DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to preliminary amendments and remarks filed on 01/05/2024. In the current amendments, the specification is amended, the abstract is amended, claims 1-11 are amended, and claim 12 is newly presented. Claims 1-12 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/05/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because unlabeled boxes 41-43 in Fig. 2 and S1-S10 in Fig. 4 should be provided with descriptive labels. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
In claim 10, lines 1-2, “a computer readable medium” lacks proper antecedent basis support in the specification.
In claim 11, line 1, “machine readable medium” lacks proper antecedent basis support in the specification.
Claim Objections
Claim 12 is objected to because of the following informalities:
In claim 12, line 2, “an average score metrics” should read “an average scoring metric”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the training data” in line 4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the training data” has been interpreted as “training data”.
Claim 9 recites the limitation “the training data” in line 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the training data” has been interpreted as “training data”.
Claim 10 recites the limitation “said program” in line 2. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “said program” has been interpreted as “said computer program code” in reference to “computer program code” in line 2.
Claim 10 recites the limitation “the computer” in line 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the computer” has been interpreted as “a computer”.
Claim 11 recites the limitation “the computer” in line 2. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the computer” has been interpreted as “a computer”.
Dependent claims 2-8 and 10-12 are rejected based on being directly or indirectly dependent on rejected claim 1.
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.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because, under its broadest reasonable interpretation in light of the specification, the claim is directed to software per se.
Regarding claim 9, the claim is directed to a “device”. However, the specification does not define the “device” as hardware, and a processor and memory are not recited anywhere in the specification. Thus, the broadest reasonable interpretation of “device” in light of the specification encompasses software devices.
Claims 10 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims could be considered signals per se.
Independent claim 10 recites “computer program product comprising a computer readable medium.” The broadest reasonable interpretation of a claim that recites "computer readable medium," in view of the present specification, covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable medium, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. § 101, Aug. 24, 2009; p. 2. 1351 Off. Gaz. Pat. Off. 212 (2010). Under broadest reasonable interpretation, "computer readable medium" recited in claim 10 encompasses a transitory, propagating signal, which is not a process, machine, manufacture, or composition of matter. Nuijten, 500 F.3d at 1357. The claim "covers material not found in any of the four statutory categories [and thus] falls outside the plainly expressed scope of § 101." Id. at 1354. A recommended amendment is to recite “non-transitory computer readable medium” (emphasis added).
Independent claim 11 recites “machine readable medium.” The broadest reasonable interpretation of a claim that recites "machine readable medium," in view of the present specification, covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of machine readable medium, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. § 101, Aug. 24, 2009; p. 2. 1351 Off. Gaz. Pat. Off. 212 (2010). Under broadest reasonable interpretation, "machine readable medium" recited in claim 11 encompasses a transitory, propagating signal, which is not a process, machine, manufacture, or composition of matter. Nuijten, 500 F.3d at 1357. The claim "covers material not found in any of the four statutory categories [and thus] falls outside the plainly expressed scope of § 101." Id. at 1354. A recommended amendment is to recite “non-transitory machine readable medium” (emphasis added).
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 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 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating synthetic training data for training of a data-driven ultrasonic sensor model for a given configuration of an ultrasonic sensor system having multiple ultrasonic sensor devices, wherein the training data includes input data representing time-series data of received ultrasonic signals and output data indicating object characteristics of environmental objects in a sensing range of the ultrasonic sensor system”
“generate the synthetic training data by applying a random noise vector as input”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass generating synthetic training data for training a sensor model, the training data including inputs representing time-series data of sensor signals and the outputs indicating object characteristics of environmental objecting in a sensing range of the sensor system (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate synthetic data including time-series sensor signal inputs and environmental object characteristic outputs for training a sensor model); and generating synthetic training data by applying a random noise vector as input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can apply a random noise vector to generate synthetic training data).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“computer”
“a data-driven ultrasonic sensor model”
“an ultrasonic sensor system having multiple ultrasonic sensor devices”
“training a generator model with a training model using the real training data”
“using the generator model”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“providing real training data obtained by a measurement of the given configuration of an ultrasonic sensor system”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, sensors, models, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2,
Claim 2 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 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 1. The limitations of claim 2 are only additional elements to the abstract ideas of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“wherein the generator model is trained by applying as the training model a GAN model comprising the generator model and a discriminator model which are adversarially trained”
“the generator model is trained by applying as the training model a Variational Autoencoder wherein a decoder portion of the Variational Autoencoder forms the generator model”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 1 of a generic computer, sensors, models, and generic model training are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “providing …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, sensors, models, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 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 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the training model for obtaining the generator model is selected from a plurality of given training models depending on one or more scoring metrics”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass selecting the training model from a plurality of given training models based on one or more scoring metrics (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use one or more scoring metrics to select the training model from a plurality of given training models).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computer, sensors, models, and generic model training are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “providing …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, sensors, models, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 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 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the plurality of given training models include at least one of a variational autoencoder, a Conditional GAN model, and a CopulaGAN model”
As drafted, is part of the abstract idea of claim 3 of selecting from a plurality of given training models. The limitation of claim 4 further limits the limitation of claim 3 by further defining what the plurality of given training models comprises. The above limitation in the context of this claim encompasses selecting the training model from a plurality of given training models based on one or more scoring metrics, the given training models including at least one of a variational autoencoder, a Conditional GAN model, and a CopulaGAN model (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use one or more scoring metrics to select the training model from a plurality of given training models including at least one of a variational autoencoder, a Conditional GAN model, and a CopulaGAN model).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 3 of a generic computer, sensors, models, and generic model training are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “providing …” limitation of claim 3 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, sensors, models, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 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 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the one or more scoring metrics include at least one of: a statistical metric, a detection metric, and a likelihood metric”
