Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 5, 6, 8, 9, 10, 19, 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.
For claims 5, 6, 9, 10, and 19 the variables of the equations are not defined with the equations, both in the claims and in the specification. Additionally, the specification prefaces the variable definitions with ‘in other features’. It is unclear if the variables defined in claim 8 or the specification refer to the equations 5, 6, 9, 10, 19, or 20 as the definitions do not refer to the equations. As shown in claim 8, some variables have multiple meanings, as with the variable Y and K, which adds to the ambiguity when the definitions are not attached to an equation. For the sake of evaluation, the Examiner is assuming that there is no double definition of variables for the equations and that the set of array observations Y is being applied to all equations except for the first two equations in claims 8 and 20. Similarly claims 8 and 20 are ambiguous in that there are two different K values and it is unclear which K value is used in the A, B, and S matrices as the variable is not directed to an equation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 4, 16, 17, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zheng April 2024 [L. Zheng, J. Long, M. Lops, F. Liu, X. Hu, and C. Zhao, "Detection of Ghost Targets for Automotive Radar in the Presence of Multipath," in IEEE Transactions on Signal Processing, vol. 72, pp. 2204–2220, 2024 [5.8], doi: 10.1109/TSP.2024.3384750.] in view of Wang (CN 115335724 A).
Regarding claim 1 Zheng discloses
A method comprising: receiving a set of radar signals incident on a set of objects (Figure 1 elements (a) and (b)); determining a first hypothesis model that represents a monostatic signal model of the set of radar signals (Page 2 Column 2 Paragraph 1 line 7-13 , "After deriving the MIMO radar signal model, the problem of first-order paths existence is stated as a binary decision problem between a composite hypothesis, H0 say, that the observations only contain an unknown number of direct paths sharing the same (unknown) DOD’s and DOA’s, and a composite alternative, H1 say, that the observations also contain an unknown number of indirect paths" where a monostatic model is interpreted as a direct path and bistatic is interpreted as multipath as per paragraph 0002 of the specification from the instant application ); determining a second hypothesis model that represents a bistatic signal model of the set of radar signals (Page 2 Column 2 Paragraph 1 line 7-13, "After deriving the MIMO radar signal model, the problem of first-order paths existence is stated as a binary decision problem between a composite hypothesis, H0 say, that the observations only contain an unknown number of direct paths sharing the same (unknown) DOD’s and DOA’s, and a composite alternative, H1 say, that the observations also contain an unknown number of indirect paths" where a monostatic model is interpreted as a direct path and bistatic is interpreted as multipath as per paragraph 0002 of the specification from the instant application ); selecting, from a set of generalized likelihood ratio test (GLRT) detectors, a selected GLRT detector based on a set of signal criteria (Abstract, "We exploit the Generalized Likelihood Ratio Test (GLRT) philosophy to determine the detector structure"; Page 10 Column 1 Paragraph 1, "For the proposed detection scheme, CSCD-H0 is adopted under H0, and CSCD-H1 is adopted under H1, so the detector is named GLRT-CSCD for simplicity. Likewise, we have GLRT-OMP algorithms for the detectors with OMP-based estimators. We include the IAA Based and the least absolute shrinkage and selection operator (LASSO)-based methods in the GLRT test, denoting them as GLRT-IAA and GLRT-LASSO, respectively" where a hypothesis H is a signal criteria for a GLRT, and the detector structure determines the detector); determining whether the ratio is greater than a threshold (Equation 13 where lambda is the threshold and is applicable to the GLRT test); Based on the selected GLRT detector, determining a ratio between a maximized likelihood function of the second hypothesis model and a maximized likelihood function of the first hypothesis model (Abstract, "We exploit the Generalized Likelihood Ratio Test (GLRT) philosophy to determine the detector structure"; Page 10 Column 1 Paragraph 1, "For the proposed detection scheme, CSCD-H0 is adopted under H0, and CSCD-H1 is adopted under H1, so the detector is named GLRT-CSCD for simplicity" where the GLRT test always uses the maximum likelihood and always chooses between at least two different hypotheses); and in response to a determination that the set of angles is not available: selecting an angle estimation method based on the ratio (Page 2 Column 1 Paragraph 3, "The coexistence of these paths often results in significant mutual interference, while discrepancies in DOD and DOA for indirect paths further complicate estimations, posing a challenge to achieving high accuracy angle measurements" where multiple paths create an ambiguity in angle which require the GLRT statistics; Page 10 Column 1 Paragraph 1, "For the proposed detection scheme, CSCD-H0 is adopted under H0, and CSCD H1 is adopted under H1" where you use the GLRT test when you don't know what the angles are and the ratio determines if H0 or H1 is true); estimating the set of angles using the selected angle estimation method (Page 7 Column 2 Paragraph 5, "Under H1, the algorithm we propose is an extension of the previous one, on the understanding that now the angles of both direct and first-order paths must be estimated" where it would pick the CSCD H1 algorithm based on if H1 was true; Page 11 Column 1 Paragraph 2, "In this subsection, we verify the estimation performance of the proposed CSCD-H0 and CSCD-H1 algorithms"); and tracking the set of objects based on the set of angles (Figure 11 elements (a) and (d) where tracking the position of a moving target involves tracking; the presence and removal of ghost targets from graph (a) to graph (d) shows the use of the multipath angle information to remove the ghost targets; and the removal of ghost targets to know the true target is a form of tracking).
