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 .
Priority
Examiner acknowledges no foreign priority is claimed.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 5/20/2024 and 10/13/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered if signed and initialed by the Examiner.
Examiner’s Note on Restriction
Applicant has received an action on the merits for the originally presented invention. The Examiner notes that Applicant has presented two slightly differing systems claims, and that the Examiner has examined these claims as a single invention because the claims are obvious variants of each other. However, if future amendments to the claims or newly submitted claims are directed towards inventions that are independent or distinct form the invention originally claimed, the Examiner may review these claims in view of MPEP section 806 which is partially quoted below.
The general principles relating to distinctness or independence may be summarized as follows:
(A) Where inventions are independent (i.e., no disclosed relation there between), restriction to one thereof is ordinarily proper,
(B) Where inventions are related as disclosed but are distinct as claimed, restriction may be proper.
(C) Where inventions are related as disclosed but are not distinct as claimed, restriction is never proper.
(D) A reasonable number of species may be claimed when there is an allowable claim generic thereto.
Claim Interpretation
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) is/are:
Claim 1: a plurality of transmitter modules configured to
Claim 1: a plurality of receiver modules configured to
Claim 9: at least one receiver module configured to
Claim 16: a radar system receiver module
A specialized function must be supported in the specification by the computer and the algorithm that the computer uses to perform the claimed specialized function.
The following have been identified as the structure for the transmitter module and receiver module:
¶[0015], ¶[0016], ¶[0019], ¶[0022] of the published specification provides description that accomplishes the claimed function associated with the claimed transmitter module and fig. 1 discloses radar device 10 on which the module is incorporated. Therefore, there is sufficient structure for the transmitter module.
¶[0015], ¶[0017], ¶[0018] of the published specification provides description that accomplishes the claimed function associated with the claimed receiver module and fig. 1 discloses the radar device 10 on which the module is incorporated. Therefore, there is sufficient structure for the converting unit.
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 § 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 1-20 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 “a plurality of supports”, “a set of selected supports”, “an optimized set of selected supports”, “a first support”, “a second support”. The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 2 recites “set of selected supports”, The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 3 recites “a second set of selected supports”, “a third support,” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 4 recites “the second set of selected supports.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 5 recites “a third set of selected supports”, “a fourth support.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 6 recites “the third set of selected supports.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claims 7-8 depends on independent claim 1, and therefore are also rejected.
Claim 9 recites “a plurality of supports”, “a set of selected supports”, “a second subset of supports”, “supports”, “an optimized set of selected supports”, “a first support”, “a second support.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 10 recites “a second set of selected supports”, “a third support”, “the optimized set of selected supports”, “the second set of selected supports”, “the set of selected supports.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 11 recites “supports.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 12 recites “a third set of selected supports”, “a fourth support”, “the set of selected supports”, “third set of selected supports.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 13 recites “the third set of selected supports.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claims 14-15 depends on claim 9 and therefore are also rejected.
Claim 16 recites “a plurality of supports”, “a set of selected supports”, “the set of selected supports”, “a first subset of the plurality of supports”, “a second subset of supports”, “supports of the plurality of supports”, “an optimized set of selected supports”, “a first support”, “a second support”, “the optimized set of selected supports”, “the set of selected supports.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 17 recites “a second set of select supports”, “a third support”, “the optimized set of selected supports, “the second set of selected supports”, “the set of selected supports.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 18 recites “the second set of selected supports.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 19 recites “a third set of selected supports”, “a fourth support”, “the set of selected supports”, “the third set of selected supports.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim 20 recites “the third set of selected supports.” The specification of the instant application merely describes “However, the performance of MP and OMP algorithms can be affected by the algorithms’ sensitivity to antenna array geometry and support selection, sensitivity to angle quantitation, and the growing burden of least-squares (LS) computation in OMP as more objects are found. Both MP and OMP are referred to as forward algorithms because they start from an empty set and then add one support into the set at each iteration”, in paragraph 28. It is not clear what is meant by “support” in claim 1. It is unclear if “support” is claimed as “support vector”, “support matrix” or some other parameter or feature. The applicant needs to clarify.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Claim 1. A radar system, comprising:
a plurality of transmitter modules configured to transmit a plurality of transmitted radar signals;
a plurality of receiver modules configured to receive reflections of the plurality of transmitted radar signals reflected by at least one object and to generate signals based on the received reflections; and
a controller configured to: determine a measurement vector using signals received by the plurality of receiver modules,
determine a steering vector matrix,
determine a plurality of supports using the measurement vector,
execute a regression algorithm to determine a weight vector that defines a relationship between the measurement vector and the steering vector matrix by: defining a set of selected supports out of the plurality of supports, executing an exchange operation to determine an optimized set of selected supports by removing a first support from the set of selected supports and adding a second support to the set of selected supports; and
calculating the weight vector using the optimized set of selected supports, and
determine an estimated angle of arrival of a first object by correlating the steering vector matrix to the measurement vector using the weight vector.
101 Analysis - Step 1: Statutory category – Yes
The claim recites a system including at least one structure. The claim falls within one of the four statutory categories. See MPEP 2106.03.
