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 .
Examiner’s Note
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 use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments. Merck & Co. v.Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) See MPEP 2123.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claim(s) are directed to a system and a method and recite(s) judicial exceptions as explained in the Step 2A, Prong 1 analysis below. The judicial exceptions are not integrated into a practical application as explained in the Step 2A, Prong 2 analysis below. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception as explained in the Step 2B analysis below.
Independent claim(s) 1:
Claim 1:
A computer implemented method for determining a beam vector list for object detection, the method comprising the following steps carried out by computer hardware components: receiving radar data from at least one radar sensor; determining range information and velocity information based on the radar data; and determining at least one beam vector list based on the range information and the velocity information using a neural network.
Step
Analysis
1: Statutory Category?
Yes. Claim 1 recites a series of steps and therefore, is a process. As such, the claim is directed to one of the four categories of patent eligible subject matter, and is eligible for further analysis.
2A - Prong 1: Judicial Exception Recited (i.e., mathematical concepts, certain methods of organizing human activities such as a fundamental economic practice, or mental processes)?
Yes. Claim 1 recites “A computer implemented method for determining a beam vector list for object detection, the method comprising the following steps carried out by computer hardware components: receiving radar data from at least one radar sensor; determining range information and velocity information based on the radar data; and determining at least one beam vector list based on the range information and the velocity information using a neural network”.
The focus of the claim (i.e., “determining range information and velocity information based on the radar data; and determining at least one beam vector list based on the range information and the velocity information using a neural network”) is on selecting certain information and analyzing it. These observations or evaluations are simply mathematical concepts (e.g., algorithms, spatial relationships, geometry). When given its broadest reasonable interpretation in light of the disclosure, “determining range information and velocity information based on the radar data; and determining at least one beam vector list based on the range information and the velocity information using a neural network” are simply selection and mathematical manipulation of data. Merely selecting information for collection and analysis does nothing significant to differentiate a process from an abstract idea.
Thus, the claim recites an abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim does not recite any additional elements that would integrate the judicial exception into a practical application.
The additional limitation(s) of “receiving radar data from at least one radar sensor” is considered insignificant extra-solution activities to the judicial exception. Furthermore, the limitation(s) of “at least one radar sensor” and “a neural network” are recited at a high level of generality. The additional limitation(s) merely are used to perform the abstract idea, and are merely invoked as tools of performing generic functions. The limitation(s) represent no more than mere instructions to apply the judicial exception on generic devices, and can be viewed as nothing more than an attempt to link the use of the judicial exception to the technological environment. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 224-26 (2014). The additional limitation(s) represent no more than mere attempt to recite a field in which the device is intended to be applied.
Accordingly, the claim as a whole does not integrate the recited judicial exception into a practical application.
2B: Claim provides an Inventive Concept?
No.
Step 2 considers whether the claim provides limitations which amount to “significantly more” than the recited judicial exception. The claim as a whole does not provide any meaningful limitations which amount to significantly more than the mathematical concept of claim 1.
The limitation(s) of “receiving radar data from at least one radar sensor” and “a neural network” are recited in a manner that is well understood, generic and conventional. The additional recitation(s) do not impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation(s) are at a high level of generality and are just a nominal or tangential addition to the claim. The limitation(s) are at best the equivalent of merely adding the words “apply it” to the judicial exception. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more.
Therefore, the claim as a whole does not provide meaningful limitations which amount to significantly more than the mathematical concept of claim 1 and does not state an inventive concept. The limitation(s) are just a nominal or tangential addition to the claim. Looking at the elements as a combination does not add anything more than the elements analyzed individually.