Zheng does not disclose based on the selected GLRT detector, determining a ratio between a maximized likelihood function of the second hypothesis model and a maximized likelihood function of the first hypothesis model; determining whether a set of angles associated with the set of radar signals is available; in response to a determination that the set of angles is available, updating a set of data associated with a set of objects.
Zheng discloses the use of a GLRT statistic to determine the signal path and associated angle of an object detection. The GLRT is needed to clarify the ambiguity in the angle determination and if an object is a ghost object or not. Therefore, it is obvious that Zheng is using the GLRT statistic because the angle cannot be adequately determined.
Wang discloses
Determining whether a set of angles associated with the set of radar signals is available ( Page 4 Paragraph 5, "In order to determine the angle of the object (i.e., the azimuth angle/elevation angle of the object), the radar system should consider all the echoes of the reference signal together"); in response to a determination that the set of angles is available, updating a set of data associated with a set of objects (Page 24 Paragraph 6, "The MIMO radar system of some embodiments is configured to detect a moving object such as another vehicle or a pedestrian, and determine various parameters of the moving object. Examples of parameters include one or combination of the following items: the distance of the moving object (e.g., distance from the radar to the moving object), the speed of the moving object (e.g., the absolute speed or relative speed between the radar and the moving object), the moving object defines the angle of the direction from the radar to the moving object" where an object defining an angle and that object moving is tantamount to the radar updating the angle information as it follows the target and continues to know its angle).
Zheng discloses using a GLRT test in the event of angle ambiguity, but it does not disclose determining and updating the angle information of the radar data. A MIMO device, as with Zheng, can determine the angle of a target through phase shift information. It would be obvious for the MIMO device to check for angle information in the radar data is it would determine if the GLRT test is needed. If the angle can be determined the GLRT would not be needed and phase shift determination of the angle would be computationally easier than a GLRT test. Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Zheng with Wang to incorporate determining and updating angle information from radar data to be more computationally efficient in determining the angle of a target.
Regarding claim 2 the combination of Zheng and Wang discloses
The method of claim 1. Zheng further discloses further comprising: in response to a determination that the ratio is greater than a threshold, determining that the second hypothesis model is accurate (Equation 13 where H1 is the second hypothesis); and in response to a determination that the ratio is less than or equal to the threshold, determining that the first hypothesis model is accurate (Equation 13 where H0 is the first hypothesis).
Regarding claim 4 the combination of Zheng and Wang discloses
The method of claim 1. Zheng further discloses wherein selecting the angle estimation method includes: in response to a determination that the ratio is greater than the threshold, selecting a first angle estimation method (Page 7 Column 2 Paragraph 3, "The detailed procedure is given in Algorithm 1 and we name the proposed method as Compressed Sensing method in Continuous Domain under hypothesis H0 (CSCD-H0) algorithm"; Page 11 Column 1 Paragraph 2, "In this subsection, we verify the estimation performance of the proposed CSCD-H0 and CSCD-H1 algorithms" where the H0 algorithm is based on H0 being true which is calculated through the ratio comparison with the threshold, and where the labels of first and second can be flipped or changed); and in response to a determination that the ratio is less than the threshold, selecting a second angle estimation method (Page 7 Column 2 Paragraph 5, "Under H1, the algorithm we propose is an extension of the previous one, on the understanding that now the angles of both direct and first-order paths must be estimated"; Page 11 Column 1 Paragraph 2, "In this subsection, we verify the estimation performance of the proposed CSCD-H0 and CSCD-H1 algorithms" where the H1 algorithm is based on H1 being true which is calculated through the ratio comparison with the threshold, and where the labels of first and second can be flipped or changed).
