DETAILED ACTION
This office action is in response to the application filed on June 24, 2024.
Claims 1-13 are pending and have been examined. Claims 1-13 are rejected.
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 .
Priority
Applicants’ claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The present application claims foreign priority based on European Patent Application No. EP23184414.3 filed July 10, 2023. The examiner notes that a certified copy (in English) of the above-noted application was received on July 22, 2024.
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements filed June 24, 2024 and July 23, 2024, which complies with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner.
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- and 9-10 are rejected under 35 U.S.C. 112(b) 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.
Regarding Claim 1, the following limitations do not clearly set the metes and bounds of the patent protection desired:
Regarding the limitation "providing the data and a physical attribute of the data, including a range or an azimuth or a velocity or an indication of polarizations of sent and received radar signals that the data is based on", there is insufficient antecedent basis for this limitation in the claim, rendering the claim indefinite because the previous limitation mentions two different types of data, such as “data of a first radar spectrum, including data from a region of interest”, where it is not clear what type of data “providing the data” is referencing. Additionally, it is not clear whether the range in “including a range” is part of the data or part of the physical attribute.
Regarding the limitation "providing a first output that is configured to map the first features to a prediction of the physical attribute, the predicting including a prediction of the range, or a prediction of the azimuth, or a prediction of the velocity or a prediction of the indication of the polarizations of the sent and received radar signals”, there is insufficient antecedent basis for this limitation in the claim, rendering the claim indefinite because “the predicting” is not defined in previous limitations.
Regarding the limitation "mapping the data with the first model to the first features", there is insufficient antecedent basis for this limitation in the claim, rendering the claim indefinite because “mapping the data” is not clearly pointing to whether data of a first radar spectrum or data from a region of interest is being mapped.
Regarding Claim 9, the limitation "capturing a radar spectrum with a radar system; determining the prediction for the task depending on the captured radar spectrum with the trained first model or the trained second model; and actuating a technical system depending on the prediction for the task" does not clearly set the metes and bounds of the patent protection desired. There is insufficient antecedent basis for this limitation in the claim, rendering the claim indefinite because “the trained second model” is not defined in Claim 6, which Claim 9 is dependent on.
Regarding Claim 10, the limitation "learning the first model, including learning the weights, with unlabeled data from a plurality of first radar spectra, including range-azimuth spectra, and/or range-velocity spectra and/or range-polarization spectra; and training the learned first model or the second model with data from a plurality of second radar spectra, including range-azimuth spectra and/or range-velocity spectra and/or range-polarization spectra, wherein the data from the plurality of second radar spectra is labelled with the reference" does not clearly set the metes and bounds of the patent protection desired. There is insufficient antecedent basis for this limitation in the claim, rendering the claim indefinite because “the second model” is not defined in Claim 6, which Claim 10 is dependent on.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are: “The first model being configured to” and “providing a first output that is configured to” in Claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
Step 1: The claim does not fall within one of the four statutory categories of invention (process, machine, manufacture, or composition of matter) – see MPEP 2106.03, or,
Step 2: The claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis – see MPEP 2106.04:
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? - see MPEP 2106.05
MPEP 2106.04(a)(2)(I) states: “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
MPEP 2106.04(a)(2)(III) states: “Accordingly, the “mental processes” abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions.
Further, the MPEP states: “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g. pen and paper or a slide run) to perform the claim limitation.
Using the two-step inquiry, it is clear that Claims 1-13 are each directed to non-statutory subject matter as shown below:
With respect to Claims 1, 11, 12, and 13:
Step 1: Claim 1 is directed to a method, also known as a process, which is one of the four statutory categories of patentable subject matter. Claim 11 is directed to an apparatus, which is one of the four statutory categories of patentable subject matter. Claim 12 is directed to an apparatus, which is one of the four statutory categories of patentable subject matter. Claim 13 is directed to an apparatus, which is one of the four statutory categories of patentable subject matter.
