Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 18, 25, and 30 are objected to because of the following informalities: “the simulated image data generated based on interaction between a virtual autonomous device and a virtual environment” should read “the simulated image data generated based on an interaction between a virtual autonomous device and a virtual environment”. Appropriate correction is required.
Claims 19-21, 23, 32-38 and 40-45 are further objected to for dependence, either directly or indirectly, on claims 18, 25, and 30.
Claims 20, 35, and 45 are objected to because of the following informalities: “based on first interaction” should read “based on a first interaction” and “based on second interaction” should read “based on a second interaction”. Appropriate correction is required.
Claims 21 and 36 are further objected to for dependence on claims 21 and 36, respectively.
Claim 45 is objected to because of the following informalities: “based on second interaction” should read “based on a second interaction”. Appropriate correction is required.
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 18-21, 23, 25, 30, 32-38, and 40-45 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 18,
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a method to generate training data for machine learning, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
The limitations:
“generating …first transformed image data based on simulated image data and a random noise input”
“based on the realness loss value and the distortion loss value, updating one or more coefficients of the generator neural network to increase conformance of second transformed image data to be generated by the generator neural network to the real-world depth image data and the simulated image data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
The limitations:
“computing, using a generative adversarial network (GAN) loss function, (a) a realness loss value based on a difference between the first transformed image data and real-world depth image data and (b) a distortion loss value representative of an amount of conformance of the first transformed image data to the simulated image data, the real-world depth image data measured by one or more first depth cameras in a real-world environment and including non-linear distortion”
As drafted, under their broadest reasonable interpretations, cover mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. The above limitations in the context of this claim correspond to mathematical calculations.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“with a generator neural network”
“the simulated image data generated based on interaction between a virtual autonomous device and a virtual environment, the virtual autonomous device controlled by a target neural network”
“the generator neural network to include the non-linear distortion of the real-world depth image data in the second transformed image data”
“with the second transformed image data generated by the generator neural network, training the target neural network to control the virtual autonomous device in the virtual environment”
“the target neural network to control a real-world autonomous device after training, the real-world autonomous device utilizing one or more second depth cameras that exhibit the non-linear distortion”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are mere instructions to apply an exception for the abstract ideas. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 19,
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a method to generate training data for machine learning, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“updating the one or more coefficients of the generator neural network based on feedback for the outputs”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“accessing outputs of the target neural network during the training of the target neural network”
As drafted, are additional elements that amount to no more than insignificant extra-
solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. As discussed above with
respect to the integration of the abstract idea into a practical application, all of the
additional elements are “insignificant extra-solution activity”. Furter, the accessing limitation recites the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Insignificant extra-solution activity cannot
provide an inventive concept. The claim is not patent eligible.
Regarding Claim 20,
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a method to generate training data for machine learning, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 18.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)) and mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the interaction between the virtual autonomous device and the virtual environment is first interaction”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“training the target neural network with the second transformed image data based on second interaction between the virtual autonomous device and the virtual environment”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way or are “mere instructions to apply”. The claim is not patent eligible.
Regarding Claim 21,
Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 21 is directed to a method to generate training data for machine learning, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 20.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)) and mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the real-world depth image data is first real-world depth image data”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“the training of the target neural network is to form a trained target neural network”
“operating the trained target neural network to control the real-world autonomous device”
“operating the real-world autonomous device in the real-world environment based on second real-world depth image data collected from the real-world environment by the one or more second depth cameras”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way or are “mere instructions to apply”. The claim is not patent eligible.