Regarding claim 11 the combination of Zheng and Wang discloses
The method of claim 1. Zheng further discloses wherein the first hypothesis model (Equation 9) is based on: a quantity of elements in a radar array that receives the set of radar signals (Equation 9 where A(Θ0) contains the steering matrix of the TX, RX array and the number of elements is the same as the number of elements that would be receiving), a first quantity of reflection paths (Equation 9 where Θ and Φ contain the angles of the direct paths of K0 and multipaths of K1 ), a first quantity of observations, the set of array observations (Equation 9 where z is based on X in equation 5 which are observations), the first spatial matrix of reflection paths (Equation 9 where A(Θ0) is a response matrix and it is paired with α or β, for the amplitudes of the direct paths and multipaths), the set of transmitted signals (Equation 9 and equation 4 where the received data matrix Y is in the matrix Z that gets vectorized into equation 9), and the set of noise data (Equation 9 where r contains the noise parameter W).
Regarding claim 12 the combination of Zheng and Wang discloses
The method of claim 11. Zheng further discloses wherein the second hypothesis model is based on: the quantity of elements in a radar array that receives the set of radar signals (Equation 9 where A(Θ) contains the steering matrix of the TX, RX array and the number of elements is the same as the number of elements that would be receiving), a second quantity of reflection paths (Equation 9 where Θ and Φ contain the angles of the direct paths of K0 and multipaths of K1 ), a second quantity of observations, the set of array observations (Equation 9 where z is based on X in equation 5 which are observations and there are multiple observations), the second spatial matrix of reflection paths (Equation 9 where A(Θ, Φ) is a response matrix and it is paired with α or β, for the amplitudes of the direct paths and multipaths), the set of transmitted signals (Equation 9 and equation 4 where the received data matrix Y is in the matrix Z that gets vectorized into equation 9), and the set of noise data (Equation 9 where r contains the noise parameter W).
Regarding claim 14 the combination of Zheng and Wang discloses
The method of claim 1. Zheng further discloses wherein the set of angles includes a direction of departure and a direction of arrival (Equation 9 where A(Θ, Φ) has angles for the DOD and DOA; Page 4 Column 1 Paragraph 5, “DOD angle vector Θ1 = [ϑ1, ϑ2, . . . , ϑK1 ]T ∈ RK1×1, the
DOA angle vector Φ1 = [ϕ1, ϕ2, . . . , ϕK1 ]T ∈ RK1×1”).
Regarding claim 16 Zheng discloses
A system comprising: receiving a set of radar signals incident on a set of objects (Figure 1 elements (a) and (b)); determining a first hypothesis model that represents a monostatic signal model of the set of radar signals (Page 2 Column 2 Paragraph 1 line 7-13 , "After deriving the MIMO radar signal model, the problem of first-order paths existence is stated as a binary decision problem between a composite hypothesis, H0 say, that the observations only contain an unknown number of direct paths sharing the same (unknown) DOD’s and DOA’s, and a composite alternative, H1 say, that the observations also contain an unknown number of indirect paths" where a monostatic model is interpreted as a direct path and bistatic is interpreted as multipath as per paragraph 0002 of the specification from the instant application ); determining a second hypothesis model that represents a bistatic signal model of the set of radar signals (Page 2 Column 2 Paragraph 1 line 7-13, "After deriving the MIMO radar signal model, the problem of first-order paths existence is stated as a binary decision problem between a composite hypothesis, H0 say, that the observations only contain an unknown number of direct paths sharing the same (unknown) DOD’s and DOA’s, and a composite alternative, H1 say, that the observations also contain an unknown number of indirect paths" where a monostatic model is interpreted as a direct path and bistatic is interpreted as multipath as per paragraph 0002 of the specification from the instant application ); selecting, from a set of generalized likelihood ratio test (GLRT) detectors, a selected GLRT detector based on a set of signal criteria (Abstract, "We exploit the Generalized Likelihood Ratio Test (GLRT) philosophy to determine the detector structure"; Page 10 Column 1 Paragraph 1, "For the proposed detection scheme, CSCD-H0 is adopted under H0, and CSCD-H1 is adopted under H1, so the detector is named GLRT-CSCD for simplicity. Likewise, we have GLRT-OMP algorithms for the detectors with OMP-based estimators. We include the IAA Based and the least absolute shrinkage and selection operator (LASSO)-based methods in the GLRT test, denoting them as GLRT-IAA and GLRT-LASSO, respectively" where a hypothesis