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“…that is configured to map the first features to a prediction of the physical attribute, the predicting including a prediction of the range, or a prediction of the azimuth, or a prediction of the velocity or a prediction of the indication of the polarizations of the sent and received radar signals;” (Mapping the first features to a prediction of the physical attribute covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement – see MPEP 2106.04.)
“mapping the first features with the first output to the prediction of the physical attribute;” (Covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement – see MPEP 2106.04.)
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“A computer implemented method for machine learning, comprising the following steps:
“providing a first model, including a neural network having weights, the first model being configured to map data of a first radar spectrum, including data from a region of interest of the first radar spectrum, to first features that represent the data;” (Providing a first model, including a neural network having weights, which is configured to map data adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).)
“providing the data and a physical attribute of the data, including a range or an azimuth or a velocity or an indication of polarizations of sent and received radar signals that the data is based on;” (Providing data and a physical attribute of the data, including a range or an azimuth or a velocity or an indication of polarizations of sent and received radar signals that the data is based on is akin to receiving data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).)
“providing a first output…” (Providing a first output is akin to outputting data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).)
“mapping the data with the first model to the first features;” (Mapping the data with the first model to the first features only amount to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
“and learning the first model, including learning the weights, depending on a difference between the prediction of the physical attribute and the physical attribute.” (Learning, akin to training, a first model, including learning the weights, depending on a difference between the prediction of the physical attribute and the physical attribute only amount to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Providing a first model, including a neural network having weights, which is configured to map data adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).) Providing data and a physical attribute of the data, including a range or an azimuth or a velocity or an indication of polarizations of sent and received radar signals that the data is based on is akin to receiving data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).
Providing a first output is akin to outputting data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g). Mapping the data with the first model to the first features only amount to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1). Learning, akin to training, a first model, including learning the weights, depending on a difference between the prediction of the physical attribute and the physical attribute only amount to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).
With respect to Claim 2:
Step 2A, Prong 1: Inherits the limitations from Claim 1.
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“wherein the first output includes a first neural network that has first weights, (A first output that includes a first neural network that has first weights generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).)
and wherein the method further comprises learning the first weights depending on the difference between the prediction of the physical attribute and the physical attribute.” (Learning first weights depending on the difference between the prediction of the physical attribute and the physical attribute is only indicating the particular function of training weights depending on a difference between two values is performed by a computer. This only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. A first output that includes a first neural network that has first weights generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h). Learning first weights depending on the difference between the prediction of the physical attribute and the physical attribute is only indicating the particular function of training weights depending on the difference between two values is performed by a computer. This only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).
With respect to Claim 3:
Step 2A, Prong 1: Inherits the limitations from Claim 1. An additional judicial exception is recited in the claim as it recites mental processes, which are abstract ideas:
“wherein the learning of the first model depending on the difference between the prediction of the physical attribute and the physical attribute includes: (i) determining a difference between the prediction of the range and the range, and/or (ii) determining a difference between the prediction of the azimuth and the azimuth, and/or (iii) determining a difference between the prediction of the velocity and the velocity, and/or (iv) determining a difference between the prediction of the indication of the polarizations and the indication of the polarizations.” (Covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement – see MPEP 2106.04.)
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
With respect to Claim 4:
Step 2A, Prong 1: Inherits the limitations from Claim 1.
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“wherein the method further comprises learning the first model or the first output depending on at least two of: (i) the difference between the prediction of the range and the range, (ii) the difference between the prediction of the azimuth and the azimuth, (iii) the difference between the prediction of the velocity and the velocity, (iv) the difference between the prediction of the indication of the polarizations and the indication of the polarizations.” (Learning a first model or a first output is only indicating the particular function of training a model or output depending on the difference between two values is performed by a computer. This only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Learning a first model or a first output is only indicating the particular function of training a model or output depending on the difference between two values is performed by a computer. This only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).
With respect to Claim 5:
Step 2A, Prong 1: Inherits the limitations from Claim 1.