As drafted, is part of the abstract idea of claim 3 of selecting from a plurality of given training models. The limitation of claim 5 further limits the limitation of claim 3 by further defining what the one or more scoring metrics comprise. The above limitation in the context of this claim encompasses selecting the training model from a plurality of given training models based on one or more scoring metrics, the one or more scoring metrics including at least one of a statistical metric, a detection metric, and a likelihood metric (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use one or more scoring metrics including at least one of a statistical metric, a detection metric, and a likelihood metric to select the training model from a plurality of given training models).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 3 of a generic computer, sensors, models, and generic model training are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “providing …” limitation of claim 3 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, sensors, models, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 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 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 1. The limitations of claim 6 are only additional elements to the abstract ideas of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“wherein the synthetic training data is used to train the ultrasonic sensor model in combination with the real training data”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 1 of a generic computer, sensors, models, and generic model training are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “providing …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, sensors, models, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 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 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 6. The limitations of claim 7 are only additional elements to the abstract ideas of claim 6.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“wherein the ultrasonic sensor model is formed as an artificial neural network or a gradient boosting model”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 6 of a generic computer, sensors, models, and generic model training are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “providing …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, sensors, models, neural network, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 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 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein training data is given as tabular data”
As drafted, is part of the abstract idea of claim 1 of generating synthetic training data, wherein the training data including time-series inputs and object characteristics outputs. The limitation of claim 8 further limits the limitation of claim 1 by further defining what the training data comprising. The above limitation in the context of this claim encompasses generating synthetic training data for training a sensor model, the training data being tabular data including inputs representing time-series data of sensor signals and the outputs indicating object characteristics of environmental objecting in a sensing range of the sensor system (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate synthetic data as tabular data including time-series sensor signal inputs and environmental object characteristic outputs for training a sensor model).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computer, sensors, models, and generic model training are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “providing …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, sensors, models, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 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 9, as noted above, is directed towards non-statutory subject matter as software per se. However, for purposes of this rejection, it will be assumed that the “device” is a hardware device and that the claim is therefore directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating synthetic training data for training of a data-driven ultrasonic sensor model for a given configuration of an ultrasonic sensor system having multiple ultrasonic sensor devices, wherein the training data includes input data representing time-series data of received ultrasonic signals and output data indicating object characteristics of environmental objects in a sensing range of the ultrasonic sensor system”
“generate the synthetic training data by applying a random noise vector as input”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass generating synthetic training data for training a sensor model, the training data including inputs representing time-series data of sensor signals and the outputs indicating object characteristics of environmental objecting in a sensing range of the sensor system (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate synthetic data including time-series sensor signal inputs and environmental object characteristic outputs for training a sensor model); and generating synthetic training data by applying a random noise vector as input (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can apply a random noise vector to generate synthetic training data).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“a data-driven ultrasonic sensor model”
“an ultrasonic sensor system having multiple ultrasonic sensor devices”
“train a generator model with a training model using the real training data”
“use the generator model”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“provide real training data obtained by a measurement of the given configuration of an ultrasonic sensor system”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic, sensors, models, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 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 10, as noted above, is directed towards non-statutory subject matter as signals per se. However, for purposes of this rejection, it will be assumed that the claim is directed to a non-transitory computer readable medium that stores the computer program product and that the claim is therefore directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 1. The limitations of claim 10 are only additional elements to the abstract ideas of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“A computer program product comprising a computer readable medium, having thereon computer program code”
“the computer execute procedures to perform the method according to claim 1”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 1 of a generic computer, sensors, models, and generic model training are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “providing …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, computer readable medium, sensors, models, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 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 11, as noted above, is directed towards non-statutory subject matter as signals per se. However, for purposes of this rejection, it will be assumed that the claim is directed to a non-transitory medium readable medium that stores the program and that the claim is therefore directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 1. The limitations of claim 11 are only additional elements to the abstract ideas of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“A machine readable medium, having a program recorded thereon”
“the computer execute the method according to claim 1”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 1 of a generic computer, sensors, models, and generic model training are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “providing …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, machine readable medium, sensors, models, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 12,
Claim 12 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 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the one or more scoring metrics comprises an average scoring metrics of multiple scoring metrics”
As drafted, is part of the abstract idea of claim 3 of selecting from a plurality of given training models. The limitation of claim 12 further limits the limitation of claim 3 by further defining what the one or more scoring metrics comprise. The above limitation in the context of this claim encompasses selecting the training model from a plurality of given training models based on one or more scoring metrics, the one or more scoring metrics comprising an average scoring metric of multiple scoring metrics (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use one or more scoring metrics an average scoring metric to select the training model from a plurality of given training models).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 3 of a generic computer, sensors, models, and generic model training are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “providing …” limitation of claim 3 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. 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 integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, sensors, models, and generic model training for applying the abstract ideas) or insignificant extra-solution activity (i.e. providing/transmitting data). Furthermore, the “providing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. 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 1, 6, 7, and 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Mustikovela et al. (US 2021/0150757 A1) in view of Pöpperli et al. ("Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks").
