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 final office action is in response to the amendment filed 11 June 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…a first feature map… including first latent features corresponding to first object indicated in synthetic data and a second feature map… including second latent features corresponding to second objects indicated in real-world data, the first objects having 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 and creation (extraction) of a feature map based upon these features)
detect a distributional discrepancy between the synthetic data and the real-world data at least on one or more discriminators determining respective classification of respective locations in the first feature map and the second feature map of whether the respective locations correspond to a real-world domain or a synthetic domain (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 evaluation to classify data as synthetic or real-world data and an observation to detect a distributional discrepancy between the labeled synthetic data and the labeled 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 first portions of one or more machine learning models (MLMs)
a latent representation space of the one or more MLMs
the latent representation space
update, using the respective classifications and one or more predictions generated from the first latent features and the second latent features by one or more second portions of the one or more MLM, one or more parameters of at least the one or more first portions 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))
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 first portions of one or more machine learning models (MLMs)
a latent representation space of the one or more MLMs
the latent representation space
update, using the respective classifications and one or more predictions generated from the first latent features and the second latent features by one or more second portions of the one or more MLM, one or more parameters of at least the one or more first portions 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))
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 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 the one or more first portions of the one or more MLMs comprise an encoder backbone, and one or more second portions of the one or more MLMs comprise an up-sampling module
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 first portions of the one or more MLMs comprise an encoder backbone, and one or more second portions of the one or more MLMs comprise an up-sampling module
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 the one or more predictions to reduce a loss between the predictions and labels corresponding to the one or more 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 the one or more predictions to reduce a loss between the predictions and labels corresponding to the one or more 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 second portions of the one or more MLMs using the second latent 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 second portions of the one or more MLMs using the second latent 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 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:
extracting… a first feature map… including first latent features corresponding to first object indicated in synthetic data and a second feature map… including second latent features corresponding to second objects indicated in real-world data, the first object having 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 and creation (extraction) of a feature map based upon these features)
detect a distributional discrepancy between the synthetic data and the real-world data based at least one or more discriminators determining respective classifications of respective locations in the first feature map and the second feature map of whether the respective locations correspond to a real-world domain or a synthetic domain (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 evaluation to classify data as synthetic or real-world data and an observation to detect a distributional discrepancy between the labeled synthetic data and the labeled 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 first portions of one or more machine learning models (MLMs)
a latent representation space of the one or more MLMs
the latent representation space
update, using the respective classifications and one or more predictions generated from the first latent features and the second latent features by one or more second portions of the one or more MLM, one or more parameters of at least the one or more first portions 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 first portions of one or more machine learning models (MLMs)
a latent representation space of the one or more MLMs
the latent representation space
update, using the respective classifications and one or more predictions generated from the first latent features and the second latent features by one or more second portions of the one or more MLM, one or more parameters of at least the one or more first portions 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 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)
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 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, 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:
parameters are updated based at least on combining the respective classifications across the respective locations
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:
parameters are updated based at least on combining the respective classifications across the respective locations
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, 6-10, 12, 14-16, 18, 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 Bai (US 11494632, filed 7 December 2017) and further in view of Granieri et al (US 2021/0300438, filed 30 March 2020, hereafter Granieri).