Regarding Claim 23,
Claim 23 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 23 is directed to a method to generate training data for machine learning, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 18.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the real-world autonomous device includes at least one of a robot, a self-driving car, or a drone”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 25,
Claim 25 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 25 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate first transformed image data based on simulated image data and a random noise input”
“based on the realness loss value and the distortion loss value, update one or more coefficients of the generator neural network to increase conformance of second transformed image data to be generated by the generator neural network to the real-world depth image data and the simulated image data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
The limitations:
“compute, using a generative adversarial network (GAN) loss function, (a) a realness loss value based on a difference between the first transformed image data and real-world depth image data and (b) a distortion loss value representative of an amount of conformance of the first transformed image data to the simulated image data, the real-world depth image data measured by one or more first depth cameras in a real-world environment and including non-linear distortion”
As drafted, under their broadest reasonable interpretations, cover mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. The above limitations in the context of this claim correspond to mathematical calculations.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“A computer system comprising: at least one processor circuit; and a memory containing computer-readable instructions that cause the at least one processor circuit to”
“train a generator neural network…”
“the simulated image data generated based on interaction between a virtual autonomous device and a virtual environment, the virtual autonomous device controlled by a target neural network”
“the generator neural network to include the non-linear distortion of the real-world depth image data in the second transformed image data”
“based on the second transformed image data generated by the generator neural network, train the target neural network to control the virtual autonomous device in the virtual environment”
“the target neural network to control be a real world autonomous device after training, the real-world autonomous device to utilize one or more second depth cameras that exhibit the non-linear distortion.”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are mere instructions to apply an exception for the abstract ideas. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 30,
Claim 30 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 30 is directed to a non-transitory computer-readable storage medium comprising instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate first transformed image data based on simulated image data and a random noise input”
“based on the realness loss value and the distortion loss value, update one or more coefficients of the generator neural network to increase conformance of second transformed image data to be generated by the generator neural network to the real-world depth image data and the simulated image data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
The limitations:
“compute, using a generative adversarial network (GAN) loss function, (a) a realness loss value based on a difference between the first transformed image data and real-world depth image data and (b) a distortion loss value representative of an amount of conformance of the first transformed image data to the simulated image data, the real-world depth image data measured by one or more first depth cameras in a real-world environment and including non-linear distortion”
As drafted, under their broadest reasonable interpretations, cover mathematical concepts, i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations. The above limitations in the context of this claim correspond to mathematical calculations.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“A non-transitory computer-readable storage medium comprising instructions that cause a machine to:...”
“train a generator neural network…”
“the simulated image data generated based on interaction between a virtual autonomous device and a virtual environment, the virtual autonomous device controlled by a target neural network”
“the generator neural network to include the non-linear distortion of the real-world depth image data in the second transformed image data”
“based on the second transformed image data generated by the generator neural network, train the target neural network to control the virtual autonomous device in the virtual environment”
“the target neural network to control be a real world autonomous device after training, the real-world autonomous device to utilize one or more second depth cameras that exhibit the non-linear distortion.”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are mere instructions to apply an exception for the abstract ideas. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 32,
Claim 32 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 32 is directed to a non-transitory computer-readable storage medium comprising instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate the simulated image data using a model of the real-world environment”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to a mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 30.
Step 2B Analysis: See corresponding analysis of claim 30.
Regarding Claim 33,
Claim 33 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 33 is directed to a non-transitory computer-readable storage medium comprising instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 30.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the virtual autonomous device is a virtualization of the real- world autonomous device”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 34,
Claim 34 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 34 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“update the one or more coefficients of the generator neural network based on feedback for the outputs”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“access outputs of the target neural network during the training of the target neural network”
As drafted, are additional elements that amount to no more than insignificant extra-
solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. As discussed above with
respect to the integration of the abstract idea into a practical application, all of the
additional elements are “insignificant extra-solution activity”. Furter, the accessing limitation recites the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Insignificant extra-solution activity cannot
provide an inventive concept. The claim is not patent eligible.
Regarding Claim 35,
Claim 35 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 35 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 25.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)) and mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the interaction between the virtual autonomous device and the virtual environment is first interaction”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“train the target neural network with the second transformed image data based on second interaction between the virtual autonomous device and the virtual environment”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way or are “mere instructions to apply”. The claim is not patent eligible.
Regarding Claim 36,
Claim 36 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 36 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 35.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)) and mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the real-world depth image data is first real-world depth image data”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“the training of the target neural network is to form a trained target neural network”
“operate the trained target neural network to control the real-world autonomous device”
“operate the real-world autonomous device in the real-world environment based on second real-world depth image data collected from the real-world environment by the one or more second depth cameras”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way or are “mere instructions to apply”. The claim is not patent eligible.