Applicant’s disclosure does not provide evidence that the additional element(s) recited in claim 1 (i.e., the claim element(s) in addition to the abstract idea) is sufficient to amount to significantly more than the abstract idea itself. This issue is explained by the Federal Circuit, as follows:
It has been clear since Alice that a claimed invention’s use of the ineligible concept to which it is directed cannot supply the inventive concept that renders the invention “significantly more” than that ineligible concept. In Alice, the Supreme Court held that claims directed to a computer-implemented scheme for mitigating settlement risks claimed a patent-ineligible abstract idea. 134 S.Ct. at 2352, 2355—56. Some of the claims at issue covered computer systems configured to mitigate risks through various financial transactions. Id. After determining that those claims were directed to the abstract idea of intermediated settlement, the Court considered whether the recitation of a generic computer added “significantly more” to the claims. Id. at 2357. Critically, the Court did not consider whether it was well-understood, routine, and conventional to execute the claimed intermediated settlement method on a generic computer. Instead, the Court only assessed whether the claim limitations other than the invention’s use of the ineligible concept to which it was directed were well-understood, routine and conventional. Id. at 2359-60. BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1290 (2018) (emphases added).
Therefore, independent claim 1 is ineligible.
Claims 2-15:
Step
Analysis
1: Statutory Category?
Yes. Claims 2-15 recite a series of steps and therefore, fall under a process. As such, the claim(s) are directed to one of the four categories of patent eligible subject matter, and are eligible for further analysis. Claim(s) 3-15 will not be evaluated separately because the claim(s) contain the same or sufficiently similar defects as those noted for claim 2 below.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim is directed to the method of claim 1 which recites a mathematical concept (see analysis above). Merely selecting information for collection and analysis does nothing significant to differentiate a process from the abstract idea.
2A - Prong 2: Integrated into a Practical Application?
No. The claim is considered an insignificant extra-solution activity to the judicial exception. The additional limitation(s) merely are used to perform the abstract idea. The claimed limitations are recited at a high level of generality, and are merely invoked as tools of performing generic functions.
2B: Claim provides an Inventive Concept?
No. The claim fails to impose a meaningful limit on the judicial exception other than what would be considered well understood, routine and conventional. The limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. The type of information being manipulated does not impose meaningful limitations or render the idea less abstract.
Therefore, dependent claim(s) 2-15 are ineligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 7, 9, and 13-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Roger et al. (US 2019/0041494 A1 cited in Applicant’s IDS “ROGER”).
Regarding claim 1, ROGER discloses a computer implemented method for determining a beam vector list for object detection, the method comprising the following steps carried out by computer hardware components: receiving radar data from at least one radar sensor (one or more radar ECUs (radar sensors) [0057]); determining range information and velocity information based on the radar data (performing the computation of the radar data cubes (i.e. FFT computation for generating the Range-Doppler Maps) as well as radar target detection, classification, tracking, etc. [0058]); (when the radar target is moving, the Doppler effect has to be considered to determine the information of the radar target's velocity (relative to the radar sensor), which can be done based on the mentioned Range-Doppler map [0037]); and determining at least one beam vector list based on the range information and the velocity information using a neural network (these Range Doppler Maps X′(n, m) are supplied to functional unit 74, which includes one or more artificial neural networks that are used to detect radar targets based on the restored Range Doppler Maps X′(n, m). The output of functional block 74 is a list of detected radar targets T(i) (i.e. their position and velocity) [0052]).
Regarding claim 2, ROGER discloses the method of claim 1, further comprising the following steps carried out by computer hardware components: determining a signal strength of the radar data based on the range information and the velocity information (a peak in the Range Map [0043]); transmitting the signal strength as input to the neural network; and determining the at least one beam vector list based on the range information, the velocity information and the signal strength of the radar data using the neural network (the data compression is achieved by identifying peaks (local maxima) and discarding values outside the main lobes of the detected peaks to zero [0049]); (in this central radar post processing unit 8, the compressed signals received from the radar ECU(s) 1, 2, 3, etc. are decompressed (i.e. restored), which may be dome by zero padding the “gaps” between the main lobes of the detected peaks (step X4). Finally, the decompressed signal(s) (restored Range Doppler Maps X′(n, m)) are further processed to detect radar targets (step X5) [0050]).