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“wherein the providing of the first output includes providing the first output to include one branch for mapping the first features to the prediction of the physical attribute, including: (i) one branch for mapping the first features to a prediction of the range, and/or (ii) one branch for mapping the first features to a prediction of the azimuth, and/or (iii) one branch for mapping the first features to a prediction of the velocity, and/or (iv) one branch for mapping the first features to a prediction of the indication of the polarizations.” (Providing of the first output that includes providing the first output to include one branch for mapping the first features to the prediction of the physical attribute generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Providing of the first output that includes providing the first output to include one branch for mapping the first features to the prediction of the physical attribute generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).
With respect to Claim 6:
Step 2A, Prong 1: Inherits the limitations from Claim 1. An additional judicial exception is recited in the claim as it recites mental processes, which are abstract ideas:
“mapping data of a second radar spectrum, including data from a region of interest of the second radar spectrum, with the learned first model to second features;” (Covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement – see MPEP 2106.04.)
“mapping the second features with the second output to the prediction for the task;” (Covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement – see MPEP 2106.04.)
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“providing a second output that is configured to map the second features to a prediction for a task, including: (i) a prediction for a classification, or (ii) a prediction for an object detection;” (Providing a second output that is configured to map second features to a prediction for a task, including a prediction for a classification or a prediction for an object detection is akin to outputting data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).)
“providing a reference for the prediction for the task, including a label indicating a ground truth of the classification or the object detection;” (Providing a reference for the prediction for the task, including a label indicating a ground truth of the classification or the object detection is akin to inputting data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).)
“and training the learned first model, including updating the learned weights, depending on a difference between the prediction for the task and the reference.” (Training a learned first model, including updating the learned weights, depending on a difference between the prediction for the task and the reference only amount to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Providing a second output that is configured to map second features to a prediction for a task, including a prediction for a classification or a prediction for an object detection is akin to outputting data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g). Providing a reference for the prediction for the task, including a label indicating a ground truth of the classification or the object detection is akin to inputting data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g). Training a learned first model, including updating the learned weights, depending on a difference between the prediction for the task and the reference only amount to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).
With respect to Claim 7:
Step 2A, Prong 1: Inherits the limitations from Claim 1. An additional judicial exception is recited in the claim as it recites mental processes, which are abstract ideas:
“mapping data of a second radar spectrum, including data from a region of interest of the second radar spectrum, with the second model to second features;” (Covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement – see MPEP 2106.04.)
“mapping the second features with the second output to the prediction for the task;” (Covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement – see MPEP 2106.04.)
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“providing a second model that includes the same architecture as the learned first model, including providing the second model with the learned neural network including the learned weights;” (Providing a second model that includes the same architecture as the learned first model, including providing the second model with the learned neural network including the learned weights only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
“providing a second output that is configured to map the second features to a prediction for a task, including a prediction for a classification or a prediction for an object detection;” (Providing a second output that is configured to map the second features to a prediction for a task, including a prediction for a classification or a prediction for an object detection is akin to outputting data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).)
“providing a reference for the prediction for the task, including a label indicating a ground truth of the classification or a ground truth of the object detection;” (Providing a reference for the prediction for the task, including a label indicating a ground truth of the classification or a ground truth of the object detection is akin to inputting data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).)
“and training the second model, including updating the learned weights, depending on a difference between the prediction for the task and the reference.” (Training the second model, including updating the learned weights, depending on a difference between the prediction for the task and the reference only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Providing a second model that includes the same architecture as the learned first model, including providing the second model with the learned neural network including the learned weights only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1). Providing a second output that is configured to map the second features to a prediction for a task, including a prediction for a classification or a prediction for an object detection is akin to outputting data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).)
Providing a reference for the prediction for the task, including a label indicating a ground truth of the classification or a ground truth of the object detection is akin to inputting data, which adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g).) Training the second model, including updating the learned weights, depending on a difference between the prediction for the task and the reference only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
With respect to Claim 8:
Step 2A, Prong 1: Inherits the limitations from Claim 6.
