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 § 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 abstract ideas without significantly more.
Step 1: Claims 1-9 are directed to a system, which is a machine, falling under a statutory category of invention. Claims 10-11 are directed to a non-transitory computer-readable medium, which is a manufacture, falling under a statutory category of invention. Claims 12-20 are directed to a method, which is a process, falling under a statutory category of invention. Therefore, claims 1-20 are directed to patent eligible categories of invention.
Regarding claim 1:
Step 2A Prong 1: The following limitations under broadest reasonable interpretation recite abstract ideas:
As per MPEP § 2106.04(a)(2): “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018)” See MPEP § 2106.04(a)(2).
The limitation “compute positions for elements on an antenna array within a placement area using randomization that accounts for varying quantities of the elements according to a distance constraint and a side-lobe power” covers a mathematical concept. For example, computing position values involves mathematical calculations, equations/formulas, and/or relationships. Claim 2 also recites that computing positions involves using a Monte Carlo method. Such a method involves mathematical concepts including mathematical calculations, equations/formulas, and/or relationships. This also amounts to a mental process. For example, a person can mentally make evaluations and judgment on the positions of the elements.
The limitation “adjust the placement area according to a location associated with one of the elements” covers a mental process. For example, this covers a person mentally making a judgment on the appropriate placement area and mentally making changes to the layout model.
The limitation “in response to the elements satisfying criteria after predetermined iterations, optimize the positions for a physical layout of the antenna array using a gradient operation according to the side-lobe power” covers a mathematical concept. For example, a gradient operation involves mathematical concepts including mathematical calculations, equations/formulas, and/or relationships. This also amounts to a mental process. For example, this covers a person evaluating a gradient mentally or with a pen and paper.
Step 2A Prong 2: The following limitations recite additional elements:
The additional elements “a processor” and “a memory storing instructions that, when executed by the processor” do not integrate the judicial exception into a practical application because they amount to no more than mere instructions to apply the judicial exception using a generic computer. See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the judicial exception into a practical application.
Accordingly, the claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B: Furthermore, the additional elements do not amount to significantly more than the judicial exception.
As previously discussed, the additional elements amount to no more than mere instructions to apply the exception using a generic computer, which do not amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Accordingly, the claim does not recite any additional elements that amount to significantly more than the judicial exception.
Therefore, claim 1 is not eligible.
Regarding claim 2:
The limitation “wherein the instructions to compute the positions further include instructions to move, using a Monte Carlo method, the positions randomly until a number of phase shifters are active” amounts to a mathematical concept and a mental process as explained in claim 1 regarding the Monte Carlo method.
The limitation “wherein the Monte Carlo method is associated with a distribution size and the elements are grouped according to one of a shape and a size” merely further limits the Monte Carlo method and the elements recited previously. Therefore, this amounts to a mathematical concept and a mental process ad explained previously.
Regarding claim 3: The limitation “reduce, using the Monte Carlo method, a number of the elements and the number of phase shifters to steer a main beam at a main-lobe power and the side-lobe power” amounts to a mental process. For example, a person can mentally make changes to the values for the number of elements and the phase shifters and perform the calculations for the optimization again.
Regarding claim 4: The limitation “adjust the placement area further includes adapting a diameter for the placement area according to the distance constraint being unmet by the location, wherein the diameter is dynamically selected” covers a mental process. For example, this covers a person mentally making a judgment on the appropriate diameter.
Regarding claim 5:
The limitation “remove the one of the elements” amounts to a mental process. For example, a person can mentally remove an element from the model and perform the calculations for the optimization again.
The limitation “reduce the diameter randomly according to a difference between a main-lobe power and the side-lobe power” amounts to a mental process. For example, a person can mentally modify the diameter the model and perform the calculations for the optimization again.
Regarding claim 6:
The limitation “add an additional element while maintaining or increasing the diameter and satisfying the distance constraint” amounts to a mental process. For example, a person can mentally add an element from the model and perform the calculations for the optimization again.
Regarding claim 7:
The limitation “wherein the criteria is a difference between a main-lobe power and the side-lobe power” amounts to a mathematical concept and a mental process. For example, calculating a difference covers a mathematical concept involving mathematical calculations, equations/formulas, and/or relationships. A person can also mentally evaluate a difference mentally or with a pen and paper.
The limitation “the gradient operation minimizes a penalty associated with the side-lobe power for the elements” further limits the gradient operation recited in claim 1. Therefore, this amounts to a mathematical concept and a mental process for the similar reasons.
Regarding claim 8:
The limitation “group the elements using a pattern according to a manufacturing specification for the antenna array” amounts to a mental process. For example, this covers a person mentally making a judgment on the appropriate groupings of the elements.