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III).
The claim recites the limitation of determine a measurement vector using signals received by the plurality of receiver modules, determine a steering vector matrix,
determine a plurality of supports using the measurement vector, execute a regression algorithm to determine a weight vector that defines a relationship between the measurement vector and the steering vector matrix by: defining a set of selected supports out of the plurality of supports, executing an exchange operation to determine an optimized set of selected supports by removing a first support from the set of selected supports and adding a second support to the set of selected supports; calculating the weight vector using the optimized set of selected supports, and determine an estimated angle of arrival of a first object by correlating the steering vector matrix to the measurement vector using the weight vector.
These limitations, as drafted, are a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind. For example, the claim encompasses a person looking at information and making a simple judgement of visually determining that a plurality of transmitter modules are transmitting, a plurality of receiver modules receiving reflections, and mentally estimating, or using a pen and paper, to determine angle of arrival of object or target.
Thus, the claim recites a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional elements or steps of a plurality of transmitter modules configured to transmit a plurality of transmitted radar signals; a plurality of receiver modules configured to receive reflections of the plurality of transmitted radar signals reflected by at least one object and to generate signals based on the received reflections; a controller configured.
The detecting an indication of a position and receiving the indication are recited at a high level of generality (i.e., as a general means of collecting information), and amount to mere data gathering, which is a form of insignificant extra-solution activity. The “a plurality of transmitter modules configured to transmit a plurality of transmitted radar signals” and “a plurality of receiver modules configured to receive reflections of the plurality of transmitted radar signals reflected by at least one object and to generate signals based on the received reflections” and the “a controller configured” merely describes how to generally “apply” the otherwise mental judgements using generic or general-purpose system components and generic computer components.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the transmitting step and the receiving step were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The background recites that the sensors are all conventional radar sensors mounted on the vehicle, and the specification does not provide any indication that the vehicle controller is anything other than a conventional computer within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer.
Thus, the claim is ineligible.
Claim 9
Claim 9. A radar system, comprising:
at least one receiver module configured to receive radar signals; and
a controller configured to: determine a measurement vector using the radar signals,
determine a steering vector matrix,
determine a plurality of supports using the measurement vector,
execute a regression algorithm to determine a weight vector that defines a relationship between the measurement vector and the steering vector matrix by: defining a set of selected supports, wherein the set of selected supports includes a first subset of the plurality of supports, wherein a second subset of supports includes supports of the plurality of supports that are not in the first subset;
executing an exchange operation to determine an optimized set of selected supports by removing a first support from the set of selected supports and adding a second support from the second subset into the optimized set of selected supports, wherein the regression algorithm is associated with an optimization problem, and a first value of the optimization problem calculated using the optimized set of selected supports is less than a second value of the optimization problem calculated using the set of selected supports; and
calculating the weight vector using the optimized set of selected supports, and
determine an estimated angle of arrival of a first object by correlating the steering vector matrix to the measurement vector using the weight vector.
101 Analysis - Step 1: Statutory category – Yes
The claim recites a system including at least one structure. The claim falls within one of the four statutory categories. See MPEP 2106.03.
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III).
The claim recites the limitation of determine a measurement vector using the radar signals, determine a steering vector matrix, determine a plurality of supports using the measurement vector, execute a regression algorithm to determine a weight vector that defines a relationship between the measurement vector and the steering vector matrix by: defining a set of selected supports, wherein the set of selected supports includes a first subset of the plurality of supports, wherein a second subset of supports includes supports of the plurality of supports that are not in the first subset; executing an exchange operation to determine an optimized set of selected supports by removing a first support from the set of selected supports and adding a second support from the second subset into the optimized set of selected supports, wherein the regression algorithm is associated with an optimization problem, and a first value of the optimization problem calculated using the optimized set of selected supports is less than a second value of the optimization problem calculated using the set of selected supports; calculating the weight vector using the optimized set of selected supports, determine an estimated angle of arrival of a first object by correlating the steering vector matrix to the measurement vector using the weight vector.
These limitations, as drafted, are a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind. For example, the claim encompasses a person looking at information and making a simple judgement of visually determining that at least one receiver module receiving reflections, and mentally estimating, or using a pen and paper, to determine angle of arrival of object or target.
Thus, the claim recites a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional elements or steps of at least one receiver module configured to receive radar signals; a controller configured.
The detecting an indication of a position and receiving the indication are recited at a high level of generality (i.e., as a general means of collecting information), and amount to mere data gathering, which is a form of insignificant extra-solution activity. The “at least one receiver module configured to receive radar signals” and the “a controller configured” merely describes how to generally “apply” the otherwise mental judgements using generic or general-purpose system components and generic computer components.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the transmitting step and the receiving step were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The background recites that the sensors are all conventional radar sensors mounted on the vehicle, and the specification does not provide any indication that the vehicle controller is anything other than a conventional computer within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer.
Thus, the claim is ineligible.