Regarding Claim 37
Claim 37 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 37 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 25.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“the virtual autonomous device is a virtualization of the real-world autonomous device”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 38,
Claim 38 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 38 is directed to a non-transitory computer-readable storage medium comprising instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“updating the one or more coefficients of the generator neural network based on feedback for the outputs”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“access outputs of the target neural network during the training of the target neural network”
As drafted, are additional elements that amount to no more than insignificant extra-
solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to
amount to significantly more than the judicial exception. As discussed above with
respect to the integration of the abstract idea into a practical application, all of the
additional elements are “insignificant extra-solution activity”. Furter, the accessing limitation recites the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Insignificant extra-solution activity cannot
provide an inventive concept. The claim is not patent eligible.
Regarding Claim 40,
Claim 40 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 40 is directed to a non-transitory computer-readable storage medium comprising instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 30.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)) and mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the real-world depth image data is first real-world depth image data”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“the training of the target neural network is to form a trained target neural network”
“operate the trained target neural network to control the real-world autonomous device”
“operate the real-world autonomous device in the real-world environment based on second real-world depth image data collected from the real-world environment by the one or more second depth cameras”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way or are “mere instructions to apply”. The claim is not patent eligible.
Regarding Claim 41,
Claim 41 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 41 is directed to a method to generate training data for machine learning, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating the simulated image data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“using a model of the real-world environment”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 42,
Claim 42 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 42 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generate the simulated image data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“using a model of the real-world environment”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 43,
Claim 43 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 43 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 25.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the real-world autonomous device includes at least one of a robot, a self-driving car, or a drone”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 44,
Claim 44 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 44 is directed to a non-transitory computer-readable storage medium comprising instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 30.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the real-world autonomous device includes at least one of a robot, a self-driving car, or a drone”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 45,
Claim 45 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 45 is directed to a non-transitory computer-readable storage medium comprising instructions, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 30.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are additional details that do not apply the exception in a meaningful way (See MPEP 2106.05(e)) and mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the interaction between the virtual autonomous device and the virtual environment is first interaction”
As drafted, are additional elements that do not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“train the target neural network with the second transformed image data based on second interaction between the virtual autonomous device and the virtual environment”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way or are “mere instructions to apply”. The claim is not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 18-21, 23, 25, 30, 32-38, and 40-45 are rejected under 35 U.S.C. 103 as being unpatentable over Bousmalis et al. (U.S. Patent Publication No. 2020/0279134) (“Bousmalis”) in view of Ignatov et al. (DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks) (“Ignatov”) in further view of Chen et al. (Socially Aware Motion Planning with Deep Reinforcement Learning) (“Chen”).
Regarding claim 18, Bousmalis teaches a method comprising: generating, with a generator neural network, first transformed image data based on simulated image data and a random noise input (Bousmalis [0056] “The generator neural network 120 training system 300 trains the generator neural network 120 so that the trained generator neural network 120 can be used in training the action selection neural network 130.”; [0057] “As described above, the generator neural network 120 is configured to process a simulation image in accordance with current values of the generator parameters to generate an adapted image that appears to be an image of the real-world environment.”; [0033] “For example, the system can modify the dynamics of the simulated environment by adding randomly sampled noise to the selected action command” Bousmalis provides generator neural network 120, which generates an adapted image, corresponding to first transformed image data based on a simulated image data and randomly sampled noise, corresponding to a random noise input.) the simulated image data generated based on interaction between a virtual autonomous device and a virtual environment (Bousmalis [0029] “As part of training the action selection neural network, the system 100 obtains a plurality of simulation training inputs. Each simulation training input includes one or more simulation images 102 of a simulated version of the real-world environment captured while a simulated version of the robotic agent interacts with the simulated version of the real-world environment to perform a simulated version of the robotic task.” Bousmalis provides simulation images, including the robotic agent interacts with the simulated version of the real-world environment to perform a simulated version of the robotic task, corresponding to generating the simulated image based on an interaction between a virtual autonomous device and a virtual environment.), the virtual autonomous device controlled by a target neural network (Bousmalis [0026] “To allow