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“wherein the second output includes a second neural network that has second weights, (A second output that includes a second neural network that has second weights generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h).)
wherein the method further comprises learning the second weights depending on the difference between the prediction for the task and the reference.” (Learning second weights depending on the difference between the prediction for the task and the reference is only indicating the particular function of training weights depending on a difference between two values is performed by a computer. This only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. A second output that includes a second neural network that has second weights generally links the use of the abstract idea to a particular technological environment or field of use – see MPEP 2106.05(h). Learning second weights depending on the difference between the prediction for the task and the reference is only indicating the particular function of training weights depending on a difference between two values is performed by a computer. This only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).
With respect to Claim 9:
Step 2A, Prong 1: Inherits the limitations and abstract ideas from Claim 6. A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“capturing a radar spectrum with a radar system;” (Covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement – see MPEP 2106.04.)
“determining the prediction for the task depending on the captured radar spectrum with the trained first model or the trained second model;” (Covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement – see MPEP 2106.04.)
“and actuating a technical system depending on the prediction for the task.” (Covers mental concepts that could be practically performed in the human mind or through the use of a pencil and paper, including observation, evaluation, and judgement – see MPEP 2106.04.)
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
With respect to Claim 10:
Step 2A, Prong 1: Inherits the limitations and abstract ideas from Claim 6.
Step 2A, Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application:
“learning the first model, including learning the weights, with unlabeled data from a plurality of first radar spectra, including range-azimuth spectra, and/or range-velocity spectra and/or range-polarization spectra;” (Learning a first model, including learning the weights, with unlabeled data from a plurality of first radar spectra is only indicating the particular function of training a model with data is performed by a computer. This only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
“and training the learned first model or the second model with data from a plurality of second radar spectra, including range-azimuth spectra and/or range-velocity spectra and/or range-polarization spectra, wherein the data from the plurality of second radar spectra is labelled with the reference.” (Training a learned first model or a second model with data from a plurality of second radar spectra is only indicating the particular function of training a model with is performed by a computer. This only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).)
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. Learning a first model, including learning the weights, with unlabeled data from a plurality of first radar spectra is only indicating the particular function of training a model with data is performed by a computer. This only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1). Training a learned first model or a second model with data from a plurality of second radar spectra is only indicating the particular function of training a model with is performed by a computer. This only amounts to “apply it” and mere instructions to implement an abstract idea on a computer – see MPEP 2106.05(f)(1).
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.
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.
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 non-obviousness.
Claim(s) 1-6 and 8-13 are rejected under 35 U.S.C. 103 as being unpatentable over “CNN Based Road User Detection Using the 3D Radar Cube” by Palffy et al. (non-patent literature published on April 25, 2020, hereinafter “Palffy”), in view of Blaes et al. (US Patent Application Number US12221115B1 filed on April 29, 2022, hereinafter “Blaes”).
With respect to Claims 1, 11, 12, and 13:
Palffy teaches:
“A computer implemented method for machine learning, comprising the following steps:
“providing a first model, including a neural network having weights, the first model being configured to map data of a first radar spectrum, including data from a region of interest of the first radar spectrum, to first features that represent the data;” (Pages 5 and 6, Section “1) Down-Sample Range and Azimuth Dimensions” recites RTCnet (providing a first model) contains 3D convolutional layers with a kernel (weight) size of 7, akin to neural networks containing weighted network layers (including a neural network having weights). Page 5, Section “III. Proposed Method” recites the RTCnet network consists of three parts, where it first encodes the data in spatial and radar domains like range and azimuth and then is applied on the output to extract class information from the distribution of speed. The third part provides classifications scores by two fully connected layers (configured to map data of a first radar spectrum to first features that represent the data). Page 3, Section “1. Introduction” recites RTCnet involves a radar cube that is a 3D data matrix with axes corresponding to range, azimuth, and velocity. Page 5, Section “A. Pre-Processing” further recites a 3D block of the radar cube is cropped around each radar target’s grid cell with radius in range/azimuth/Doppler dimensions (including data from a region of interest of the first radar spectrum).)