Claim 16
Claim 16. A method, comprising:
receiving radar signals using a radar system receiver module;
determining a measurement vector using the radar signals;
determining a steering vector matrix,
determining a plurality of supports using the measurement vector,
executing a regression algorithm to determine a weight vector that defines a relationship between the measurement vector and the steering vector matrix by: defining a set of selected supports, wherein the set of selected supports includes a first subset of the plurality of supports, wherein a second subset of supports includes supports of the plurality of supports that are not in the first subset;
executing an exchange operation to determine an optimized set of selected supports by removing a first support from the set of selected supports and adding a second support from the second subset into the optimized set of selected supports, wherein the regression algorithm is associated with an optimization problem, and a first value of the optimization problem calculated using the optimized set of selected supports is less than a second value of the optimization problem calculated using the set of selected supports; and
calculating the weight vector using the optimized set of selected supports, and
determine an estimated angle of arrival of a first object by correlating the steering vector matrix to the measurement vector using the weight vector.
101 Analysis - Step 1: Statutory category – Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories. See MPEP 2106.03.
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III).
The claim recites the limitation of determining a measurement vector using the radar signals; determining a steering vector matrix, determining a plurality of supports using the measurement vector, executing a regression algorithm to determine a weight vector that defines a relationship between the measurement vector and the steering vector matrix by: defining a set of selected supports, wherein the set of selected supports includes a first subset of the plurality of supports, wherein a second subset of supports includes supports of the plurality of supports that are not in the first subset; executing an exchange operation to determine an optimized set of selected supports by removing a first support from the set of selected supports and adding a second support from the second subset into the optimized set of selected supports, wherein the regression algorithm is associated with an optimization problem, and a first value of the optimization problem calculated using the optimized set of selected supports is less than a second value of the optimization problem calculated using the set of selected supports; calculating the weight vector using the optimized set of selected supports, determine an estimated angle of arrival of a first object by correlating the steering vector matrix to the measurement vector using the weight vector.
These limitations, as drafted, are a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “using a radar system receiver module,” nothing in the claim elements precludes the step from practically being performed in the mind. For example, but for the recitation of “using a radar system receiver module,” the claim encompasses a person looking at information and making a simple judgement of visually determining radar system module receiving, mentally estimating, or using a pen and paper, to determine angle of arrival of an object or target. The mere nominal recitation of “using a radar system receiver module” does not take the claim limitations out of the mental process grouping.
Thus, the claim recites a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional elements or steps of receiving radar signals using a radar system receiver module.
The receiving radar signals is recited at a high level of generality (i.e., as a general means of collecting information), and amount to mere data gathering, which is a form of insignificant extra-solution activity. The “radar system receiver” of the vehicle merely describes how to generally “apply” the otherwise mental judgements using generic or general-purpose vehicle components and generic computer components. The data processing system is recited at a high level of generality and is merely automates the determining steps.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving step was considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The background recites that the sensors are all conventional radar sensors mounted on the vehicle, and the specification does not provide any indication that the vehicle controller is anything other than a conventional computer within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer.
Thus, the claim is ineligible.
Dependent Claims
Dependent claims 2-8, 10-15 and 17-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-8, 10-15 and 17-20 are not patent eligible under the same rationale as provided for in the rejection of the independent claims.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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 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.
For applicant’s benefit portions of the cited reference(s) have been cited to aid in the review of the rejection(s). While every attempt has been made to be thorough and consistent within the rejection it is noted that the PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS. See MPEP 2141.02 VI.
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.
Claims 1-2, 7-9, 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2022/0196798 A1), and further in view of Wu et al. (US 2022/0268883 A1).
Regarding claim 1, Chen et al. (‘798) discloses “a radar system (paragraph 2: radar, antenna array configuration in radar, processing of radar signals by one or more artificial neural networks), comprising:
a plurality of transmitter modules configured to transmit a plurality of transmitted radar signals (paragraph 84: the radar frontend 103 for example includes one or more (radar) transmitters…the antenna arrangement 102 may include multiple transmit antennas in the form of a transmit antenna array);
a plurality of receiver modules configured to receive reflections of the plurality of transmitted radar signals reflected by at least one object and to generate signals based on the received reflections (paragraph 84: the radar frontend 103 for example includes one or more (radar) receivers…the antenna arrangement 102 may include multiple receive antennas in the form of a receive antenna array; paragraph 86: the radio transmit signal 105 is reflected by the object 106 resulting in an echo 107; paragraph 99: for the detection of the object 213, the radar processor 210 transmits, using the radar frontend 211 and the antenna arrangement 212, a radio transmit signal 214…the radio transmit signal 214 is reflected by the object 213 resulting in an echo 215); and
a controller (paragraph 94: the actuator can respond to commands given by the controller 206 (the so-called activation); paragraph 95: the term “controller” may be understood as any type of logic implementing entity, which may include, for example, a circuit and/or a processor capable of executing software stored in a storage medium, firmware, or a combination thereof, and which can issue instructions, e.g. to an actuator in the present example…the controller may be configured, for example, by program code (e.g., software) to control the operation of a system; paragraph 96: the controller 206 is in communication with a radar processor 210) configured to:
determine a measurement vector using signals received by the plurality of receiver modules (paragraph 170: the output value 1004 for an input value 1003 is obtained by applying a filter matrix 1005 to the input value 1003 and its surrounding values 1006 (represented in FIG. 10 by the components of the matrix except the input value 1003, for which exemplary entries are shown)…the size of the surrounding area from which the surrounding values 1006 are taken is given by the filter matrix 1005. The surrounding values 1006 together with the input value 1003 form a submatrix of the input data, which has the same size as the filter matrix 1005. The filter matrix 1005 is applied to the input value 1005 and its surrounding values 1006 by forming the inner product of the filter matrix 1005 with the submatrix (both matrices are regarded as vectors)),
determine a steering vector matrix (paragraph 368: in a coarse step, the antenna response is transformed into angular domain based on steering vectors and array factors),
determine a plurality of supports using the measurement vector (paragraph 76: References herein to classification models may contemplate a model that implements, e.g., any one or more of the following techniques: linear classifiers (e.g., logistic regression or naive Bayes classifier), support vector machines, decision trees, boosted trees, random forest, neural networks, or nearest neighbor),
execute a regression algorithm to determine a weight vector (paragraph 283: the machine learning algorithm may include a vector of weighted values…each of the weighted values may correspond to a different object parameter…the receiver may determine the one or more object parameters to include a location, relative speed, and a classification of each object detected in the scene…the machine learning algorithm may include large weight vector θ)”,
“determine an estimated angle of arrival (paragraph 146: Figure 6: the determination of an angle of arrival).”
