence level, discloses wherein the one or more parameters are further updated based at least on predictions made by the one or more MLMs using the first features to reduce a loss between the predictions and labels corresponding to the predictions (paragraphs 0051 and 0057: Here, a machine learning model is used to label objects in an image (paragraph 0051). This includes performing labeling based upon the object using a confidence threshold (paragraph 0057)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Olarig with Lin-Ganieri, with a reasonable expectation of success, as it would have allowed for labeling objects when a confidence threshold is met (Olarig: paragraph 0057). This would have improved labeling using the machine learning model, as items below a threshold would not be labeled, while those meeting the confidence threshold would be labeled, thereby improving accuracy of the model.
As per dependent claim 19, Lin, Ganieri, Graf, and Iqbal disclose the limitations similar to those in claim 16, and the same rejection is incorporated herein. Lin fails to specifically disclose wherein the one or more parameters are updated based at least in part on using one or more pseudo labels determined at least in part on one or more outputs of the machine learning model having a confidence above a confidence threshold.
However, Olarig, which is analogous to the claimed invention because it is directed toward identifying objects using a confidence level, discloses wherein the one or more parameters are updated based at least in part on using one or more pseudo labels determined at least in part on one or more outputs of the machine learning model having a confidence above a confidence threshold (paragraphs 0051 and 0057: Here, a machine learning model is used to label objects in an image (paragraph 0051). This includes performing labeling based upon the object using a confidence threshold (paragraph 0057)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Olarig with Lin-Ganieri, with a reasonable expectation of success, as it would have allowed for labeling objects when a confidence threshold is met (Olarig: paragraph 0057). This would have improved labeling using the machine learning model, as items below a threshold would not be labeled, while those meeting the confidence threshold would be labeled, thereby improving accuracy of the model.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Lin, Granieri, Graf, and Iqbal, and Olarig and further in view of Yoon et al. (US 2021/0326654, filed 20 April 2021, hereafter Yoon).
As per dependent claim 5, Lin, Granieri, Graf, Iqbal, and Olarig disclose the limitations similar to those in claim 4, and the same rejection is incorporated herein. Lin fails to specifically disclose wherein the one or more parameters are further updated based at least on second predictions made by the one or more MLMs using the second features to reduce a second loss between the second predictions and pseudo labels corresponding to the second predictions using a different loss function than a loss function used to compute the loss, wherein one or more pseudo labels are generated during training of the one or more MLMs using output of the one or more MLMs that have a confidence above a confidence threshold.
However, Olarig, which is analogous to the claimed invention because it is directed toward identifying objects using a confidence level, discloses wherein the one or more parameters are further updated based at least on predictions made by the one or more MLMs using the first features to reduce a loss between the predictions and labels corresponding to the predictions (paragraphs 0051 and 0057: Here, a machine learning model is used to label objects in an image (paragraph 0051). This includes performing labeling based upon the object using a confidence threshold (paragraph 0057)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Olarig with Lin-Ganieri, with a reasonable expectation of success, as it would have allowed for labeling objects when a confidence threshold is met (Olarig: paragraph 0057). This would have improved labeling using the machine learning model, as items below a threshold would not be labeled, while those meeting the confidence threshold would be labeled, thereby improving accuracy of the model.
Further, Yoon, which is analogous to the claimed invention because it is directed toward adjusting parameters to reduce loss between predictions and labels discloses wherein the one or more parameters are further updated based at least on second predictions made by the one or more MLMs (paragraph 0019: Here, a second prediction label is generated for images and a first and second loss functions corresponding to the first labels and the second labels are calculated) using the second features to reduce a second loss between the second predictions and pseudo labels corresponding to the second predictions using a different loss function (paragraph 0061: Here, a neural network model adjusts parameters to minimize loss functions). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Yoon with Lin-Granieri-Graf-Iqbal-Olarig, with a reasonable expectation of success, as it would have allowed for adjusting parameters for multiple different predictions in order to improve classification by minimizing the loss function (Yoon: paragraph 0061).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Xin, Granieri, Graf, Iqbal, and Chen and further in view of Olarig.
As per dependent claim 11, Xin, Granieri, Graf, Iqbal, and Chen disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Xin fails to specifically disclose wherein the one or more parameters are updated based at least on using one or more pseudo labels determined at least in part on one or more outputs of the one or more MLMs having a confidence above a confidence threshold.
However, Olarig, which is analogous to the claimed invention because it is directed toward identifying objects using a confidence level, discloses wherein the one or more parameters are updated based at least on using one or more pseudo labels determined at least in part on one or more outputs of the one or more MLMs having a confidence above a confidence threshold (paragraphs 0051 and 0057: Here, a machine learning model is used to label objects in an image (paragraph 0051). This includes performing labeling based upon the object using a confidence threshold (paragraph 0057)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Olarig with Xin-Ganieri, with a reasonable expectation of success, as it would have allowed for labeling objects when a confidence threshold is met (Olarig: paragraph 0057). This would have improved labeling using the machine learning model, as items below a threshold would not be labeled, while those meeting the confidence threshold would be labeled, thereby improving accuracy of the model.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Lin, Granieri, Graf, and Iqbal, and further in view of Mugan et al. (US 11977991, filed 24 July 2020, hereafter Mugan).
As per dependent claim 18, Lin, Granieri, Graf, and Iqbal disclose the limitations similar to those in claim 16, and the same rejection is incorporated herein. Lin discloses classifying items based on per-locations in the spatial map (Section 3). Lin fails to specifically discloses wherein the one or more discriminators classify, per-locations in the spatial map, whether the first feature and the second features correspond to a synthetic domain or a real-world domain.
However, Mugan, which is analogous to the claimed invention because it is directed toward applying a discriminator to identify synthetic records, discloses wherein the one or more discriminators classify whether the first feature and the second features correspond to a synthetic domain or a real-world domain (Figure 5; column 7, line 61- column 8, line 2: Here, a discriminator determines whether a record is a synthetic record or a real-world record). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Mugan with Lin-Granieri-Graf-Iqbal, with a reasonable expectation of success, as it would have allowed for discriminating between synthetic and real-word data (Mugan: column 7, line 61- column 8, line 2).
Response to Arguments
Applicant’s arguments with respect to the rejection(s) of claim(s) under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Lin, Granieri, Graf, and Iqbal.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wang et al. (US 2022/0139531): Discloses adjusting operational parameters of a machine learning model to minimize a loss function (paragraph 0056)
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/KYLE R STORK/Primary Examiner, Art Unit 2128