Palffy does not appear to explicitly disclose:
“providing the data and a physical attribute of the data, including a range or an azimuth or a velocity or an indication of polarizations of sent and received radar signals that the data is based on;”
“providing a first output that is configured to map the first features to a prediction of the physical attribute, the predicting including a prediction of the range, or a prediction of the azimuth, or a prediction of the velocity or a prediction of the indication of the polarizations of the sent and received radar signals;”
“and learning the first model, including learning the weights, depending on a difference between the prediction of the physical attribute and the physical attribute.”
However, Blaes teaches:
“providing the data and a physical attribute of the data, including a range or an azimuth or a velocity or an indication of polarizations of sent and received radar signals that the data is based on;” (Column 29, Lines 14-16 recite a radar-based perception system receiving radar data representing a physical environment over time. Column 5, Lines 64-66 further recite the radar data may also include velocity information (and a physical attribute of the data, including a velocity).)
“providing a first output that is configured to map the first features to a prediction of the physical attribute, the predicting including a prediction of the range, or a prediction of the azimuth, or a prediction of the velocity or a prediction of the indication of the polarizations of the sent and received radar signals;” (Column 32, Lines 5-11 recites providing the radar data, including doppler velocity of the object at a second time and first time to a machine learning model configured to output a second estimated velocity (a prediction of the physical attribute), which is akin to a prediction of the velocity.)
“mapping the data with the first model to the first features;” (Column 29, Lines 57-67 recite taking the velocity features from the data of the object and producing a second estimated velocity as the output using an ML model, which Is akin to mapping data with a first model to first features.)
“mapping the first features with the first output to the prediction of the physical attribute;” (Column 29, Lines 57-67 recite taking the velocity features from the data of the object, mapping the first features, and producing a second estimated velocity as the output (first output to the prediction of the physical attribute) using an ML model, which Is akin to mapping data with a first model to first features.)
“and learning the first model, including learning the weights, depending on a difference between the prediction of the physical attribute and the physical attribute.” (Column 32, Lines 5-11 recites providing the radar data, including doppler velocity of the object at a second time and first time to a machine learning model configured to output a second estimated velocity through computing a loss function (depending on a difference between the prediction of the physical attribute and the physical attribute), which means the machine learning model’s weights are trained (learned) during this process.)
It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the present application to combine the teachings of Palffy and the teachings of Blaes, which are both in the same field of invention. A PHOSITA would be motivated to combine the “region of interest” radar spectra feature extractor from Palffy with the self-supervised physical attribute training technique from Blaes in order to avoid manually labeling data, which can be slow, and instead train the radar model using a physical attribute such as velocity and the self-supervised training technique.
With respect to Claim 2:
Palffy and Blaes combined teach:
“wherein the first output includes a first neural network that has first weights, (Column 12, Lines 35-40 from Blaes recite a perception system that uses one or more neural networks (a first neural network). Column 12, Lines 41-49 from Blaes recite one or more neural networks output any number of learned inferences or heads, as well as other learned output heads. It is inherently understood that a learned output head consists of trainable layers each with its own weight parameters (first weights).)
and wherein the method further comprises learning the first weights depending on the difference between the prediction of the physical attribute and the physical attribute.” (Column 12, Lines 46-48 from Blaes recite the neural network may be a trained neural network architecture that is end-to-end with Stochastic Gradient Descent. It is inherently understood that Stochastic Gradient Descent adjusts all of the weights within the network based on a loss computed from the difference between predictions (prediction of the physical attribute) and a ground truth (physical attribute). This difference is then used to update the weights of the learned heads (learning the first weights depending on the difference between the prediction of the physical attribute and the physical attribute).)
With respect to Claim 3:
Palffy and Blaes combined teach:
“wherein the learning of the first model depending on the difference between the prediction of the physical attribute and the physical attribute includes: (i) determining a difference between the prediction of the range and the range, and/or (ii) determining a difference between the prediction of the azimuth and the azimuth, and/or (iii) determining a difference between the prediction of the velocity and the velocity, and/or (iv) determining a difference between the prediction of the indication of the polarizations and the indication of the polarizations” (Column 30, Lines 1-5 from Blaes recite a process including determining a difference between a first estimated velocity and a second estimated velocity ((iii) determining a difference between the prediction of the velocity and the velocity).)