Chen et al. (‘798) does not explicitly disclose a weight vector “that defines a relationship between the measurement vector and the steering vector matrix by: defining a set of selected supports out of the plurality of supports, executing an exchange operation to determine an optimized set of selected supports by removing a first support from the set of selected supports and adding a second support to the set of selected supports; and calculating the weight vector using the optimized set of selected supports, and determine an estimated angle of arrival of a first object by correlating the steering vector matrix to the measurement vector using the weight vector.”
Wu et al. (‘883) relates to radar apparatuses/systems and related methods. Wu et al. (‘883) teaches “that defines a relationship between the measurement vector and the steering vector matrix (paragraph 61: as is known the greedy algorithm starts by modelling the angle estimation problem as a linear regression problem, that is, by modelling the array output measurement vector x as a product of an array steering matrix A and a spatial frequency support amplitude vector c plus noise e, where each column of A is a steering vector of the array steered to a support spatial frequency
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in normalized unit (between 0 and 1) upon which one desires to evaluate the amplitude of a target and the spatial sampling positions
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in normalized integer units. To achieve high angular resolution, a large number of supports can be established, thereby dividing up the 0˜2π radian frequency spectrum resulting in a fine grid and a “wide” A matrix (that is, number of columns, which corresponds to the number of supports, is much greater than the number of rows, which corresponds to the number of array outputs or measurements). Since A is a wide matrix, this implies that the number of unknowns (vector c) is greater than the number of knowns (vector x) and the solving of equation
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is an under-determined linear regression problem, where x and e are N×1 vectors, A is a N×M matrix, and c is a M×1 vector…this is seen below as:
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) by: defining a set of selected supports out of the plurality of supports (paragraph 26: using a matrix-based model in which each of the possible spectrum support vectors is drawn from a distinct distribution),
executing an exchange operation to determine an optimized set of selected supports by removing a first support from the set of selected supports and adding a second support to the set of selected supports (paragraph 30: to help offset burdens in connection with processing of the matrix-based computations, in specific examples the above type of approach may be further enhanced by including with each iterative update, an automatic pruning effort to eliminate certain of the less-probable support vectors from among the many most probable spectrum support vectors…these are selected as the support vectors having amplitudes which are insignificant relative to a statistical expectation of the support vector of in a preceding iteration…the statistical expectation among a plurality of support vectors may be, for example, an average or a median vector or another middle-ground selection taken from within a limited range such as the mean or median plus and/or minus seven percent; paragraph 63: the identification of the most probably support (without loss of generality, assume one support is to be selected at a time) is by correlating the columns of A with measurement vector and support frequency that leads to the highest correlation is selected…the correlation vector, y, can be directly computed by
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for the first iteration where
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denotes the transpose-conjugate (i.e. Hermitian transpose) of A…for the
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iteration, the correlation output is computed as
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where
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is the residual measurement vector computed in the k-1-th iteration and
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…the found support of the
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iteration is then added to a solution support set
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); and
calculating the weight vector using the optimized set of selected supports, and determine an estimated angle of arrival of a first object by correlating the steering vector matrix to the measurement vector using the weight vector (paragraph 66: in order to achieve high angular resolution, many supports much more than the number of measurements (i.e. N<<M) is modelled and estimated. This naturally leads to very high Coherence which in turn results in small K or recoverable target amplitudes. One way to reduce the Coherence is by randomizing the spatial sampling of the steering vectors. For example, one may create a N′×1 steering vector where N′>N, and randomly (following any sub-Gaussian or Gaussian probability distribution) deleting the samples to obtain a N×1 vector. The resulting matrix is called Random Fourier matrix. In the following equation, the matrix A represents such a Random Fourier matrix where
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are N integers randomly selected from {0, 1, . . . , N′}; paragraph 93: such a pruning aspect of the present disclosure may be beneficial as pruning-type sparse learning method applicable for processing output data, indicative of reflection signals, passed from a sparse array. It is appreciated that such an array may be in any of a variety of different forms such as those disclosed as above. In each instance, the logic (or processing) circuitry receives the output data as being indicative of signal magnitude (e.g., in a spectrum support vector) of the reflection signals via the sparse array, and then discerns angle-of-arrival information for the output data by performing certain steps in an iterative manner for implementation of a sparse learning method which includes pruning, for each iterative update, certain of the plurality of spectrum-related support vectors having respective amplitudes which are insignificant relative to the statistical expectation of the support vector in a preceding iteration).”