Regarding Claim 1,
Mustikovela et al. teaches a computer-implemented method for generating synthetic training data for training of a data-driven ultrasonic sensor model for a given configuration of an ultrasonic sensor system having multiple ultrasonic sensor devices (Fig. 13; [0151]: "FIG. 13 shows an illustrative example of a process 1300 to train a neural network to predict a viewpoint of an object within an image, in accordance with at least one embodiment. In at least one embodiment, some or all of process 1300 (or any other processes described herein, or variations and/or combinations thereof) is performed under control of one or more computer systems configured with computer-executable instructions" teaches a process (method) performed by a computer system (computer-implemented) for training a neural network (sensor model) to predict objects within images. [0157]-[0159]: "In at least one embodiment, one or more neural networks is trained in a self-supervised manner on a collection of images of different objects of a same category as an object of an image to be inferred. In at least one embodiment, different objects of a same category may refer to different images which may be one or more images of a first car at one or more orientations, one or more images of a different second car at one or more orientations, and so on. In at least one embodiment, an image of an object to be inferred is included in a collection of images used to train one or more neural networks to inference orientations. In at least one embodiment, one or more neural networks are trained in a self-supervised manner by at least using a set of loss functions to evaluate one or more characteristics of objects within images … a neural network trained in a self-supervised manner is trained to generate synthetic images of objects with a specific orientation, which may be a same orientation as a predicted orientation of an input image. In at least one embodiment, a synthetic image is created using a deep generative model such as a variational autoencoder (VAE), differentiable renderer, generative adversarial network (GAN), or a renderer … In at least one embodiment, one or more neural networks are trained on a collection of images of cars and is used to infer orientations of cars captured in real-time by a camera or other suitable video/image capture device attached to a vehicle" teaches capturing real images (real training data) of an object using a capture device attached to a vehicle and generating synthetic images (synthetic training data) for training the neural network (sensor model). [0300]: "In at least one embodiment, vehicle 2100 may further include ultrasonic sensor(s) 2162. Ultrasonic sensor(s) 2162, which may be positioned at front, back, and/or sides of vehicle 2100, may be used for park assist and/or to create and update an occupancy grid. In at least one embodiment, a wide variety of ultrasonic sensor(s) 2162 may be used, and different ultrasonic sensor(s) 2162 may be used for different ranges of detection" teaches that the capture device attached to a vehicle include multiple ultrasonic sensors (ultrasonic sensor devices)), … comprising:
providing real training data obtained by a measurement of the given configuration of an ultrasonic sensor system ([0157]-[0159]: "In at least one embodiment, one or more neural networks is trained in a self-supervised manner on a collection of images of different objects of a same category as an object of an image to be inferred. In at least one embodiment, different objects of a same category may refer to different images which may be one or more images of a first car at one or more orientations, one or more images of a different second car at one or more orientations, and so on. In at least one embodiment, an image of an object to be inferred is included in a collection of images used to train one or more neural networks to inference orientations. In at least one embodiment, one or more neural networks are trained in a self-supervised manner by at least using a set of loss functions to evaluate one or more characteristics of objects within images … a neural network trained in a self-supervised manner is trained to generate synthetic images of objects with a specific orientation, which may be a same orientation as a predicted orientation of an input image. In at least one embodiment, a synthetic image is created using a deep generative model such as a variational autoencoder (VAE), differentiable renderer, generative adversarial network (GAN), or a renderer … In at least one embodiment, one or more neural networks are trained on a collection of images of cars and is used to infer orientations of cars captured in real-time by a camera or other suitable video/image capture device attached to a vehicle" teaches capturing and providing real images (real training data) of an object using a capture device attached to a vehicle. [0300]: "In at least one embodiment, vehicle 2100 may further include ultrasonic sensor(s) 2162. Ultrasonic sensor(s) 2162, which may be positioned at front, back, and/or sides of vehicle 2100, may be used for park assist and/or to create and update an occupancy grid. In at least one embodiment, a wide variety of ultrasonic sensor(s) 2162 may be used, and different ultrasonic sensor(s) 2162 may be used for different ranges of detection" teaches that the capture device attached to a vehicle include multiple ultrasonic sensors (ultrasonic sensor devices)).
Mustikovela et al. does not appear to explicitly teach wherein the training data includes input data representing time-series data of received ultrasonic signals and output data indicating object characteristics of environmental objects in a sensing range of the ultrasonic sensor system, training a generator model with a training model using the real training data; and using the generator model to generate the synthetic training data by applying a random noise vector as input.
However, Pöpperli et al. teaches wherein the training data includes input data representing time-series data of received ultrasonic signals and output data indicating object characteristics of environmental objects in a sensing range of the ultrasonic sensor system (Fig. 4; Section II. C, first paragraph: "C. Measurement Results … Two samples of the measurements are shown in Fig. 4. Each signal consists of 9,900 samples with a sampling frequency of 330 kHz. Oversampling is applied to handle Doppler shift in dynamic scenarios. At the beginning, the reverberation time of the membrane is clearly visible. This is typical for a monostatic ultrasonic setup. Subsequently, the ground reflections appear. The ground reflections vary by changing the measurement setup and the ground type. By comparing Fig. 4(a) and (b), the differences in the ground reflections become clear. Fig. 4(a) shows a typical signal on asphalt, whereas Fig. 4(b) is typical for gravel" teaches collecting measurement results (training data) for ultrasonic sensor signals as input data time-series with output data indicating characteristics (object characteristics) of the ground (environmental object) in the sensing range of the ultrasonic sensor (ultrasonic sensor system)),
training a generator model with a training model using the real training data (Fig 9; Section IV. A, second paragraph: "During training the generator tries to generate realistic ultrasonic signals based on the random noise vector and the given condition, as the discriminator is trained to distinguish the generated from real measurements. While the generator tries to fool the discriminator with more and more realistic signals, the capability of the discriminator to detect generated signals also increases. Thus, the aim of the training is to minimize the loss of the generator and to maximize the loss of the discriminator" teaches training the generator model with a GAN model (training model) using the measured data (real training data)); and
using the generator model to generate the synthetic training data by applying a random noise vector as input (Fig 9; Section IV. A, first-second paragraphs: "An overview of the used network architecture is shown in Fig. 9. To train the cGAN, two separate neural networks are used. Of course a generative neural network G is necessary. We use a simple multilayer perceptron (MLP) for this purpose. The network consists of 4 fully connected layers that use a leaky rectified linear unit (ReLU) as activation function and a fully connected output layer with a hyperbolic tangent as activation function. The network takes a random noise vector z and a conditional vector x as input … The discriminative network D that is needed for training is also a MLP that consists of 3 fully connected layers using a leaky ReLU activation function and a fully connected output layer with a SoftMax activation … During training the generator tries to generate realistic ultrasonic signals based on the random noise vector and the given condition, as the discriminator is trained to distinguish the generated from real measurements. While the generator tries to fool the discriminator with more and more realistic signals, the capability of the discriminator to detect generated signals also increases. Thus, the aim of the training is to minimize the loss of the generator and to maximize the loss of the discriminator" teaches that the generator model generates realistic ultrasonic signals (synthetic training data) by applying a random noise vector as input).