With respect to Claim 4:
Palffy and Blaes combined teach:
“wherein the method further comprises learning the first model or the first output depending on at least two of: (i) the difference between the prediction of the range and the range, (ii) the difference between the prediction of the azimuth and the azimuth, (iii) the difference between the prediction of the velocity and the velocity, (iv) the difference between the prediction of the indication of the polarizations and the indication of the polarizations” (Column 12, Lines 41-49 from Blaes recite one or more neural networks that generate any number of learned inferences or heads (learning the first model), as well as other learned output heads such as target azimuth. Column 12, Lines 56-60 further recite that these output heads are trained using loss functions like mean square, which are both difference-based measures applied across the neural network’s learned inference heads, akin to a (ii) difference between the prediction of the azimuth and the azimuth. Column 30, Lines 1-5 from Blaes recite a process including determining a difference between a first estimated velocity and a second estimated velocity ((iii) determining a difference between the prediction of the velocity and the velocity).)
With respect to Claim 5:
Palffy and Blaes combined teach:
“wherein the providing of the first output includes providing the first output to include one branch for mapping the first features to the prediction of the physical attribute, including: (i) one branch for mapping the first features to a prediction of the range, and/or (ii) one branch for mapping the first features to a prediction of the azimuth, and/or (iii) one branch for mapping the first features to a prediction of the velocity, and/or (iv) one branch for mapping the first features to a prediction of the indication of the polarizations” (Column 12, Lines 41-49 from Blaes recite one or more neural networks that generate any number of learned inferences or heads, as well as other learned output heads, such as target velocity (first output). It is inherently understood that a learned output head such as target velocity is a separate output branch off a shared feature representation produced by a neural network, where the branch is predicting velocity from the shared features, akin to (iii) one branch for mapping the first features to a prediction of the velocity. Page 6, Figure 4 showcases the learned output head (branch) is a distinct branch (one branch) for the semantic class head and direction to object instance head, where it receives the common feature output of the network and produced a velocity prediction.)
With respect to Claim 6:
Palffy and Blaes combined teach:
“mapping data of a second radar spectrum, including data from a region of interest of the second radar spectrum, with the learned first model to second features;” (Page 5, Section “III. Proposed Method” from Palffy recites the RTCnet network consists of three parts, where it first encodes the data in spatial and radar domains like range and azimuth and then is applied on the output to extract class information from the distribution of speed. The third part provides classifications scores by two fully connected layers (mapping data of a second radar spectrum to second features). Page 3, Section “1. Introduction” from Palffy recites RTCnet involves a radar cube that is a 3D data matrix with axes corresponding to range, azimuth, and velocity. Page 5, Section “A. Pre-Processing” from Palffy further recites a 3D block of the radar cube is cropped around each radar target’s grid cell with radius in range/azimuth/Doppler dimensions (including data from a region of interest of the second radar spectrum).)
“providing a second output that is configured to map the second features to a prediction for a task, including: (i) a prediction for a classification, or (ii) a prediction for an object detection;” (Page 6, Section “3) Score Calculation” from Palffy recites a second output from a second module is mapped by flattening and concatenations second target-level features into classification scores (prediction for the task, including (i) a prediction for a classification).)
“providing a reference for the prediction for the task, including a label indicating a ground truth of the classification or the object detection;” (Page 6, Section “IV. Dataset” from Palffy recites how the class labels from a camera sensor are used as reference and ground truth labels as a reference for the prediction of the task, where ground-truth classification labels are obtained for radar target using camera sensors.)
“mapping the second features with the second output to the prediction for the task;” (Page 6, Section “3) Score Calculation” from Palffy recites a second output from a second module is mapped by flattening and concatenations second target-level features into classification scores (prediction for the task).)