It would have been obvious to one of ordinary skill-in-the-art before the effective filing date of the claimed invention to modify the radar system of Chen et al. (‘798) with the teaching of Wu et al. (‘883) for accurate angle of arrival measurement (Wu et al. (‘883) – paragraph 6). In addition, both of the prior art references, (Chen et al. (‘798) and Wu et al. (‘883)) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, using radar devices and systems in which objects are detected by sensing and processing reflections or radar signals for discerning angle-of-arrival information.
Regarding claim 2, which is dependent on independent claim 1, Chen et al. (‘798)/Wu et al. (‘883) discloses the radar system of claim 1. Chen et al. (‘798) does not explicitly disclose “the regression algorithm is associated with an optimization problem, and a first value of the optimization problem calculated using the optimized set of selected supports is less than a second value of the optimization problem calculated using the set of selected supports.”
Wu et al. (‘883) relates to radar apparatuses/systems and related methods. Wu et al. (‘883) teaches “the regression algorithm is associated with an optimization problem, and a first value of the optimization problem calculated using the optimized set of selected supports is less than a second value of the optimization problem calculated using the set of selected supports (paragraph 96: such sparse learning algorithms have a large number of intrinsic parameters to be handled by the algorithms…for a problem size of s supports, there exists s number of τ (known as the precision) parameters and a σ.sub.n.sup.2 (known as the noise variance) parameter that are to be computed and updated in each iteration…the optimization automatically adapts and finds the best values of these s+1 internal parameters and so the tuning of these parameters is not done manually, which is a significant advantage of SBL and pSBL algorithms…the convergence of these parameters, however, may be slow because of the sheer large dimension of optimization problem…the problem is somewhat mitigated by the support pruning implemented in pSBL, which reduces the number of supports from M to s so the dimension of the problem is already reduced…given limited number of measurements, intuitively, the fewer number of parameters required to be estimated the more robust the algorithm can be. So, by further reducing the dimension of the parameter space, more robust performance can be achieved).”
It would have been obvious to one of ordinary skill-in-the-art before the effective filing date of the claimed invention to modify the radar system of Chen et al. (‘798) with the teaching of Wu et al. (‘883) for accurate angle of arrival measurement (Wu et al. (‘883) – paragraph 6). In addition, both of the prior art references, (Chen et al. (‘798) and Wu et al. (‘883)) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, using radar devices and systems in which objects are detected by sensing and processing reflections or radar signals for discerning angle-of-arrival information.
Regarding claim 7, which is dependent on independent claim 1, Chen et al. (‘798)/Wu et al. (‘883) discloses the radar system of claim 1. Chen et al. (‘798) does not explicitly disclose “the steering vector matrix includes a plurality of spatial frequencies associated with an array pattern.”
Wu et al. (‘883) relates to radar apparatuses/systems and related methods. Wu et al. (‘883) teaches “the steering vector matrix includes a plurality of spatial frequencies associated with an array pattern (paragraph 61: as is known the greedy algorithm starts by modelling the angle estimation problem as a linear regression problem, that is, by modelling the array output measurement vector x as a product of an array steering matrix A and a spatial frequency support amplitude vector c plus noise e, where each column of A is a steering vector of the array steered to a support spatial frequency
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in normalized unit (between 0 and 1) upon which one desires to evaluate the amplitude of a target and the spatial sampling positions
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in normalized integer units…to achieve high angular resolution, a large number of supports can be established, thereby dividing up the 0˜2π radian frequency spectrum resulting in a fine grid and a “wide” A matrix (that is, number of columns, which corresponds to the number of supports, is much greater than the number of rows, which corresponds to the number of array outputs or measurements)…since A is a wide matrix, this implies that the number of unknowns (vector c) is greater than the number of knowns (vector x) and the solving of equation
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is an under-determined linear regression problem, where x and e are N×1 vectors, A is a N×M matrix, and c is a M×1 vector…this is seen below as:
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).”