the action selection neural network to effectively be used to select actions to be performed by the robotic agent, the system or a separate system trains the action selection neural network to update the values of the action selection parameters.”; [0029] “As part of training the action selection neural network, the system 100 obtains a plurality of simulation training inputs. Each simulation training input includes one or more simulation images 102 of a simulated version of the real-world environment captured while a simulated version of the robotic agent interacts with the simulated version of the real-world environment to perform a simulated version of the robotic task.” Bousmalis provides the action selection neural network, corresponding to the target neural network, which also operates in the robotic agent in the simulated environment.) computing, using a generative adversarial network (GAN) loss function, (a) a realness loss value based on a difference between the first transformed image data and real-world depth image data (Bousmalis [0073] “The discriminator loss term measures how well the discriminator neural network 350 classifies input images, i.e., a term that has a high loss if the discriminator does not accurately classify an input image. For example, when a score of zero corresponds to an adapted image and a score of one corresponds to a real-world image, the discriminator loss term can be the difference between the likelihood output generated by the discriminator for the input image and the ground truth score for the input image.”; [0079] “The segmentation output 410 is one or more segmentation masks over the input simulated image 102 that segment the portions of the input simulated image 102 that belong to pre-determined categories, e.g., the background of the image, portions of the image that depict the robotic agent or other objects in the environment, and so on.” Bousmalis provides computing a discriminator loss corresponding to the realness loss value based on a difference between the transformed image training data and actual image input data, the actual input data measured in a real-world environment and corresponding to real-world data, wherein the image data analysis of the background of the image, corresponding to depth image data.) and (b) a distortion loss value representative of an amount of conformance of the first transformed image data to the simulated image data (Bousmalis [0068] “Optionally, the first loss function can include one or more additional terms. In particular, the first loss function can further include one or more content loss terms that penalize the generator neural network 120 for generating adapted images that are semantically different from the corresponding simulated images from which they are generated.” Bousmalis provides a content loss which measures a difference in generated adapted images and simulated images respectively corresponding to an amount of conformance of the transformed image data to the simulated image data, thus representing a distortion loss.) …based on the realness loss value and the distortion loss value, updating one or more coefficients of the generator neural network (Bousmalis [0058] “The system 300 trains the generator neural network 120 jointly with a discriminator neural network 350 and with a task-specific neural network that is configured to generate an output that is specific to the robotic task that the action selection neural network 130 is configured to perform.”; [0063] “In the first optimization step, the generator training engine 310 minimizes a first loss function with respect to the generator parameters. That is, in the first optimization step, the generator training engine 310 updates the generator parameters by backpropagating gradients of the first loss function into the generator neural network 120. In the second optimization step, the generator training engine 310 minimizes a second loss function with respect to the action selection parameters and the discriminator parameters.” Bousmalis provides updating generator parameters based on the loss values by backpropagating gradients of the first loss function into the generator neural network, corresponding to updating one or more coefficients of the generator neural network.) to increase conformance of second transformed image data to be generated by the generator neural network to the real-world depth image data and the simulated image data (Bousmalis [0088] “The system performs a first optimization step to update the generator parameters (step 604). In the first optimization step, the system minimizes a first loss function to cause the generator neural network to generate higher-quality adapted images as described above with reference to FIGS. 3 and 4.” Bousmalis provides updating the training data transformer to generate higher-quality adapted images, corresponding to increase conformance of the transformed image data.) …with the second transformed image data generated by the generator neural network, training the target neural network to control the virtual autonomous device in the virtual environment (Bousmalis [0029] “As part of training the action selection neural network, the system 100 obtains a plurality of simulation training inputs. Each simulation training input includes one or more simulation images 102 of a simulated version of the real-world environment captured while a simulated version of the robotic agent interacts with the simulated version of the real-world environment to perform a simulated version of the robotic task.”; [0049] “FIG. 2 is a diagram that shows an example architecture of the action selection neural network 130. In the example of FIG. 2, the action selection neural network 130 is configured to receive an input 202 that is either an adapted training input or a real-world training input. That is, the input 202 includes either (i) an initial adapted image x.sub.o.sup.f, a current adapted image x.sub.c.sup.f and a vector v.sub.c.sup.f, that represents a current simulated action (i.e., a vector of torques for the joints of the robotic agent) or (ii) an initial real-world image x.sub.o.sup.t, a current real-world image x.sub.c.sup.t, and a vector v.sub.c.sup.t that represents a current real-world action.” Bousmalis provides training the action selection neural network with the adapted image data generated by the generator neural network, including with the virtual (simulated) training environment.), the target neural network to control a real-world autonomous device after training (Bousmalis [0028] “As described above, the system 100 trains an action selection neural network 130 to allow the neural network 130 to effectively be used to select actions to be performed by a robotic agent interacting with a real-world environment, i.e., so that actions selected using the neural network 130 cause the robotic agent to successfully perform the robotic task.” Bousmalis provides the action selection neural network to control the robotic agent, corresponding to the target neural network to control a real-world autonomous device after training.)