Regarding claim 3, ROGER discloses the method of claim 1, further comprising the following steps carried out by computer hardware components: determining at least one angle based on the range information and the velocity information (once the FFT peaks are detected/selected in a radar sensor, the local software algorithm executed in the radar sensor may decide whether to add spectral values (spectral lines, frequency bins) in the Range and/or in the Doppler dimension(s), which are adjacent to the spectral line of the detected peak. Optionally, to further compress the sensor data, the sensor could compute the azimuth angle and elevation angle (that is the DoA detection) [0045]); transmitting the at least one angle as input to the neural network; and determining the at least one beam vector list based on the range information, the velocity information and the at least one angle using the neural network (in this central radar post processing unit 8, the compressed signals received from the radar ECU(s) 1, 2, 3, etc. are decompressed (i.e. restored), which may be dome by zero padding the “gaps” between the main lobes of the detected peaks (step X4). Finally, the decompressed signal(s) (restored Range Doppler Maps X′(n, m)) are further processed to detect radar targets (step X5) [0050]).
Regarding claim 4, ROGER discloses the method of claim 3, wherein the at least one angle is determined using a Discrete Fourier Transformation (DFT) method (the common method for calculating the Range-Doppler maps is a two-dimensional Fourier Transform, which is usually implemented using a Fast Fourier Transform (FFT) algorithm [0038]); (once the FFT peaks are detected/selected in a radar sensor, the local software algorithm executed in the radar sensor may decide whether to add spectral values (spectral lines, frequency bins) in the Range and/or in the Doppler dimension(s), which are adjacent to the spectral line of the detected peak. Optionally, to further compress the sensor data, the sensor could compute the azimuth angle and elevation angle (that is the DoA detection) [0045]).
Regarding claim 5, ROGER discloses the method of claim 1, further comprising the following steps carried out by computer hardware components: determining at least one numerical value based on the range information and the velocity information (a peak in the Range Map [0043]); transmitting the at least one numerical value as input to the neural network; and determining the at least one beam vector list based on the range information, the velocity information and the at least one numerical value using the neural network (the data compression is achieved by identifying peaks (local maxima) and discarding values outside the main lobes of the detected peaks to zero [0049]); (in this central radar post processing unit 8, the compressed signals received from the radar ECU(s) 1, 2, 3, etc. are decompressed (i.e. restored), which may be dome by zero padding the “gaps” between the main lobes of the detected peaks (step X4). Finally, the decompressed signal(s) (restored Range Doppler Maps X′(n, m)) are further processed to detect radar targets (step X5) [0050]).
Regarding claim 7, ROGER discloses the method of claim 1, wherein a beam vector corresponding to an entry of the at least one beam vector list comprises an azimuth angle and an elevation angle (the sensor could compute the azimuth angle and elevation angle (that is the DoA detection) [0045]).
Regarding claim 9, ROGER discloses the method of claim 1, wherein the at least one beam vector list corresponds to a number of cells in a range-Doppler map (these Range Doppler Maps X′(n, m) are supplied to functional unit 74, which includes one or more artificial neural networks that are used to detect radar targets based on the restored Range Doppler Maps X′(n, m). The output of functional block 74 is a list of detected radar targets T(i) (i.e. their position and velocity) [0052], cited and incorporated in the rejection of claim 1); (a list of radar targets herein denoted as T(i), wherein i is the index of the detected radar target [0040]).
Regarding claim 13, ROGER discloses a computer system comprising a plurality of computer hardware components configured to carry out steps of the computer implemented method of claim 1 (CPU 72 [0052]).
Regarding claim 14, ROGER discloses a vehicle, comprising the computer system of claim 13 and the at least one radar sensor (radar sensors in a vehicle; radar sensors 1-7 (radar ECUs) are distributed over a vehicle [0042]).
Regarding claim 15, ROGER discloses a non-transitory computer readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the computer implemented method of claim 1 (CPU 72 [0052]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over ROGER, in view of Fetterman et al. (US 2017/0059695 A1 “FETTERMAN”).