It would have been obvious to one of ordinary skill-in-the-art before the effective filing date of the claimed invention to modify the radar system of Chen et al. (‘798) with the teaching of Wu et al. (‘883) for accurate angle of arrival measurement (Wu et al. (‘883) – paragraph 6). In addition, both of the prior art references, (Chen et al. (‘798) and Wu et al. (‘883)) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, using radar devices and systems in which objects are detected by sensing and processing reflections or radar signals for discerning angle-of-arrival information
Regarding claim 8, which is dependent on independent claim 1, Chen et al. (‘798)/Wu et al. (‘883) discloses the radar system of claim 1. Chen et al. (‘798) does not explicitly disclose “the relationship between the steering matrix and the measurement vector is of the form
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, wherein y is the measurement vector, A is the steering vector matrix, x is a spatial frequency vector and
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is a noise factor.”
Wu et al. (‘883) relates to radar apparatuses/systems and related methods. Wu et al. (‘883) teaches “the relationship between the steering matrix and the measurement vector is of the form
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, wherein y is the measurement vector, A is the steering vector matrix, x is a spatial frequency vector and
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is a noise factor (paragraph 61: as is known the greedy algorithm starts by modelling the angle estimation problem as a linear regression problem, that is, by modelling the array output output measurement vector x as a product of an array steering matrix A and a spatial frequency support amplitude vector c plus noise e, where each column of A is a steering vector of the array steered to a support spatial frequency
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in normalized unit (between 0 and 1) upon which one desires to evaluate the amplitude of a target and the spatial sampling positions
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in normalized integer units…to achieve high angular resolution, a large number of supports can be established, thereby dividing up the 0˜2π radian frequency spectrum resulting in a fine grid and a “wide” A matrix (that is, number of columns, which corresponds to the number of supports, is much greater than the number of rows, which corresponds to the number of array outputs or measurements)…since A is a wide matrix, this implies that the number of unknowns (vector c) is greater than the number of knowns (vector x) and the solving of equation
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is an under-determined linear regression problem, where x and e are N×1 vectors, A is a N×M matrix, and c is a M×1 vector…this is seen below as:
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).”
It would have been obvious to one of ordinary skill-in-the-art before the effective filing date of the claimed invention to modify the radar system of Chen et al. (‘798) with the teaching of Wu et al. (‘883) for accurate angle of arrival measurement (Wu et al. (‘883) – paragraph 6). In addition, both of the prior art references, (Chen et al. (‘798) and Wu et al. (‘883)) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, using radar devices and systems in which objects are detected by sensing and processing reflections or radar signals for discerning angle-of-arrival information
Regarding independent claim 9, Chen et al. (‘798) discloses “a radar system (paragraph 2: radar, antenna array configuration in radar, processing of radar signals by one or more artificial neural networks), comprising:
at least one receiver module configured to receive radar signals (paragraph 84: the radar frontend 103 for example includes one or more (radar) receivers…the antenna arrangement 102 may include multiple receive antennas in the form of a receive antenna array; paragraph 86: the radio transmit signal 105 is reflected by the object 106 resulting in an echo 107; paragraph 99: for the detection of the object 213, the radar processor 210 transmits, using the radar frontend 211 and the antenna arrangement 212, a radio transmit signal 214…the radio transmit signal 214 is reflected by the object 213 resulting in an echo 215); and
a controller (paragraph 94: the actuator can respond to commands given by the controller 206 (the so-called activation); paragraph 95: the term “controller” may be understood as any type of logic implementing entity, which may include, for example, a circuit and/or a processor capable of executing software stored in a storage medium, firmware, or a combination thereof, and which can issue instructions, e.g. to an actuator in the present example…the controller may be configured, for example, by program code (e.g., software) to control the operation of a system; paragraph 96: the controller 206 is in communication with a radar processor 210) configured to:
determine a measurement vector using the radar signals (paragraph 170: the output value 1004 for an input value 1003 is obtained by applying a filter matrix 1005 to the input value 1003 and its surrounding values 1006 (represented in FIG. 10 by the components of the matrix except the input value 1003, for which exemplary entries are shown) …the size of the surrounding area from which the surrounding values 1006 are taken is given by the filter matrix 1005…the surrounding values 1006 together with the input value 1003 form a submatrix of the input data, which has the same size as the filter matrix 1005…the filter matrix 1005 is applied to the input value 1005 and its surrounding values 1006 by forming the inner product of the filter matrix 1005 with the submatrix (both matrices are regarded as vectors)),
determine a steering vector matrix (paragraph 368: in a coarse step, the antenna response is transformed into angular domain based on steering vectors and array factors),
determine a plurality of supports using the measurement vector (paragraph 76: References herein to classification models may contemplate a model that implements, e.g., any one or more of the following techniques: linear classifiers (e.g., logistic regression or naive Bayes classifier), support vector machines, decision trees, boosted trees, random forest, neural networks, or nearest neighbor),
execute a regression algorithm to determine a weight vector (paragraph 283: the machine learning algorithm may include a vector of weighted values…each of the weighted values may correspond to a different object parameter…the receiver may determine the one or more object parameters to include a location, relative speed, and a classification of each object detected in the scene…the machine learning algorithm may include large weight vector θ)”,
“determine an estimated angle of arrival (paragraph 146: Figure 6: the determination of an angle of arrival).”