Mustikovela et al. and Pöpperli et al. are analogous to the claimed invention because they are directed towards the use of generative adversarial networks (GAN) for generating synthetic ultrasonic sensor data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the training data includes input data representing time-series data of received ultrasonic signals and output data indicating object characteristics of environmental objects in a sensing range of the ultrasonic sensor system, training a generator model with a training model using the real training data; and using the generator model to generate the synthetic training data by applying a random noise vector as input as taught by Pöpperli et al. to the disclosed invention of Mustikovela et al.
One of ordinary skill in the art would have been motivated to make this modification "to bring the environment simulation to a high quality close to realistic data ... the proposed approach is flexible to external influences" and provides "low complexity and smaller time effort needed for data generation" (Pöpperli et al. Abstract).
Regarding Claim 6,
Mustikovela et al. in view of Pöpperli et al. teaches the method according to claim 1.
In addition, Mustikovela et al. further teaches wherein the synthetic training data is used to train the ultrasonic sensor model in combination with the real training data (Fig. 13; [0151]-[0152]: "FIG. 13 shows an illustrative example of a process 1300 to train a neural network to predict a viewpoint of an object within an image, in accordance with at least one embodiment. In at least one embodiment, some or all of process 1300 (or any other processes described herein, or variations and/or combinations thereof) is performed under control of one or more computer systems configured with computer-executable instructions … a collection of one or more images is used to train one or more neural networks to identify orientations of objects within images …a collection of images is a collection of images of cars that can include different types of cars at different orientations, in different weather, under different lighting. In at least one embodiment, a collection of cars includes images of same car or same type of car at different orientations … a collection of images includes one or more synthetic images, such as an image created from a generative adversarial network (GAN). In at least one embodiment, all images of a collection of images are real images" teaches a process (method) performed by a computer system (computer-implemented) for training a neural network (sensor model) to predict objects within images using both real (real training data) and synthetic images (synthetic training data). [0157]-[0159]: "In at least one embodiment, one or more neural networks is trained in a self-supervised manner on a collection of images of different objects of a same category as an object of an image to be inferred. In at least one embodiment, different objects of a same category may refer to different images which may be one or more images of a first car at one or more orientations, one or more images of a different second car at one or more orientations, and so on. In at least one embodiment, an image of an object to be inferred is included in a collection of images used to train one or more neural networks to inference orientations. In at least one embodiment, one or more neural networks are trained in a self-supervised manner by at least using a set of loss functions to evaluate one or more characteristics of objects within images … a neural network trained in a self-supervised manner is trained to generate synthetic images of objects with a specific orientation, which may be a same orientation as a predicted orientation of an input image. In at least one embodiment, a synthetic image is created using a deep generative model such as a variational autoencoder (VAE), differentiable renderer, generative adversarial network (GAN), or a renderer … In at least one embodiment, one or more neural networks are trained on a collection of images of cars and is used to infer orientations of cars captured in real-time by a camera or other suitable video/image capture device attached to a vehicle" teaches capturing real images (real training data) of an object using a capture device attached to a vehicle and generating synthetic images (synthetic training data) for training the neural network (sensor model). [0300]: "In at least one embodiment, vehicle 2100 may further include ultrasonic sensor(s) 2162. Ultrasonic sensor(s) 2162, which may be positioned at front, back, and/or sides of vehicle 2100, may be used for park assist and/or to create and update an occupancy grid. In at least one embodiment, a wide variety of ultrasonic sensor(s) 2162 may be used, and different ultrasonic sensor(s) 2162 may be used for different ranges of detection" teaches that the capture device attached to a vehicle include multiple ultrasonic sensors (ultrasonic sensor devices)).
Regarding Claim 7,
Mustikovela et al. in view of Pöpperli et al. teaches the method according to claim 6.
In addition, Mustikovela et al. further teaches wherein the ultrasonic sensor model is formed as an artificial neural network or a gradient boosting model (Fig. 13; [0151]: "FIG. 13 shows an illustrative example of a process 1300 to train a neural network to predict a viewpoint of an object within an image, in accordance with at least one embodiment. In at least one embodiment, some or all of process 1300 (or any other processes described herein, or variations and/or combinations thereof) is performed under control of one or more computer systems configured with computer-executable instructions" teaches a process (method) performed by a computer system (computer-implemented) for training a neural network (sensor model) to predict objects within images. [0157]-[0159]: "In at least one embodiment, one or more neural networks is trained in a self-supervised manner on a collection of images of different objects of a same category as an object of an image to be inferred. In at least one embodiment, different objects of a same category may refer to different images which may be one or more images of a first car at one or more orientations, one or more images of a different second car at one or more orientations, and so on. In at least one embodiment, an image of an object to be inferred is included in a collection of images used to train one or more neural networks to inference orientations. In at least one embodiment, one or more neural networks are trained in a self-supervised manner by at least using a set of loss functions to evaluate one or more characteristics of objects within images … a neural network trained in a self-supervised manner is trained to generate synthetic images of objects with a specific orientation, which may be a same orientation as a predicted orientation of an input image. In at least one embodiment, a synthetic image is created using a deep generative model such as a variational autoencoder (VAE), differentiable renderer, generative adversarial network (GAN), or a renderer … In at least one embodiment, one or more neural networks are trained on a collection of images of cars and is used to infer orientations of cars captured in real-time by a camera or other suitable video/image capture device attached to a vehicle" teaches capturing real images (real training data) of an object using a capture device attached to a vehicle and generating synthetic images (synthetic training data) for training the neural network (the sensor model). [0300]: "In at least one embodiment, vehicle 2100 may further include ultrasonic sensor(s) 2162. Ultrasonic sensor(s) 2162, which may be positioned at front, back, and/or sides of vehicle 2100, may be used for park assist and/or to create and update an occupancy grid. In at least one embodiment, a wide variety of ultrasonic sensor(s) 2162 may be used, and different ultrasonic sensor(s) 2162 may be used for different ranges of detection" teaches that the capture device attached to a vehicle include multiple ultrasonic sensors (i.e. the artificial neural network is an ultrasonic sensor model)).