As per independent claim 1, Lin discloses a processor comprising:
extract, using one or more first portions of one or more machine learning models (MLMs), a first feature map in a latent representation space of the one or more MLMs and including first latent features corresponding to first object indicated in synthetic data, the first objects having object locations determined by sampling one or more probability distribution (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)
Lin fails to specifically disclose:
extracting… a second feature map in the latent representation space and including second latent features corresponding to second objects indicate in real-world data
detect a distributional discrepancy between the synthetic data and the real-world data based at least on one or more discriminators determining respective classifications of respective locations in the first feature map and the second feature map of whether the respective locations correspond to a real-world domain or a synthetic domain
update, using the respective classifications and one or more predictions generated from the first latent features and the second latent features by one or more second portions of the one or more MLMs, one or more parameters of at least the one or more first portions of the one or more MLMs to reduce the detected distributional discrepancy
However, Bai, which is analogous to the claimed invention because it is directed toward mitigating the reality gap, discloses:
extracting… a second feature map in the latent representation space and including second latent features corresponding to second objects indicate in real-world data (Figure 2; column 17, line 9- column 18, line 31: Here, an instance for an episode is stored. This includes environmental data (item 2542) including the beginning environment state for one or more environmental objects in a space of real world physical objects including 3D models for environmental objects)
detect a distributional discrepancy between the synthetic data and the real-world data based at least on one or more discriminators determining respective classifications of respective locations in the first feature map and the second feature map of whether the respective locations correspond to a real-world domain or a synthetic domain (Figures 3-4; column 19, line 39- column 20, line 34: Here, a comparison is performed between the real world episode success and simulated episode success to determine a reality measure. This reality measure is compared against a reality measure threshold to determine if parameters should be modified and new simulated training data should be generated)
update, using the respective classifications and one or more predictions generated from the first latent features and the second latent features by one or more second portions of the one or more MLMs, one or more parameters of at least the one or more first portions of the one or more MLMs to reduce the detected distributional discrepancy (Figure 3; column 20, lines 32-58: Here, the modified parameters are used to retrain the model and generate a new set of simulated training examples)
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 Bai with Lin, with a reasonable expectation of success, as it would have allowed for improved generation of simulated training data to close the reality gap (Bai: column 2, lines 44-50).
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-Bai, 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.
As per dependent claim 6, Lin, Bai, and Granieri disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Lin discloses wherein the 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, Bai, and Granieri 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, Bai, and Granieri 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, Bai, and Granieri 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 10, the claim recites the limitations substantially similar to those in claim 1, and the rejection is incorporated herein by reference.
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.
As per dependent claim 12, Lin, Bai, and Granieri 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 14, Lin, Bai, and Granieri 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, Bai, and Granieri 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 independent claim 16, the claim recites limitations substantially similar to those in claim 1. Claim 16 is similarly rejected.
As per dependent claim 18, Lin, Bai, and Granieri 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 parameters are updated based at least on combining the respective classifications across the respective locations.
However, Bai, discloses wherein the one or more parameters are updated based at least on combining the respective classifications across the respective locations (Figures 3-4; column 19, line 39- column 20, line 34: Here, a comparison is performed between the real world episode success and simulated episode success to determine a reality measure. This reality measure is compared against a reality measure threshold to determine if parameters should be modified and new simulated training data should be generated).
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 Bai with Lin, with a reasonable expectation of success, as it would have allowed for improved generation of simulated training data to close the reality gap (Bai: column 2, lines 44-50).
As per dependent claim 20, Lin, Bai, and Granieri 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, 13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lin, Bai, and Granieri, 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, Bai, and Granieri 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 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-Bai-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 13, Lin, Bai, and Granieri, disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Lin fails to specifically disclose wherein one or more probabilities of the one or more probability distributions decrease proportionally to the 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 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-Bai-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).
With respect to dependent claim 17, the claim recites limitations substantially similar to those in claim 2. Claim 17 is similarly rejected.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Lin, Bai, and Granieri and further in view of Galeev et al. (US 2021/0089845, filed 11 September 2020, hereafter Galeev).
As per dependent claim 3, Lin, Bai, and Granieri disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Lin fails to specifically disclose first portions of the one or more MLMs comprise an encoder backbone, and the one or more second portions of the one or more MLMs comprise an up-sampling module.
However, Galeev, which is analogous to the claimed invention because it is directed toward teaching generation of data, discloses first portions of the one or more MLMs comprise an encoder backbone, and the one or more second portions of the one or more MLMs comprise an up-sampling module (paragraphs 0009-0010: Here, a backbone encoder is used to encode images. These images may then be up sampled through a bilinear interpolation or a series of transposed convolutions). 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 Galeev with Lin-Bai-Granieri, with a reasonable expectation of success, as it would have allowed for encoding/decoding images to obtain sharper segmentation masks (Galeev: paragraph 0010).