Chen et al. (‘798) does not explicitly disclose a weight vector “that defines a relationship between the measurement vector and the steering vector matrix by: defining a set of selected supports, wherein the set of selected supports includes a first subset of the plurality of supports, wherein a second subset of supports includes supports of the plurality of supports that are not in the first subset; executing an exchange operation to determine an optimized set of selected supports by removing a first support from the set of selected supports and adding a second support from the second subset into the optimized set of selected supports, wherein the regression algorithm is associated with an optimization problem, and a first value of the optimization problem calculated using the optimized set of selected supports is less than a second value of the optimization problem calculated using the set of selected supports; and calculating the weight vector using the optimized set of selected supports, and determine an estimated angle of arrival of a first object by correlating the steering vector matrix to the measurement vector using the weight vector.”
Wu et al. (‘883) Wu et al. (‘883) relates to radar apparatuses/systems and related methods. Wu et al. (‘883) teaches a weight vector “that defines a relationship between the measurement vector and the steering vector matrix (paragraph 61: as is known the greedy algorithm starts by modelling the angle estimation problem as a linear regression problem, that is, by modelling the array output measurement vector x as a product of an array steering matrix A and a spatial frequency support amplitude vector c plus noise e, where each column of A is a steering vector of the array steered to a support spatial frequency
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in normalized unit (between 0 and 1) upon which one desires to evaluate the amplitude of a target and the spatial sampling positions
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in normalized integer units…to achieve high angular resolution, a large number of supports can be established, thereby dividing up the 0˜2π radian frequency spectrum resulting in a fine grid and a “wide” A matrix (that is, number of columns, which corresponds to the number of supports, is much greater than the number of rows, which corresponds to the number of array outputs or measurements)…since A is a wide matrix, this implies that the number of unknowns (vector c) is greater than the number of knowns (vector x) and the solving of equation
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is an under-determined linear regression problem, where x and e are N×1 vectors, A is a N×M matrix, and c is a M×1 vector…this is seen below as:
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) by: defining a set of selected supports (paragraph 26: using a matrix-based model in which each of the possible spectrum support vectors is drawn from a distinct distribution),
wherein the set of selected supports includes a first subset of the plurality of supports, wherein a second subset of supports includes supports of the plurality of supports that are not in the first subset (paragraph 64: the LS-fit is based on solving a new equation
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in LS sense, where
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consists of columns of A of selected support set and elements of
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is a subset of elements of c of the selected supports);
executing an exchange operation to determine an optimized set of selected supports by removing a first support from the set of selected supports and adding a second support from the second subset into the optimized set of selected supports (paragraph 30: to help offset burdens in connection with processing of the matrix-based computations, in specific examples the above type of approach may be further enhanced by including with each iterative update, an automatic pruning effort to eliminate certain of the less-probable support vectors from among the many most probable spectrum support vectors…these are selected as the support vectors having amplitudes which are insignificant relative to a statistical expectation of the support vector of in a preceding iteration…the statistical expectation among a plurality of support vectors may be, for example, an average or a median vector or another middle-ground selection taken from within a limited range such as the mean or median plus and/or minus seven percent; paragraph 63: the identification of the most probably support (without loss of generality, assume one support is to be selected at a time) is by correlating the columns of A with measurement vector and support frequency that leads to the highest correlation is selected…the correlation vector, y, can be directly computed by
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for the first iteration where
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denotes the transpose-conjugate (i.e. Hermitian transpose) of A…for the
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iteration, the correlation output is computed as
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where
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is the residual measurement vector computed in the k-1-th iteration and
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…the found support of the
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iteration is then added to a solution support set
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),
wherein the regression algorithm is associated with an optimization problem, and a first value of the optimization problem calculated using the optimized set of selected supports is less than a second value of the optimization problem calculated using the set of selected supports (paragraph 96: such sparse learning algorithms have a large number of intrinsic parameters to be handled by the algorithms…for a problem size of s supports, there exists s number of τ (known as the precision) parameters and a σ.sub.n.sup.2 (known as the noise variance) parameter that are to be computed and updated in each iteration…the optimization automatically adapts and finds the best values of these s+1 internal parameters and so the tuning of these parameters is not done manually, which is a significant advantage of SBL and pSBL algorithms…the convergence of these parameters, however, may be slow because of the sheer large dimension of optimization problem…the problem is somewhat mitigated by the support pruning implemented in pSBL, which reduces the number of supports from M to s so the dimension of the problem is already reduced…given limited number of measurements, intuitively, the fewer number of parameters required to be estimated the more robust the algorithm can be. So, by further reducing the dimension of the parameter space, more robust performance can be achieved); and
calculating the weight vector using the optimized set of selected supports, and determine an estimated angle of arrival of a first object by correlating the steering vector matrix to the measurement vector using the weight vector (paragraph 66: in order to achieve high angular resolution, many supports much more than the number of measurements (i.e. N<<M) is modelled and estimated. This naturally leads to very high Coherence which in turn results in small K or recoverable target amplitudes. One way to reduce the Coherence is by randomizing the spatial sampling of the steering vectors. For example, one may create a N′×1 steering vector where N′>N, and randomly (following any sub-Gaussian or Gaussian probability distribution) deleting the samples to obtain a N×1 vector…the resulting matrix is called Random Fourier matrix. In the following equation, the matrix A represents such a Random Fourier matrix where
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are N integers randomly selected from {0, 1, . . . , N′}; paragraph 93: such a pruning aspect of the present disclosure may be beneficial as pruning-type sparse learning method applicable for processing output data, indicative of reflection signals, passed from a sparse array. It is appreciated that such an array may be in any of a variety of different forms such as those disclosed as above…in each instance, the logic (or processing) circuitry receives the output data as being indicative of signal magnitude (e.g., in a spectrum support vector) of the reflection signals via the sparse array, and then discerns angle-of-arrival information for the output data by performing certain steps in an iterative manner for implementation of a sparse learning method which includes pruning, for each iterative update, certain of the plurality of spectrum-related support vectors having respective amplitudes which are insignificant relative to the statistical expectation of the support vector in a preceding iteration).”