Bousmalis fails to teach …the real-world depth image data measured by one or more first depth cameras in a real-world environment and including non-linear distortion … the generator neural network to include the non-linear distortion of the real-world depth image data in the second transformed image data …the real world autonomous device utilizing one or more second depth cameras that exhibit the non-linear distortion.
However, Ignatov teaches …the generator neural network to include the non-linear distortion of the real-world depth image data in the second transformed image data (Ignatov Section 3.1 Loss Function “The main difficulty of the image enhancement task is that input and target photos cannot be matched densely (i.e., pixel-to-pixel): different optics and sensors cause specific local non-linear distortions and aberrations, leading to a non-constant shift of pixels between each image pair even after precise alignment.” Section 3.1.2 Texture loss “Instead of using a pre-defined loss function, we build upon generative adversarial networks (GANs) [5] to directly learn a suitable metric for measuring texture quality. The discriminator CNN is applied to grayscale images so that it is targeted specifically on texture processing. It observes both fake (improved) and real (target) images, and its goal is to predict whether the input image is real or not. It is trained to minimize the cross-entropy loss function, and the texture loss is defined as a standard generator objective:” Ignatov provides training a generator neural network including using loss functions with data that includes non-linear distortion of a camera sensor, corresponding to the generator neural network include the non-linear distortion of the real-world depth image data in the second transformed image data.).
Bousmalis and Ignatov are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically generative adversarial networks. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bousmalis with the above teachings of Ignatov. Doing so would allow for a photo enhancement solution to effectively increase the quality of image generation (Ignatov Section 5. Conclusions “We proposed a photo enhancement solution to effectively transform cameras from common smartphones into high quality DSLR cameras.”).
Further, Chen teaches …the real-world depth image data measured by one or more first depth cameras in a real-world environment and including non-linear distortion (Chen Section IV Results “The SA-CADRL policy is implemented on a robotic vehicle for autonomous navigation in an indoor environment with many pedestrians, as shown in Fig. 1. The differential-drive vehicle is outfitted with a Lidar for localization, three Intel Realsenses for free space detection, and four webcams for pedestrian detection.” Chen provides use of an Intel RealSense camera in an indoor environment, corresponding to a real-world environment, which is a depth camera that includes non-linear distortions.) …the real world autonomous device utilizing one or more second depth cameras that exhibit the non-linear distortion (Chen Section IV Results “The SA-CADRL policy is implemented on a robotic vehicle for autonomous navigation in an indoor environment with many pedestrians, as shown in Fig. 1. The differential-drive vehicle is outfitted with a Lidar for localization, three Intel Realsenses for free space detection, and four webcams for pedestrian detection.” Chen provides an autonomous robotic vehicle that utilizes three Intel RealSense cameras, which are depth cameras that exhibit non-linear distortion.).
Bousmalis, Ignatov, and Chen are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically neural network training using images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bousmalis in view of Ignatov with the above teachings of Chen. Doing so would enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians (Chen Abstract “The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.”).