Regarding Claim 9,
Mustikovela et al. teaches a device for generating synthetic training data for training of a data-driven ultrasonic sensor model for a given configuration of an ultrasonic sensor system having multiple ultrasonic sensor devices (Fig. 13; [0154]: "a system performing at least a part of process 1300 includes executable code to train 1304 one or more neural networks to identify an orientation of an object within an image based, at least in part, on one or more characteristics of said object other than said object's orientation. In at least one embodiment, process 1300 is implemented on a processor comprising: one or more circuits to help train one or more neural networks to identify an orientation of an object within an image based, at least in part, on one or more characteristics of said object other than said object's orientation" teaches a system (device) for training a neural network (sensor model) to predict objects within images. [0157]-[0159]: "In at least one embodiment, one or more neural networks is trained in a self-supervised manner on a collection of images of different objects of a same category as an object of an image to be inferred. In at least one embodiment, different objects of a same category may refer to different images which may be one or more images of a first car at one or more orientations, one or more images of a different second car at one or more orientations, and so on. In at least one embodiment, an image of an object to be inferred is included in a collection of images used to train one or more neural networks to inference orientations. In at least one embodiment, one or more neural networks are trained in a self-supervised manner by at least using a set of loss functions to evaluate one or more characteristics of objects within images … a neural network trained in a self-supervised manner is trained to generate synthetic images of objects with a specific orientation, which may be a same orientation as a predicted orientation of an input image. In at least one embodiment, a synthetic image is created using a deep generative model such as a variational autoencoder (VAE), differentiable renderer, generative adversarial network (GAN), or a renderer … In at least one embodiment, one or more neural networks are trained on a collection of images of cars and is used to infer orientations of cars captured in real-time by a camera or other suitable video/image capture device attached to a vehicle" teaches capturing real images (real training data) of an object using a capture device attached to a vehicle and generating synthetic images (synthetic training data) for training the neural network (sensor model). [0300]: "In at least one embodiment, vehicle 2100 may further include ultrasonic sensor(s) 2162. Ultrasonic sensor(s) 2162, which may be positioned at front, back, and/or sides of vehicle 2100, may be used for park assist and/or to create and update an occupancy grid. In at least one embodiment, a wide variety of ultrasonic sensor(s) 2162 may be used, and different ultrasonic sensor(s) 2162 may be used for different ranges of detection" teaches that the capture device attached to a vehicle include multiple ultrasonic sensors (ultrasonic sensor devices)), … wherein the device is configured to:
provide real training data obtained by a measurement of the given configuration of an ultrasonic sensor system ([0157]-[0159]: "In at least one embodiment, one or more neural networks is trained in a self-supervised manner on a collection of images of different objects of a same category as an object of an image to be inferred. In at least one embodiment, different objects of a same category may refer to different images which may be one or more images of a first car at one or more orientations, one or more images of a different second car at one or more orientations, and so on. In at least one embodiment, an image of an object to be inferred is included in a collection of images used to train one or more neural networks to inference orientations. In at least one embodiment, one or more neural networks are trained in a self-supervised manner by at least using a set of loss functions to evaluate one or more characteristics of objects within images … a neural network trained in a self-supervised manner is trained to generate synthetic images of objects with a specific orientation, which may be a same orientation as a predicted orientation of an input image. In at least one embodiment, a synthetic image is created using a deep generative model such as a variational autoencoder (VAE), differentiable renderer, generative adversarial network (GAN), or a renderer … In at least one embodiment, one or more neural networks are trained on a collection of images of cars and is used to infer orientations of cars captured in real-time by a camera or other suitable video/image capture device attached to a vehicle" teaches capturing and providing real images (real training data) of an object using a capture device attached to a vehicle. [0300]: "In at least one embodiment, vehicle 2100 may further include ultrasonic sensor(s) 2162. Ultrasonic sensor(s) 2162, which may be positioned at front, back, and/or sides of vehicle 2100, may be used for park assist and/or to create and update an occupancy grid. In at least one embodiment, a wide variety of ultrasonic sensor(s) 2162 may be used, and different ultrasonic sensor(s) 2162 may be used for different ranges of detection" teaches that the capture device attached to a vehicle include multiple ultrasonic sensors (ultrasonic sensor devices)).
Mustikovela et al. does not appear to explicitly teach wherein the training data includes input data representing time-series data of received ultrasonic signals and output data indicating object characteristics of environmental objects in a sensing range of the ultrasonic sensor system; train a generator model with a training model using the real training data; and use the generator model to generate the synthetic training data by applying a random noise vector as input.