“and training the learned first model, including updating the learned weights, depending on a difference between the prediction for the task and the reference.” (Page 7, Section “B. Implementation” from Palffy recites training a feature-extracting model’s weights based on cross entropy loss, which is akin to a measure of a difference between a predicted classification and a ground-truth reference label.)
With respect to Claim 8:
Palffy and Blaes combined teach:
“wherein the second output includes a second neural network that has second weights, (Page 6, Section “3) Score Calculation” from Palffy recites a second output from a second module is mapped by flattening and concatenations second target-level features into classification scores. Page 5, Section “III. Proposed Method” from Palffy recites a third part where classification scores are provided by two fully connected layers, akin to a second neural network that has second weights.)
wherein the method further comprises learning the second weights depending on the difference between the prediction for the task and the reference.” (Page 7, Section “B. Implementation” from Palffy recites training a feature-extracting model’s weights based on cross entropy loss, which is akin to a measure of a difference between a predicted classification and a ground-truth reference label.)
With respect to Claim 9:
Palffy and Blaes combined teach:
“capturing a radar spectrum with a radar system;” (Column 19, Lines 53-57 from Blaes recite a radar sensor attached to a vehicle where the radar sensor may capture radar data, akin to capturing a radar spectrum with a radar system.)
“determining the prediction for the task depending on the captured radar spectrum with the trained first model or the trained second model;” (Column 30, Lines 20-24 from Blaes recites a previously trained model being applied to radar data captured by an autonomous vehicle system to determine two-dimensional velocity for an object. Column 13, Lines 14-16 from Blaes recite post processing may be performed on sparse object state representation to generate object data that is then outputted to a prediction/planning system for use in making operational decision for the platform or autonomous vehicle.)
“and actuating a technical system depending on the prediction for the task.” (Column 13, Lines 14-16 from Blaes recite post processing may be performed on sparse object state representation to generate object data that is then outputted to a prediction/planning system for use in making operational decision for the platform or autonomous vehicle. Column 15, Lines 33-37 recite the vehicle computing device stores the planning system and prediction system. Column 16, Lines 4-7 from Blaes recite the planning system may be used to determine a path for the vehicle to follow to traverse through a physical environment, with could depend on the outputted prediction used to make operational decisions for the vehicle.)
With respect to Claim 10:
Palffy and Blaes combined teach:
“learning the first model, including learning the weights, with unlabeled data from a plurality of first radar spectra, including range-azimuth spectra, and/or range-velocity spectra and/or range-polarization spectra;” (Column 32, Lines 9-12 from Blaes recite determining a loss function based on a first estimated velocity and a second estimated velocity, and adjust one or more parameters of the model based on the loss function (learning the weights). Column 8, Lines 4-12 from Blaes recite training data is not labeled for object velocities (unlabeled data), and only for object locations. Page 3, Section “1. Introduction” from Palffy recites RTCnet involves a radar cube that is a 3D data matrix with axes corresponding to range, azimuth, and velocity.)
“and training the learned first model or the second model with data from a plurality of second radar spectra, including range-azimuth spectra and/or range-velocity spectra and/or range-polarization spectra, wherein the data from the plurality of second radar spectra is labelled with the reference.” (Page 7, Section “B. Implementation” from Palffy recites using PyTorch to train a model with cross-entropy loss in 10 training epochs. Per the teachings of Claim 6, the learned first model was first pretrained using cross entropy and unlabeled radar spectrum data before being trained again with labeled second radar spectra data. Page 3, Section “1. Introduction” from Palffy recites RTCnet involves uses a radar cube that is a 3D data matrix with axes corresponding to range, azimuth, and velocity. Page 6, Section “IV. Dataset” from Palffy recites the real-world dataset the model is trained on, where mislabeled ground truths are correct, meaning the data from the plurality of second radar spectra is labeled with the reference.)
Claim 7 is rejected under USC 103 as being unpatentable over Blaes et al. (US Patent Application Number US12221115B1 filed on April 29, 2022, hereinafter “Blaes”), in view of “CNN Based Road User Detection Using the 3D Radar Cube” by Palffy et al. (non-patent literature published on April 25 2020, hereinafter “Palffy”), in further view of “Smart App Attack: Hacking Deep Learning Models in Android Apps” by Huang et al. (Non-patent literature published on April 23, 2022, hereinafter “Huang”).