It would have been obvious to one of ordinary skill-in-the-art before the effective filing date of the claimed invention to modify the radar system of Chen et al. (‘798) with the teaching of Wu et al. (‘883) for accurate angle of arrival measurement (Wu et al. (‘883) – paragraph 6). In addition, both of the prior art references, (Chen et al. (‘798) and Wu et al. (‘883)) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, using radar devices and systems in which objects are detected by sensing and processing reflections or radar signals for discerning angle-of-arrival information
Regarding claim 14, which is dependent on independent claim 9, and which has the same limitation as claim 7, Chen et al. (‘798)/Wu et al. (‘883) discloses all the claimed invention, as shown above for claim 7.
Regarding claim 15, which is dependent on independent claim 9, and which has the same limitation as claim 8, Chen et al. (‘798)/Wu et al. (‘883) discloses all the claimed invention, as shown above for claim 8.
Regarding independent claim 16, which is a corresponding method claim of independent system claim 9, Chen et al. (‘798)/Wu et al. (‘883) discloses all the claimed invention as shown above for claim 9.
Allowable Subject Matter
Claim 3 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the corresponding 112(b) and 101 rejections are overcome.
Allowable Subject Matter:
“to execute the regression algorithm, the controller is configured to: execute an insertion test to determine a second set of selected supports by adding a third support into the optimized set of selected supports, and determine that a third value of an optimization problem calculated using the second set of selected supports is less than the second value of the optimization problem calculated using the set of selected supports.”
Claims 4-6 depends on allowable claim 3, and therefore are objected to be allowable.
Claim 10 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the corresponding 112(b) and 101 rejections are overcome.
Allowable Subject Matter:
“to execute the regression algorithm, the controller is configured to: execute an insertion test to determine a second set of selected supports by adding a third support from the second subset into the optimized set of selected supports, and determine that a third value of the optimization problem calculated using the second set of selected supports is less than the second value of the optimization problem calculated using the set of selected supports.”
Claims 11-13 depends on allowable claim 11, and therefore are objected to be allowable.
Claim 17 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the corresponding 112(b) and 101 rejections are overcome.
Allowable Subject Matter:
“executing an insertion test to determine a second set of selected supports by adding a third support from the second subset into the optimized set of selected supports, and determining that a third value of the optimization problem calculated using the second set of selected supports is less than the second value of the optimization problem calculated using the set of selected supports.”
Claims 18-20 depends on allowable claim 17, and therefore are objected to be allowable.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Loesch et al. (US 11,500,061 B2) describes relates to a method for the phase calibration of two high-frequency components of a radar sensor, which includes an array of receiving antennas formed by two sub-arrays and an evaluation unit, which is designed to carry out an angle estimation for located radar targets based on phase differences between the signals received by the receiving antennas, each high-frequency component including parallel receiving paths for the signals of the receiving antennas of one of the sub-arrays (column 1 lines 5-13).
Zhang et al. (US 12,216,227 B2) describes the techniques and systems of a radar system with sequential 2D angle estimation…even with a 2D array of antenna elements, the described techniques enable an example radar system to estimate angles in two dimensions efficiently…a radar system includes a processor and an antenna to receive electromagnetic energy reflected by one or more objects…the antenna includes a 2D array that provides antenna elements positioned in the first and second dimensions. Using electromagnetic energy received by the 2D array, the processor can determine first angles in the first dimension associated with detecting one or more objects…the processor can then steer the 2D array to the first angle to generate a steered 1D array for each first angle…using the steered 1D array, the processor can determine second angles associated with the first angle for the detection…in this way, high angular resolution and estimation accuracy on first-dimension angles and second-dimension angles may be achieved without requiring expensive computer components, thereby enabling these techniques on a wide range of vehicles, e.g., from economy to luxury classes (column 1 lines 31-51); he angle-estimation module 116 can project the 2D array 204 onto a subspace formed by a first-dimension steering vector associated with the first angle 306-k, θk. The k-th projected 2D array is associated with the first angle 306-k, θk, and any second-dimension information obtained from the projected 2D array is associated with the first angle 306-k, θk (column 8 lines 24-30).
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/NUZHAT PERVIN/Primary Examiner, Art Unit 3648