However, Pöpperli et al. teaches wherein the training data includes input data representing time-series data of received ultrasonic signals and output data indicating object characteristics of environmental objects in a sensing range of the ultrasonic sensor system (Fig. 4; Section II. C, first paragraph: "C. Measurement Results … Two samples of the measurements are shown in Fig. 4. Each signal consists of 9,900 samples with a sampling frequency of 330 kHz. Oversampling is applied to handle Doppler shift in dynamic scenarios. At the beginning, the reverberation time of the membrane is clearly visible. This is typical for a monostatic ultrasonic setup. Subsequently, the ground reflections appear. The ground reflections vary by changing the measurement setup and the ground type. By comparing Fig. 4(a) and (b), the differences in the ground reflections become clear. Fig. 4(a) shows a typical signal on asphalt, whereas Fig. 4(b) is typical for gravel" teaches collecting measurement results (training data) for ultrasonic sensor signals as input data time-series with output data indicating characteristics (object characteristics) of the ground (environmental object) in the sensing range of the ultrasonic sensor (ultrasonic sensor system));
train a generator model with a training model using the real training data (Fig 9; Section IV. A, second paragraph: "During training the generator tries to generate realistic ultrasonic signals based on the random noise vector and the given condition, as the discriminator is trained to distinguish the generated from real measurements. While the generator tries to fool the discriminator with more and more realistic signals, the capability of the discriminator to detect generated signals also increases. Thus, the aim of the training is to minimize the loss of the generator and to maximize the loss of the discriminator" teaches training the generator model with a GAN model (training model) using the measured data (real training data)); and
use the generator model to generate the synthetic training data by applying a random noise vector as input (Fig 9; Section IV. A, first-second paragraphs: "An overview of the used network architecture is shown in Fig. 9. To train the cGAN, two separate neural networks are used. Of course a generative neural network G is necessary. We use a simple multilayer perceptron (MLP) for this purpose. The network consists of 4 fully connected layers that use a leaky rectified linear unit (ReLU) as activation function and a fully connected output layer with a hyperbolic tangent as activation function. The network takes a random noise vector z and a conditional vector x as input … The discriminative network D that is needed for training is also a MLP that consists of 3 fully connected layers using a leaky ReLU activation function and a fully connected output layer with a SoftMax activation … During training the generator tries to generate realistic ultrasonic signals based on the random noise vector and the given condition, as the discriminator is trained to distinguish the generated from real measurements. While the generator tries to fool the discriminator with more and more realistic signals, the capability of the discriminator to detect generated signals also increases. Thus, the aim of the training is to minimize the loss of the generator and to maximize the loss of the discriminator" teaches that the generator model generates realistic ultrasonic signals (synthetic training data) by applying a random noise vector as input).
Mustikovela et al. and Pöpperli et al. are analogous to the claimed invention because they are directed towards the use of generative adversarial networks (GAN) for generating synthetic ultrasonic sensor data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the training data includes input data representing time-series data of received ultrasonic signals and output data indicating object characteristics of environmental objects in a sensing range of the ultrasonic sensor system; train a generator model with a training model using the real training data; and use the generator model to generate the synthetic training data by applying a random noise vector as input as taught by Pöpperli et al. to the disclosed invention of Mustikovela et al.
One of ordinary skill in the art would have been motivated to make this modification "to bring the environment simulation to a high quality close to realistic data ... the proposed approach is flexible to external influences" and provides "low complexity and smaller time effort needed for data generation" (Pöpperli et al. Abstract).
Regarding Claim 10,
Mustikovela et al. in view of Pöpperli et al. teaches the method according to claim 1.
In addition, Mustikovela et al. further teaches a computer program product comprising a computer readable medium, having thereon computer program code, when said program is loaded, configured to make the computer execute procedures to perform the method according to claim 1 (Fig. 13; [0151]: "FIG. 13 shows an illustrative example of a process 1300 to train a neural network to predict a viewpoint of an object within an image, in accordance with at least one embodiment. In at least one embodiment, some or all of process 1300 (or any other processes described herein, or variations and/or combinations thereof) is performed under control of one or more computer systems configured with computer-executable instructions and may be implemented as code (e.g., computer-executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, software, or combinations thereof. Code, in at least one embodiment, is stored on a computer-readable storage medium in form of a computer program comprising a plurality of computer-readable instructions executable by one or more processors. A computer-readable storage medium, in at least one embodiment, is a non-transitory computer-readable medium" teaches a computer program (computer program product) on a computer-readable storage medium storing code (computer program code) configured to make the computer processors perform the process 1300 (method according to claim 1)).
Regarding Claim 11,
Mustikovela et al. in view of Pöpperli et al. teaches the method according to claim 1.
In addition, Mustikovela et al. further teaches a machine readable medium, having a program recorded thereon, where the program is configured to make the computer execute the method according to claim 1 (Fig. 13; [0151]: "FIG. 13 shows an illustrative example of a process 1300 to train a neural network to predict a viewpoint of an object within an image, in accordance with at least one embodiment. In at least one embodiment, some or all of process 1300 (or any other processes described herein, or variations and/or combinations thereof) is performed under control of one or more computer systems configured with computer-executable instructions and may be implemented as code (e.g., computer-executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, software, or combinations thereof. Code, in at least one embodiment, is stored on a computer-readable storage medium in form of a computer program comprising a plurality of computer-readable instructions executable by one or more processors. A computer-readable storage medium, in at least one embodiment, is a non-transitory computer-readable medium" teaches a computer-readable storage medium (machine readable medium) storing a computer program configured to make the computer processors perform the process 1300 (method according to claim 1)).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Mustikovela et al. (US 2021/0150757 A1) in view of Pöpperli et al. ("Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks") and further in view of Wan et al. ("Variational autoencoder based synthetic data generation for imbalanced learning").
Regarding Claim 2,
Mustikovela et al. in view of Pöpperli et al. teaches the method according to claim 1.
In addition, Pöpperli et al. further teaches wherein the generator model is trained by applying as the training model a GAN model comprising the generator model and a discriminator model which are adversarially trained (Fig 9; Section IV. A, second paragraph: "During training the generator tries to generate realistic ultrasonic signals based on the random noise vector and the given condition, as the discriminator is trained to distinguish the generated from real measurements. While the generator tries to fool the discriminator with more and more realistic signals, the capability of the discriminator to detect generated signals also increases. Thus, the aim of the training is to minimize the loss of the generator and to maximize the loss of the discriminator" teaches training the generator model with a GAN model (training model) comprising the generator model and a discriminator model that are adversarially trained).