With respect to Claim 7:
Palffy and Blaes combined teach:
“mapping data of a second radar spectrum, including data from a region of interest of the second radar spectrum, with the second model to second features;” (Page 5, Section “III. Proposed Method” from Palffy recites the RTCnet network consists of three parts, where it first encodes the data in spatial and radar domains like range and azimuth and then is applied on the output to extract class information from the distribution of speed. The third part provides classifications scores by two fully connected layers (mapping data of a second radar spectrum to second features). Page 3, Section “1. Introduction” from Palffy recites RTCnet involves a radar cube that is a 3D data matrix with axes corresponding to range, azimuth, and velocity. Page 5, Section “A. Pre-Processing” from Palffy further recites a 3D block of the radar cube is cropped around each radar target’s grid cell with radius in range/azimuth/Doppler dimensions (including data from a region of interest of the second radar spectrum).)
“providing a second output that is configured to map the second features to a prediction for a task, including a prediction for a classification or a prediction for an object detection;” (Page 6, Section “3) Score Calculation” from Palffy recites a second output from a second module is mapped by flattening and concatenations second target-level features into classification scores (prediction for the task, including (i) a prediction for a classification).)
“providing a reference for the prediction for the task, including a label indicating a ground truth of the classification or a ground truth of the object detection;” (Page 6, Section “IV. Dataset” from Palffy recites how the class labels from a camera sensor are used as reference and ground truth labels as a reference for the prediction of the task, where ground-truth classification labels are obtained for radar target using camera sensors.)
“mapping the second features with the second output to the prediction for the task;” (Page 6, Section “3) Score Calculation” from Palffy recites a second output from a second module is mapped by flattening and concatenations second target-level features into classification scores (prediction for the task).)
“and training the second model, including updating the learned weights, depending on a difference between the prediction for the task and the reference.” (Page 7, Section “B. Implementation” from Palffy recites training a feature-extracting model’s weights based on cross entropy loss, which is akin to a measure of a difference between a predicted classification and a ground-truth reference label.)
Palffy and Blaes combined do not appear to explicitly disclose:
“providing a second model that includes the same architecture as the learned first model, including providing the second model with the learned neural network including the learned weights;”
“mapping data of a second radar spectrum, including data from a region of interest of the second radar spectrum, with the second model to second features;”
“and training the second model, including updating the learned weights, depending on a difference between the prediction for the task and the reference.”
However, Huang teaches:
“providing a second model that includes the same architecture as the learned first model, including providing the second model with the learned neural network including the learned weights;” (Page 3, Section “B. Transfer Learning” recites during initialization, a pre-trained model with N layers is selected as a base model and copies both the structure and parameters of the base model in order to provide a second model that includes the same architecture as the learned first model.)
“mapping data of a second radar spectrum, including data from a region of interest of the second radar spectrum, with the second model to second features;” (Page 3, Section “B. Transfer Learning” recites during initialization, a pre-trained model with N layers is selected as a base model and copies both the structure and parameters of the base model in order to provide a second model that includes the same architecture as the learned first model.)
“and training the second model, including updating the learned weights, depending on a difference between the prediction for the task and the reference.” (Page 3, Section “B. Transfer Learning” recites during initialization, a pre-trained model with N layers is selected as a base model and copies both the structure and parameters of the base model in order to provide a second model that includes the same architecture as the learned first model.)
It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the present application to combine the teachings of Blaes and the teachings of Palffy with the teachings of Huang, which are all in the same field of invention. A PHOSITA would be motivated to combine the self-supervised physical attribute training and radar spectra feature extractor framework from Blaes and Palffy with the transfer-learning technique of Huang, a common technique known in the field, in order to keep the original pretrained model intact and available so that it can be reused for multiple different downstream tasks.
Conclusion
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/Vibha Bhat/Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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