Claims 4, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lin, Bai, and Granieri and further in view of Olarig et al. (US 2021/0256311, filed 20 March 2020, hereafter Olarig).
As per dependent claim 4, Lin, Bai, and Granieri disclose the limitations similar to those in claim 1, 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 one or more predictions to reduce a loss between the one or more predictions and labels corresponding to the one or more 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-Bai-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 11, Lin, Bai, and Granieri disclose the limitations similar to those in claim 10, and the same rejection is incorporated herein. Lin 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 Lin-Bai-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, Bai, and Ganieri, 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-Bai-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, Bai, Granieri, 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, Bai, Granieri, 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 second portions of the one or more MLMs using the second latent 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 second portions of the one or more MLMs using the second latent 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-Bai-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).
Response to Arguments
Applicant’s arguments with respect to the rejection of claims under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Lin, Bai, Granieri.
Applicant's arguments with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
With respect to Step 2A, Prong 1, the applicant argues that “the claims are not directed to an abstract idea, at least, because they are not directed to an abstract idea (page 12).” The applicant broadly generalizes that “the “character as a whole” of the amended claims is not directed to a mental process (page 12).” However, the applicant does not refute the examiner’s assertion for any limitation identified as a mental process under Step 2A, Prong 1 (pages 12-13). For this reason, this argument is not persuasive.
With respect to Step 2A, Prong 2, the applicant argues that the claims are not directed to an abstract idea because the claims are integrated into a practical application (page 14). To support this assessment, the applicant argues that the “claims provide for improvements in technology related to, for example, domain-adaptation to minimize the gap between simulated and real-world domains (page 14).” The applicant further states the claims “like those in Diehr and Enfish, provide specific technological improvement to a technological process (page 16).” The examiner respectfully disagrees.
At best, the claimed combination amounts to an improvement to the abstract idea rather than to an improvement on the functioning of a computer or to any other technology. See MPEP 2106.05(a). Specifically, with respect to independent claim 1, the claim recites the abstract idea (mental process) of:
extract…a first feature map… including first latent features corresponding to first object indicated in synthetic data and a second feature map… including second latent features corresponding to second objects indicated in real-world data, the first objects having object locations determined by sampling one or more probability distributions
detect a distributional discrepancy between the synthetic data and the real-world data at least on one or more discriminators determining respective classification of respective locations in the first feature map and the second feature map of whether the respective locations correspond to a real-world domain or a synthetic domain
at least one processor comprising: one or more circuits
The examiner acknowledges that the claim recites the additional elements considered under Step 2A, Prong 2, of at least one processor comprising one or more circuits, which are elements 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)).
Additionally, the examiner acknowledges that the claims recite the additional elements, considered under Step 2A, Prong 2:
using one or more first portions of one or more machine learning models (MLMs)
a latent representation space of the one or more MLMs
the latent representation space
update, using the respective classifications and one or more predictions generated from the first latent features and the second latent features by one or more second portions of the one or more MLM, one or more parameters of at least the one or more first portions 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))
In this instance, the arguments are directed toward the mental process and not a “technical field” nor a “technical problem” nor a “technical solution.” The argument conflates a mental process performed using a computer and machine learning model with a technological solution. For these reasons, this argument is not persuasive.
With respect to Step 2B, the applicant argues that “even if the claims were directed to an abstract idea, the claims recite features sufficient to ensure that the claims amount to significantly more” than the abstract idea (page 16). The applicant appears to rely upon the arguments presented with respect to Step 2A, Prong 2 (page 17). This argument is not persuasive for the rationale provided with respect to Step 2A, Prong 2 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Tremblay et al. (Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization): Discloses using simulated data to train models and evaluating the synthetic data to provide better synthetic data for training (Abstract)
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.
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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