Regarding claim 19, Bousmalis in view of Ignatov in further view of Chen teaches accessing outputs of the target neural network during the training of the target neural network (Bousmalis [0036] “The target output 104 for a given training input is an output that should be generated by the action selection neural network 130 by processing the training input. When the output of the action selection neural network 130 is a likelihood that the task will successfully be completed, the target output for a given training input identifies whether the task was successfully completed after the action in the training input was performed.”; [0041] “The features 132 of a given training input are generally the outputs of a predetermined layer of the action selection neural network 130. Thus, as will be described in more detail below with reference to FIG. 2, the domain classifier neural network 140 receives as input features of a training input that are generated by the action selection neural network 130 and predicts, from the features, whether the features are features of a real-world input or a simulation training input.” Bousmalis provides receiving/accessing outputs of the target neural network during the training.); and updating the one or more coefficients of the generator neural network based on feedback for the outputs (Bousmalis [0058] “The system 300 trains the generator neural network 120 jointly with a discriminator neural network 350 and with a task-specific neural network that is configured to generate an output that is specific to the robotic task that the action selection neural network 130 is configured to perform.”; [0063] “In the first optimization step, the generator training engine 310 minimizes a first loss function with respect to the generator parameters. That is, in the first optimization step, the generator training engine 310 updates the generator parameters by backpropagating gradients of the first loss function into the generator neural network 120. In the second optimization step, the generator training engine 310 minimizes a second loss function with respect to the action selection parameters and the discriminator parameters.” Bousmalis provides updating generator parameters based on the loss values by backpropagating gradients of the first loss function into the generator neural network, corresponding to updating one or more coefficients of the generator neural network based on feedback for the outputs.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bousmalis in view of Ignatov in further view of Chen for the same reasons disclosed above in the rejection of claim 18.
Regarding claim 20, Bousmalis in view of Ignatov in further view of Chen teaches wherein the interaction between the virtual autonomous device and the virtual environment is first interaction (Bousmalis [0029] “Each simulation training input includes one or more simulation images 102 of a simulated version of the real-world environment captured while a simulated version of the robotic agent interacts with the simulated version of the real-world environment to perform a simulated version of the robotic task.” Bousmalis provides a simulated version of the robotic agent interacts with the simulated version of the real-world environment, corresponding to a first interaction between the virtual device and the virtual environment), and the method further includes training the target neural network with the second transformed image data based on second interaction between the virtual autonomous device and the virtual environment (Bousmalis [0029] “As part of training the action selection neural network, the system 100 obtains a plurality of simulation training inputs. Each simulation training input includes one or more simulation images 102 of a simulated version of the real-world environment captured while a simulated version of the robotic agent interacts with the simulated version of the real-world environment to perform a simulated version of the robotic task.”; [0049] “FIG. 2 is a diagram that shows an example architecture of the action selection neural network 130. In the example of FIG. 2, the action selection neural network 130 is configured to receive an input 202 that is either an adapted training input or a real-world training input. That is, the input 202 includes either (i) an initial adapted image x.sub.o.sup.f, a current adapted image x.sub.c.sup.f and a vector v.sub.c.sup.f, that represents a current simulated action (i.e., a vector of torques for the joints of the robotic agent) or (ii) an initial real-world image x.sub.o.sup.t, a current real-world image x.sub.c.sup.t, and a vector v.sub.c.sup.t that represents a current real-world action.” Bousmalis provides training the action selection neural network with the adapted image data generated by the generator neural network, including with the virtual (simulated) training environment.)
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bousmalis in view of Ignatov in further view of Chen for the same reasons disclosed above in the rejection of claim 18.
Regarding claim 21, Bousmalis in view of Ignatov in further view of Chen teaches wherein the real-world depth image data is first real-world depth image data (Bousmalis [0079] “The segmentation output 410 is one or more segmentation masks over the input simulated image 102 that segment the portions of the input simulated image 102 that belong to pre-determined categories, e.g., the background of the image, portions of the image that depict the robotic agent or other objects in the environment, and so on.” Bousmalis provides real-world image data including background of the image for robotic agents in an environment, corresponding to real-world depth image data.), the training of the target neural network is to form a trained target neural network (Bousmalis [0028] “As described above, the system 100 trains an action selection neural network 130 to allow the neural network 130 to effectively be used to select actions to be performed by a robotic agent interacting with a real-world environment, i.e., so that actions selected using the neural network 130 cause the robotic agent to successfully perform the robotic task.” Bousmalis provides training the action selection neural network 130 corresponding to a target neural network to form a trained target neural network.), and the method includes: operating the trained target neural network to control the real-world autonomous device (Bousmalis [0048] “Once the action selection neural network 130 has been trained, the system 100 can use the trained neural network to select actions to be performed by the robotic agent to effectively interact with the real-world environment or can output data specifying the trained neural network for later use, i.e., by the system 100 or another system, in controlling the robotic agent. The robotic agent may then implement these selections.” Bousmalis provides operating the neural network 130 corresponding to the target neural network in a robotic agent corresponding to a real-world autonomous device.);
Further, Chen teaches and operating the real-world autonomous device in the real-world environment based on second real-world depth image data collected from the real-world environment by the one or more second depth cameras (Chen Section IV Results “The SA-CADRL policy is implemented on a robotic vehicle for autonomous navigation in an indoor environment with many pedestrians, as shown in Fig. 1. The differential-drive vehicle is outfitted with a Lidar for localization, three Intel Realsenses for free space detection, and four webcams for pedestrian detection.” Chen provides an autonomous robotic vehicle that utilizes three Intel RealSense cameras, which are depth cameras that exhibit non-linear distortion, corresponding to one or more second depth cameras for collecting real-world images to operate a robotic autonomous agent.).