H is a signal criteria for a GLRT, and the detector structure determines the detector); based on the selected GLRT detector, determining a ratio between a maximized likelihood function of the second hypothesis model and a maximized likelihood function of the first hypothesis model (Abstract, "We exploit the Generalized Likelihood Ratio Test (GLRT) philosophy to determine the detector structure"; Page 10 Column 1 Paragraph 1, "For the proposed detection scheme, CSCD-H0 is adopted under H0, and CSCD-H1 is adopted under H1, so the detector is named GLRT-CSCD for simplicity" where the GLRT test always uses the maximum likelihood and always chooses between at least two different hypotheses); determining whether the ratio is greater than a threshold (Equation 13 where lambda is the threshold and is applicable to the GLRT test); and in response to a determination that the set of angles is not available: selecting an angle estimation method based on the ratio (Page 2 Column 1 Paragraph 3, "The coexistence of these paths often results in significant mutual interference, while discrepancies in DOD and DOA for indirect paths further complicate estimations, posing a challenge to achieving high accuracy angle measurements" where multiple paths create an ambiguity in angle which require the GLRT statistics; Page 10 Column 1 Paragraph 1, "For the proposed detection scheme, CSCD-H0 is adopted under H0, and CSCD H1 is adopted under H1" where you use the GLRT test when you don't know what the angles are and the ratio determines if H0 or H1 is true); estimating the set of angles using the selected angle estimation method (Page 7 Column 2 Paragraph 5, "Under H1, the algorithm we propose is an extension of the previous one, on the understanding that now the angles of both direct and first-order paths must be estimated" where it would pick the CSCD H1 algorithm based on if H1 was true; Page 11 Column 1 Paragraph 2, "In this subsection, we verify the estimation performance of the proposed CSCD-H0 and CSCD-H1 algorithms"); and tracking the set of objects based on the set of angles (Figure 11 elements (a) and (d) where tracking the position of a moving target involves tracking; the presence and removal of ghost targets from graph (a) to graph (d) shows the use of the multipath angle information to remove the ghost targets; and the removal of ghost targets to know the true target is a form of tracking).
Zheng does not disclose memory hardware configured to store instructions; processor hardware configured to execute the instructions, wherein the instructions include; determining whether a set of angles associated with the set of radar signals is available; in response to a determination that the set of angles is available, updating a set of data associated with a set of objects.
Zheng discloses the use of a GLRT statistic to determine the signal path and associated angle of an object detection. The GLRT is needed to clarify the ambiguity in the angle determination and if an object is a ghost object or not. Therefore, it is obvious that Zheng is using the GLRT statistic because the angle cannot be adequately determined.
Wang discloses
Memory hardware configured to store instructions (Page 23 Paragraph5, “In addition, the system 100 includes a processor 417 configured to execute the stored instructions 419, and a memory 421 for storing instructions executable by the processor 417”); processor hardware configured to execute the instructions, wherein the instructions include (Page 23 Paragraph5, “In addition, the system 100 includes a processor 417 configured to execute the stored instructions 419, and a memory 421 for storing instructions executable by the processor 417”); determining whether a set of angles associated with the set of radar signals is available (Page 4 Paragraph 5, "In order to determine the angle of the object (i.e., the azimuth angle/elevation angle of the object), the radar system should consider all the echoes of the reference signal together"); in response to a determination that the set of angles is available, updating a set of data associated with a set of objects (Page 24 Paragraph 6, "The MIMO radar system of some embodiments is configured to detect a moving object such as another vehicle or a pedestrian, and determine various parameters of the moving object. Examples of parameters include one or combination of the following items: the distance of the moving object (e.g., distance from the radar to the moving object), the speed of the moving object (e.g., the absolute speed or relative speed between the radar and the moving object), the moving object defines the angle of the direction from the radar to the moving object" where an object defining an angle and that object moving is tantamount to the radar updating the angle information as it follows the target and continues to know its angle).