Regarding claim 6, ROGER discloses (Examiner’s note: What ROGER does not disclose is ) the method of claim 5,
In a same or similar field of endeavor, FETTERMAN teaches averaging of multiple range-Doppler maps for the region being monitored [0044].
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 system of ROGER to include the teachings of FETTERMAN, because doing so would result in improved signal-to-noise ratio (SNR), as recognized by FETTERMAN. In addition, both of the prior art references, ROGER and FETTERMAN, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, radar processing for target detection.
Claim(s) 8, and 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over ROGER, in view of Nunn et al. (US 2019/0325241 A1 “NUNN”).
Regarding claim 8, ROGER discloses the method of claim 1,
In a same or similar field of endeavor, NUNN teaches that it can be that the convolution result is added to a constant and that an activation function is applied, which can be a function configured to perform a transformation to a predefined scale. Examples for activation functions are the sigmoid function and the tanh function [0040].
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 system of ROGER to include the teachings of NUNN, because doing so would be powerful in robustly extracting reliable dynamic information and easily be integrated into many applications such as an autonomous driving application, as recognized by NUNN. In addition, both of the prior art references, ROGER and NUNN, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, extraction of radar data using a neural network.
Regarding claim 11, ROGER discloses the method of claim 1,
In a same or similar field of endeavor, NUNN teaches to use one single, i.e. global CNN for extracting the dynamic information [0011]. The modules can be employed in a row, i.e. in a “pipe-line” structure. Although each of the modules is designed to carry out a specific processing task, the overall CNN can be trained in an end-to-end manner, which simplifies preparation of the network and any necessary adaptions [0012].
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 system of ROGER to include the teachings of NUNN, because doing so would be powerful in robustly extracting reliable dynamic information and easily be integrated into many applications such as an autonomous driving application, as recognized by NUNN.
Regarding claim 12, ROGER discloses the method of claim 1,
In a same or similar field of endeavor, NUNN teaches that the CNN 90 has a total of five modules, each of the modules formed by a sub-network of the CNN 90 [0068]. The modules can be employed in a row, i.e. in a “pipe-line” structure [0012].
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 system of ROGER to include the teachings of NUNN, because doing so would enable a holistic approach, wherein the advantage of dividing a complex processing up into multiple units is maintained, while the disadvantage of having to deal with multiple separated networks is removed, as recognized by NUNN.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over ROGER, in view of Schubert et al. (US 2021/0116541 A1 “SCHUBERT”).
Regarding claim 10, ROGER discloses the method of claim 9,
In a same or similar field of endeavor, SCHUBERT teaches that a range-Doppler matrix M may be generated by the radar system. A partial quantity of the cells of range-Doppler matrix M is selected in a selection device 11. To that end, for example, a predetermined number of cells of range-Doppler matrix M may be selected randomly. For instance, a maximum of 1% or even less, e.g., 5 per thousand, 2 per thousand, 1 per thousand or possibly even fewer of the cells of range-Doppler matrix M may be selected [0041].
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 system of ROGER to include the teachings of SCHUBERT, because doing so would simplify the determination of the detection and efficiently improve ascertainment of the detection, as recognized by SCHUBERT. In addition, both of the prior art references, ROGER and SCHUBERT, teach features that are directed to analogous art and they are directed to the same field of endeavor, that is, processing of a range-Doppler map in a radar system.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Evans et al. (US 2022/0404490 A1) is considered pertinent art for the disclosure of a method, apparatus and computer program product to generate a model of one or more objects relative to a vehicle. In the context of a method, radar information is received in the form of in-phase quadrature (IQ) data and the IQ data is converted to one or more first range-doppler maps. The method further includes evaluating the one or more first range-doppler maps with a machine learning model to generate the model that captures the detection of the one or more objects relative to the vehicle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAILEY R LE whose telephone number is (571)272-4910. The examiner can normally be reached 9:00 AM - 5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, WILLIAM J KELLEHER can be reached at (571) 272-7753. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Hailey R Le/Examiner, Art Unit 3648 March 24, 2026