Mustikovela et al. and Pöpperli et al. are analogous to the claimed invention because they are directed towards the use of generative adversarial networks (GAN) for generating synthetic ultrasonic sensor data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the generator model is trained by applying as the training model a GAN model comprising the generator model and a discriminator model which are adversarially trained as taught by Pöpperli et al. to the disclosed invention of Mustikovela et al.
One of ordinary skill in the art would have been motivated to make this modification "to bring the environment simulation to a high quality close to realistic data ... the proposed approach is flexible to external influences" and provides "low complexity and smaller time effort needed for data generation" (Pöpperli et al. Abstract).
Mustikovela et al. in view of Pöpperli et al. does not appear to explicitly teach wherein … the generator model is trained by applying as the training model a Variational Autoencoder wherein a decoder portion of the Variational Autoencoder forms the generator model.
However, Wan et al. teaches wherein … the generator model is trained by applying as the training model a Variational Autoencoder wherein a decoder portion of the Variational Autoencoder forms the generator model (Section I, fifth paragraph: "In general, the encoder and decoder are implemented by neural networks. In order to learn the complicated data distribution, the encoder is applied to output the approximate posterior of the latent variable. Then, the values of the latent variable are fed into the decoder to generate new samples. VAE is trained by applying gradient descent to minimize its loss function which is composed of two parts. The first part is the reconstruction error between the input and generated sample. The other part is the KL divergence between the approximate posterior and true prior of the latent variable. After the training process, the value of the latent variable is directly sampled from the prior distribution. Then, the sampled value is fed into the decoder to generate a new sample" teaches the generator model being a decoder portion of a variational autoencoder (VAE) for generating a new sample (synthetic data)).
Mustikovela et al. and Pöpperli et al. are analogous to the claimed invention because they are directed towards the use of generative adversarial networks (GAN) for generating synthetic ultrasonic sensor data.
Wan et al. is analogous to the claimed invention because it is directed towards the implementation of a variational autoencoder (VAE) for synthetic data generation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein … the generator model is trained by applying as the training model a Variational Autoencoder wherein a decoder portion of the Variational Autoencoder forms the generator model as taught by Wan et al. to the disclosed invention of Mustikovela et al. in view of Pöpperli et al.
One of ordinary skill in the art would have been motivated to make this modification because "the proposed method outperforms the traditional synthetic sampling methods under various assessment metrics" (Wan et al. Section I, sixth paragraph).
Claims 3-5, 8, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mustikovela et al. (US 2021/0150757 A1) in view of Pöpperli et al. ("Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks") and further in view of Xu et al. ("Modeling Tabular data using Conditional GAN").
Regarding Claim 3,
Mustikovela et al. in view of Pöpperli et al. teaches the method according to claim 1.
Mustikovela et al. in view of Pöpperli et al. does not appear to explicitly teach wherein the training model for obtaining the generator model is selected from a plurality of given training models depending on one or more scoring metrics.
However, Xu et al. teaches wherein the training model for obtaining the generator model is selected from a plurality of given training models depending on one or more scoring metrics (Section 1, last paragraph: "A benchmarking system for synthetic data generation algorithms.2 We designed a comprehensive benchmark framework using several tabular datasets and different evaluation metrics as well as implementations of several baselines and state-of-the-art methods. Our system is open source and can be extended with other methods and additional datasets. At the time of this writing, the benchmark has 5 deep learning methods, 2 Bayesian network methods, 15 datasets, and 2 evaluation mechanisms" teaches that synthetic data generation algorithms (training models for obtaining the generator model) are evaluated using different evaluation metrics (scoring metrics) to select the generation algorithm (training model) for obtaining the generator model. Table 2; Fig. 3; Section 5.2, first paragraph: "Given that evaluation of generative models is not a straightforward process, where different metrics yield substantially diverse results, our benchmarking suite evaluates multiple metrics on multiple datasets. Simulated data come from a known probability distribution and for them we can evaluate the generated synthetic data via likelihood fitness metric. For real datasets, there is a machine learning task and we evaluate synthetic data generation method via machine learning efficacy. Figure 3 illustrates the evaluation framework" teaches that the models are evaluated using multiple different metrics (one or more scoring metrics)).
Mustikovela et al. and Pöpperli et al. are analogous to the claimed invention because they are directed towards the use of generative adversarial networks (GAN) for generating synthetic ultrasonic sensor data.
Xu et al. is analogous to the claimed invention because it is directed towards the implementation of a conditional generative adversarial network (GAN) and a variational autoencoder (VAE) for synthetic data generation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the training model for obtaining the generator model is selected from a plurality of given training models depending on one or more scoring metrics as taught by Xu et al. to the disclosed invention of Mustikovela et al. in view of Pöpperli et al.
One of ordinary skill in the art would have been motivated to make this modification "to find a flexible and robust model to learn the distribution of columns with complicated distributions … And our conditional generator and training-by-sampling can overcome the imbalance training data issue ... [and] help generate data with a specific discrete value, which can be used for data augmentation" (Xu et al. Section 6).
Regarding Claim 4,
Mustikovela et al. in view of Pöpperli et al. and further in view of Xu et al. teaches the method according to claim 3.
In addition, Xu et al. further teaches wherein the plurality of given training models include at least one of a variational autoencoder, a Conditional GAN model, and a CopulaGAN model (Table 2; Section 5.1, first paragraph: "In our benchmarking suite, we have baselines that consist of Bayesian networks (CLBN, PrivBN), and implementations of current deep learning approaches for synthetic data generation (MedGAN, VeeGAN, TableGAN). We compare TVAE and CTGAN with these baselines" teaches the training models including TVAE (variational autoencoder) and CTGAN (conditional GAN)).
Mustikovela et al. and Pöpperli et al. are analogous to the claimed invention because they are directed towards the use of generative adversarial networks (GAN) for generating synthetic ultrasonic sensor data.