Zheng discloses using a GLRT test in the event of angle ambiguity, but it does not disclose determining and updating the angle information of the radar data. A MIMO device, as with Zheng, can determine the angle of a target through phase shift information. It would be obvious for the MIMO device to check for angle information in the radar data is it would determine if the GLRT test is needed. If the angle can be determined the GLRT would not be needed and phase shift determination of the angle would be computationally easier than a GLRT test. Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Zheng with Wang to incorporate determining and updating angle information from radar data to be more computationally efficient in determining the angle of a target.
Regarding claim 17 the combination of Zheng and Wang discloses
The system of claim 16. Zheng further discloses wherein the instructions include: in response to a determination that the ratio is greater than a threshold, determining that the second hypothesis model is accurate (Equation 13 where H1 is the second hypothesis); and in response to a determination that the ratio is less than or equal to the threshold, determining that the first hypothesis model is accurate (Equation 13 where H0 is the first hypothesis).
Regarding claim 18 the combination of Zheng and Wang discloses
The system of claim 16. Zheng further discloses wherein selecting the angle estimation method includes: in response to a determination that the ratio is greater than the threshold, selecting a first angle estimation method (Page 7 Column 2 Paragraph 3, "The detailed procedure is given in Algorithm 1 and we name the proposed method as Compressed Sensing method in Continuous Domain under hypothesis H0 (CSCD-H0) algorithm"; Page 11 Column 1 Paragraph 2, "In this subsection, we verify the estimation performance of the proposed CSCD-H0 and CSCD-H1 algorithms" where the H0 algorithm is based on H0 being true which is calculated through the ratio comparison with the threshold, and where the labels of first and second can be flipped or changed); and in response to a determination that the ratio is less than the threshold, selecting a second angle estimation method (Page 7 Column 2 Paragraph 5, "Under H1, the algorithm we propose is an extension of the previous one, on the understanding that now the angles of both direct and first-order paths must be estimated"; Page 11 Column 1 Paragraph 2, "In this subsection, we verify the estimation performance of the proposed CSCD-H0 and CSCD-H1 algorithms" where the H1 algorithm is based on H1 being true which is calculated through the ratio comparison with the threshold, and where the labels of first and second can be flipped or changed).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zheng April 2024 [L. Zheng, J. Long, M. Lops, F. Liu, X. Hu, and C. Zhao, "Detection of Ghost Targets for Automotive Radar in the Presence of Multipath," in IEEE Transactions on Signal Processing, vol. 72, pp. 2204–2220, 2024 [5.8], doi: 10.1109/TSP.2024.3384750.] in view of Wang (CN 115335724 A) [Wang 724] further in view of Wang (US 20240077601 A1) [Wang 601].
Regarding claim 3 the combination of Zheng and Wang 724 discloses
The method of claim 1. The combination of Zheng and Wang 724 does not disclose further comprising autonomously controlling a vehicle to avoid the set of objects.
Wang 601 discloses
Further comprising autonomously controlling a vehicle to avoid the set of objects (Paragraph 0079, "In an example embodiment, an automobile may include the MIMO radar system 100. The automobile may be an autonomous driving vehicle. While the automobile is travelling on a highway or parking in a parking space, the MIMO radar system 100 detects objects (such as vehicles, pedestrians, or the likes). Based on the detected objects, the autonomous vehicle may control its navigation").
Zheng discloses a GLRT system for a vehicle but does not specify that the vehicle is autonomous. An autonomous vehicle would be advantageous in that it can improve safety during the operation of a vehicle if the sensor system can detect an obstacle that the driver misses. Additionally, an autonomous car can control the vehicle in the event of an emergency, such as the driver having a heart attack. As such, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Zheng with Wang 601 to modify the vehicle to be autonomous to improve the safety features of the vehicle and/or operation of the vehicle.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zheng April 2024 [L. Zheng, J. Long, M. Lops, F. Liu, X. Hu, and C. Zhao, "Detection of Ghost Targets for Automotive Radar in the Presence of Multipath," in IEEE Transactions on Signal Processing, vol. 72, pp. 2204–2220, 2024 [5.8], doi: 10.1109/TSP.2024.3384750.] in view of Wang (CN 115335724 A) [Wang 724] further in view of Huang [B. Huang, W. Wang, W. Liu, S. Zhang, Y. Jia "Robust moving target detection for FDA-MIMO radar with steering vector mismatches," Digit. Signal Processing, vol. 145, February 2024.].