Bousmalis, Ignatov, and Chen are all considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically neural network training using images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bousmalis in view of Ignatov with the above teachings of Chen. Doing so would enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians (Chen Abstract “The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.”).
Regarding claim 23, Bousmalis in view of Ignatov in further view of Chen teaches wherein the real-world device includes at least one of a robot, a self-driving car, or a drone (Bousmalis [0022] “In particular, the system selects the actions so that the robotic agent can successfully perform a robotic task, e.g., an object grasping task, an object moving task, a navigation task, or another task that requires the agent to interact with the real-world environment for some specific purpose.” Bousmalis provides a robotic agent corresponding to the real-world device includes at least one of a robot.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bousmalis in view of Ignatov in further view of Chen for the same reasons disclosed above in the rejection of claim 18.
Regarding claim 25, it is the computer system embodiment of claim 18 with similar limitations to claim 18 and is rejected using the same reasoning found above in the rejection of claim 18. Further, Bousmalis teaches a computer system comprising: at least one processor circuit (Bousmalis [0049] “The term “data processing apparatus” refers to data processing hardware and encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers.” Bousmalis provides a processor.); and a memory containing computer-readable instructions (Bousmalis [0092] “For one or more computer programs to be configured to perform particular operations or actions means that the one or more programs include instructions that, when executed by data processing apparatus, cause the apparatus to perform the operations or actions.” Bousmalis provides a memory containing computer-readable instructions.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bousmalis in view of Ignatov in further view of Chen for the same reasons disclosed above in the rejection of claim 18.
Regarding claim 30, it is the non-transitory computer readable storage medium embodiment of claim 18 with similar limitations to claim 18 and is rejected using the same reasoning discussed above in the rejection of claim 18. Further, Bousmalis teaches a non-transitory computer-readable storage medium comprising instructions (Bousmalis [0093] “Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non transitory storage medium for execution by, or to control the operation of, data processing apparatus.” Bousmalis provides a non-transitory computer-readable storage medium comprising instructions.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bousmalis in view of Ignatov in further view of Chen for the same reasons disclosed above in the rejection of claim 18.
Regarding claim 32, Bousmalis in view of Ignatov in further view of Chen teaches wherein the instructions cause the machine to generate the simulated image data using a model of the real-world environment (Bousmalis [0033] “In other cases, however, the system 100 executes the simulation, i.e., as one or more computer programs, and collects the simulation training inputs as a result of executing the simulation. In these cases, the system 100 can utilize various techniques to improve the usefulness of the simulation training data in training the action selection neural network 130.”; [0034] “The system 100 also obtains a plurality of real-world training inputs. Each real-world training input includes one or more real-world images 108 of the real-world environment…” Bousmalis provides generating simulated image data using a model of a real-world environment.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bousmalis in view of Ignatov in further view of Chen for the same reasons disclosed above in the rejection of claim 30.
Regarding claim 33, Bousmalis in view of Ignatov in further view of Chen teaches wherein the virtual autonomous device is a virtualization of the real-world autonomous device (Bousmalis [0028] “As described above, the system 100 trains an action selection neural network 130 to allow the neural network 130 to effectively be used to select actions to be performed by a robotic agent interacting with a real-world environment, i.e., so that actions selected using the neural network 130 cause the robotic agent to successfully perform the robotic task.”; [0033] “In these cases, the system 100 can utilize various techniques to improve the usefulness of the simulation training data in training the action selection neural network 130” Bousmalis provides a simulating the robotic agent corresponding to a real-world autonomous device in a virtual environment.).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bousmalis in view of Ignatov in further view of Chen for the same reasons disclosed above in the rejection of claim 30.