Xu et al. is analogous to the claimed invention because it is directed towards the implementation of a conditional generative adversarial network (GAN) and a variational autoencoder (VAE) for synthetic data generation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the plurality of given training models include at least one of a variational autoencoder, a Conditional GAN model, and a CopulaGAN model as taught by Xu et al. to the disclosed invention of Mustikovela et al. in view of Pöpperli et al.
One of ordinary skill in the art would have been motivated to make this modification "to find a flexible and robust model to learn the distribution of columns with complicated distributions … And our conditional generator and training-by-sampling can overcome the imbalance training data issue ... [and] help generate data with a specific discrete value, which can be used for data augmentation" (Xu et al. Section 6).
Regarding Claim 5,
Mustikovela et al. in view of Pöpperli et al. and further in view of Xu et al. teaches the method according to claim 3.
In addition, Xu et al. further teaches wherein the one or more scoring metrics include at least one of: a statistical metric, a detection metric, and a likelihood metric (Table 2; Fig. 3; Section 5.2, first paragraph: "Given that evaluation of generative models is not a straightforward process, where different metrics yield substantially diverse results, our benchmarking suite evaluates multiple metrics on multiple datasets. Simulated data come from a known probability distribution and for them we can evaluate the generated synthetic data via likelihood fitness metric. For real datasets, there is a machine learning task and we evaluate synthetic data generation method via machine learning efficacy. Figure 3 illustrates the evaluation framework" teaches that the models are evaluated using multiple different metrics (one or more scoring metrics) including a likelihood fitness metric (likelihood metric)).
Mustikovela et al. and Pöpperli et al. are analogous to the claimed invention because they are directed towards the use of generative adversarial networks (GAN) for generating synthetic ultrasonic sensor data.
Xu et al. is analogous to the claimed invention because it is directed towards the implementation of a conditional generative adversarial network (GAN) and a variational autoencoder (VAE) for synthetic data generation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the one or more scoring metrics include at least one of: a statistical metric, a detection metric, and a likelihood metric as taught by Xu et al. to the disclosed invention of Mustikovela et al. in view of Pöpperli et al.
One of ordinary skill in the art would have been motivated to make this modification "to find a flexible and robust model to learn the distribution of columns with complicated distributions … And our conditional generator and training-by-sampling can overcome the imbalance training data issue ... [and] help generate data with a specific discrete value, which can be used for data augmentation" (Xu et al. Section 6).
Regarding Claim 8,
Mustikovela et al. in view of Pöpperli et al. teaches the method according to claim 1.
Mustikovela et al. in view of Pöpperli et al. does not appear to explicitly teach wherein training data is given as tabular data.
However, Xu et al. teaches wherein training data is given as tabular data (Section 1, last paragraph: "Conditional GANs for synthetic data generation. We propose CTGAN as a synthetic tabular data generator to address several issues mentioned above. CTGAN outperforms all methods to date and surpasses Bayesian networks on at least 87.5% of our datasets. To further challenge CTGAN, we adapt a variational autoencoder (VAE) [15] for mixed-type tabular data generation. We call this TVAE. VAEs directly use data to build the generator; even with this advantage, we show that our proposed CTGAN achieves competitive performance across many datasets and outperforms TVAE on 3 datasets" teaches that the training data for the generator can be given as tabular data).
Mustikovela et al. and Pöpperli et al. are analogous to the claimed invention because they are directed towards the use of generative adversarial networks (GAN) for generating synthetic ultrasonic sensor data.
Xu et al. is analogous to the claimed invention because it is directed towards the implementation of a conditional generative adversarial network (GAN) and a variational autoencoder (VAE) for synthetic data generation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein training data is given as tabular data as taught by Xu et al. to the disclosed invention of Mustikovela et al. in view of Pöpperli et al.
One of ordinary skill in the art would have been motivated to make this modification "to find a flexible and robust model to learn the distribution of columns with complicated distributions … And our conditional generator and training-by-sampling can overcome the imbalance training data issue ... [and] help generate data with a specific discrete value, which can be used for data augmentation" (Xu et al. Section 6).
Regarding Claim 12,
Mustikovela et al. in view of Pöpperli et al. and further in view of Xu et al. teaches the method according to claim 3.
In addition, Xu et al. further teaches wherein the one or more scoring metrics comprises an average scoring metrics of multiple scoring metrics (Table 2; Fig. 3; Section 5.2, third paragraph: "Since we are not trying to pick the best classification or regression model, we take the average performance of multiple prediction models to evaluate our metric for G … Benchmark results over three sets of experiments, namely Gaussian mixture simulated data (GMSim.), Bayesian network simulated data (BNSim.), and real data. For GMSim. and BNSim., we report the average of each metric. For real data sets, we report average F1 for classification tasks and R2 for regression tasks respectively" teaches that the models are evaluated using multiple different metrics (one or more scoring metrics) including an average scoring metric of multiple scoring metrics).
Mustikovela et al. and Pöpperli et al. are analogous to the claimed invention because they are directed towards the use of generative adversarial networks (GAN) for generating synthetic ultrasonic sensor data.
Xu et al. is analogous to the claimed invention because it is directed towards the implementation of a conditional generative adversarial network (GAN) and a variational autoencoder (VAE) for synthetic data generation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the one or more scoring metrics comprises an average scoring metrics of multiple scoring metrics as taught by Xu et al. to the disclosed invention of Mustikovela et al. in view of Pöpperli et al.
One of ordinary skill in the art would have been motivated to make this modification "to find a flexible and robust model to learn the distribution of columns with complicated distributions … And our conditional generator and training-by-sampling can overcome the imbalance training data issue ... [and] help generate data with a specific discrete value, which can be used for data augmentation" (Xu et al. Section 6).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN J HALES whose telephone number is (571)272-0878. The examiner can normally be reached M-F 9:00am - 5:00pm.
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/BRIAN J HALES/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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