Regarding claim 7 the combination of Zheng and Wang discloses
The method of claim 1. Zheng discloses the selected angle estimation method is applicable to direct-path reflections and multi-path reflections (Page 7 Column 2 Paragraph 3, "The detailed procedure is given in Algorithm 1 and we name the proposed method as Compressed Sensing method in Continuous Domain under hypothesis H0 (CSCD-H0) algorithm"; Page 11 Column 1 Paragraph 2, "In this subsection, we verify the estimation performance of the proposed CSCD-H0 and CSCD-H1 algorithms"; Page 7 Column 2 Paragraph 5, "Under H1, the algorithm we propose is an extension of the previous one, on the understanding that now the angles of both direct and first-order paths must be estimated"; Page 11 Column 1 Paragraph 2, "In this subsection, we verify the estimation performance of the proposed CSCD-H0 and CSCD-H1 algorithms" where H0 algorithm is for direct path and H1 algorithm is for multipath). Zheng does not explicitly disclose the selected GLRT detector is the third GLRT detector or the fourth GLRT detector.
Huang discloses
The selected GLRT detector is the third GLRT detector or the fourth GLRT detector (Abstract, “At the detector design stage, we propose three robust adaptive detectors based on the one-step generalized likelihood ratio test (GLRT), two-step GLRT, and Wald test”).
Zheng discloses different detector structures (e.g. CSCD H0, CSCD H1, GLRT-IAA, GLRT OMP, and GLRT-LASSO) but does not disclose a numbered number of detectors as in ‘detector three’. It would be advantageous to have multiple detectors to have redundancy in the event of failure. Also, with multiple detectors multiple algorithms can be used at the same time which may have different advantages depending on the driving situation. Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Zheng with Huang to add in a third detector to have redundancy in the event of failure.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zheng April 2024 [L. Zheng, J. Long, M. Lops, F. Liu, X. Hu, and C. Zhao, "Detection of Ghost Targets for Automotive Radar in the Presence of Multipath," in IEEE Transactions on Signal Processing, vol. 72, pp. 2204–2220, 2024 [5.8], doi: 10.1109/TSP.2024.3384750.] in view of Wang (CN 115335724 A) [Wang 724] further in view of Saboo (US 20160252607 A1).
Regarding claim 13 the combination of Zheng and Wang discloses
The method of claim 1. Zheng discloses and a second criterion that is met when a set of angles associated with a spatial matrix of reflection paths is known (algorithm 1 page 8 where if it is greater than or equal to T it knows the angles/paths of the direct path model). The combination of Zheng and Wang does not disclose wherein the set of signal criteria includes: a first criterion that is met when a power level associated with a set of noise data is known.
Saboo discloses
Wherein the set of signal criteria includes: a first criterion that is met when a power level associated with a set of noise data is known (Figure 9 left side conditions checked and surrounding noise level; Paragraph 0056, “the surrounding noise level to the left of the left peak should be less than 0.3 times the peak value”).
Zheng discloses a noise parameter but not a criterion of a certain power level of the noise. It would be advantageous to have a criterion for the noise in that if the power level is too high it may be pointless to conduct the calculation. As such, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Zheng with Saboo to incorporate a metric for the noise level to determine if the algorithm should continue.
Allowable Subject Matter
Claim 15 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.
The following is a statement of reasons for the indication of allowable subject matter: Claim 15 states the limitation ‘before determining whether the ratio is greater than the threshold, estimating the set of angles’ where the angles that the GLRT is intended to help determined are estimated beforehand. The closest pertinent art is Zheng which uses the GLRT statistic to estimate paths/angles but only estimates the angles after evaluating the threshold value of the GLRT.
Conclusion
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/PETER DAVON DOZE/Examiner, Art Unit 3648
/OLUMIDE AJIBADE AKONAI/Primary Examiner, Art Unit 3648
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