Regarding claim 34, the rejection of claim 25 is incorporated herein. Further, the limitations in this claim are taught by Bousmalis in view of Ignatov in further view of Chen for the same reasons discussed above in the rejection of claim 19.
Regarding claim 35, the rejection of claim 25 is incorporated herein. Further, the limitations in this claim are taught by Bousmalis in view of Ignatov in further view of Chen for the same reasons discussed above in the rejection of claim 20.
Regarding claim 36, the rejection of claim 35 is incorporated herein. Further, the limitations in this claim are taught by Bousmalis in view of Ignatov in further view of Chen for the same reasons discussed above in the rejection of claim 21.
Regarding claim 37, the rejection of claim 25 is incorporated herein. Further, the limitations in this claim are taught by Bousmalis in view of Ignatov in further view of Chen for the same reasons discussed above in the rejection of claim 33.
Regarding claim 38, the rejection of claim 30 is incorporated herein. Further, the limitations in this claim are taught by Bousmalis in view of Ignatov in further view of Chen for the same reasons discussed above in the rejection of claim 19.
Regarding claim 40, the rejection of claim 30 is incorporated herein. Further, the limitations in this claim are taught by Bousmalis in view of Ignatov in further view of Chen for the same reasons discussed above in the rejection of claim 21.
Regarding claim 41, the rejection of claim 18 is incorporated herein. Further, the limitations in this claim are taught by Bousmalis in view of Ignatov in further view of Chen for the same reasons discussed above in the rejection of claim 32.
Regarding claim 42, the rejection of claim 25 is incorporated herein. Further, the limitations in this claim are taught by Bousmalis in view of Ignatov in further view of Chen for the same reasons discussed above in the rejection of claim 32.
Regarding claim 43, the rejection of claim 25 is incorporated herein. Further, the limitations in this claim are taught by Bousmalis in view of Ignatov in further view of Chen for the same reasons discussed above in the rejection of claim 23.
Regarding claim 44, the rejection of claim 30 is incorporated herein. Further, the limitations in this claim are taught by Bousmalis in view of Ignatov in further view of Chen for the same reasons discussed above in the rejection of claim 23.
Regarding claim 45, the rejection of claim 30 is incorporated herein. Further, the limitations in this claim are taught by Bousmalis in view of Ignatov in further view of Chen for the same reasons discussed above in the rejection of claim 20.
Response to Arguments
Regarding the rejection applied under 35 U.S.C. 101, Applicant firstly asserts that amended claim 18 is not directed to an abstract idea, and requires concrete operations performed by neural networks using real-world sensor data obtained from depth cameras, and training a target neural network for control of a real-world autonomous device, which Applicant asserts cannot practically be performed in the human mind (“Remarks”, Page 10).
However, the claims recite at least the abstract idea of “based on the realness loss value and the distortion loss value, updating one or more coefficients of the generator neural network to increase conformance of second transformed image data…”, as discussed above in the 35 U.S.C. 101 rejection of claim 1 above.
Applicant further asserts that even assuming the claims do recite abstract ideas, the claims are integrated into a practical application by improving the technical process of training an autonomous device to operate in the real world using sensor-realistic training data, which is similar to the claims in Desjardins (“Remarks”, Page 10).
However, even assuming the claims did recite an improvement, it would be an improvement in the abstract idea of updating coefficients of the generator neural network to increase conformance…”. The MPEP notes that it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a)(II). Further, Desjardins includes an improvement related to catastrophic forgetting while the current claims only use machine learning models at a high level to perform abstract ideas. Therefore, the claims remain rejected under 35 U.S.C. 101.
Regarding the rejection applied under 35 U.S.C. 103, Applicant’s arguments with respect to claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KURT NICHOLAS PRESSLY whose telephone number is (703)756-4639. The examiner can normally be reached M-F 8-4.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KURT NICHOLAS PRESSLY/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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