The limitation “manufacture the antenna array for a radar system according to the physical layout and the pattern” is an additional element. However, this element does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception because it amounts to mere instructions to apply the judicial exception and generally linking the use of a judicial exception to a particular technological environment or field of use. Specifically, this amounts to merely applying the result of the layout design to the field of manufacturing it. See MPEP 2106.05(f) and 2106.05(h). This also amounts to an insignificant extra-solution activity. Specifically, this amounts to a post-solution activity of taking the result and manufacturing it. See MPEP 2106.05(g). Furthermore, this is akin to a well-understood, routine, and conventional activity as shown by the following references:
Zolesio et al. (WO2007147768A1)
Pg. 2: “The invention relates to a method for manufacturing an antenna with an optimized radiation pattern according to the constraints.”
Pg. 2: “There are methods of manufacturing antennas whose characteristics are calculated using templates for the conformation of the radiation pattern.”
Pg. 8: “The initial data is data describing the main parameters of the network antenna Ω to be manufactured.”
Mangenot et al. (US20140104107A1)
[0001]: “The invention relates to a method of manufacturing array antennas whose radiation pattern has a controlled envelope.”
[0019]: “The physical manufacturing step can be conventional.”
Zhou et al. (CN103353904A)
Pg. 9: “The eighth step is to use the optimization algorithm to solve the comprehensive optimization model, and judge whether the result is converged. If not, update the result obtained by the solution to the initial value of the design variable, and return to the third step to start the next solution again. Otherwise, the result Optimum structural parameters and excitation current to meet electromechanical performance; In the ninth step, according to the antenna electric field far-field data E A (θ, φ) obtained above, determine the sidelobe level and the beam pointing electrical performance index, and calculate the gain of the interlayer microstrip antenna; The tenth step is to design the feeding network in the active interlayer microstrip antenna by using the HFSS software according to the amplitude and phase of the excitation current of the radiating unit synthesized above, and finally, manufacture the antenna by using the integrated molding process.”
Regarding claim 9: The limitation “wherein the positions are initial positions according to a manufacturing specification associated with a radar system for a vehicle” merely further limits the positions recited in claim 1. Therefore, this amounts to a mathematical concept and mental process for the similar reasons.
Regarding claim 10: Claim 10 is substantially similar to claim 1 and therefore the similar analysis is applicable.
Furthermore, the limitation “A non-transitory computer-readable medium comprising: instructions that when executed by a processor cause the processor” is an addition element which amounts to no more than mere instructions to apply the judicial exception using a generic computer. Such an activity does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. See MPEP 2106.05(f).
Claims 11-20 are substantially similar to claims 1-9. Therefore, the similar analysis is applicable.
Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Claim Rejections - 35 USC § 103
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) 1, 4, 6, 9, 10, 12, 15, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lei et al. (US20210041557A1), hereinafter Lei, in view of Nunn et al. (US8928524B1), hereinafter Nunn.
Regarding claim 1, Lei discloses
a processor ([0078]: “The data processing system may include one or more processors, one or more memories, and devices connected via a bus. … Processors may be configured to execute instructions stored in the memories for performing the operations and steps discussed herein.”); and
a memory storing instructions that, when executed by the processor, cause the processor ([0078]: “The data processing system may include one or more processors, one or more memories, and devices connected via a bus. … Processors may be configured to execute instructions stored in the memories for performing the operations and steps discussed herein.”) to:
compute positions for elements on an antenna array within a placement area using randomization that accounts for varying quantities of the elements ([0017]: “According to some embodiments, a method is disclosed for designing a sparse array for an automotive radar of a specified array aperture and a specified number of antenna elements. The method, described as a particle swarm optimization method, moves each of a number of antenna elements to a range of candidate neighboring grid positions starting from an initial random seed placement to iteratively search for a placement of antenna elements that improves upon a cost function. … The method may search for a candidate placement with the lowest cost function among the multiple candidate placements based on the random seed placement.”)
according to a distance constraint ([0017]: “The cost function for each candidate placement of antenna elements of the array may be determined from characteristics of the FFT response associated with the candidate placement.”) ([0062]: “Referring back to FIG. 6A, given a desired effective array aperture, the linear distance between the two outermost Tx elements (i.e., Tx elements 1 and 3) and the linear distance between the two outermost Rx elements (i.e., Rx elements 1 and 4) may be determined. For example, if the desired effective array aperture is M·λ, the linear distance between the two outermost Tx elements may be P·λ and the linear distance between the two outermost Rx elements may be Q·λ, such that M·λ=P·λ+Q·λ based on how the virtual array is constructed by shifting the Rx elements by the spacing between the Tx elements of the MIMO array as described. The P·λ spacing and the Q·λ spacing may be divided into grids, where the grid spacing provides the spacing resolution for placing the Tx elements and Rx elements. The method searches for the placement of the Tx elements and Rx elements to minimize the cost function.”) and
a side-lobe power ([0054]: “FIG. 7 is a sample FFT response for a MIMO array to be discussed, but displays characteristics that are also pertinent to the FFT response for a conventional sparse array of FIG. 5. … Side lobes lower in received power are shown on both sides of the main lobe with the peak power of the first side lobe down by a delta 604 from the peak power of the main lobe. To compare the FFT responses for various candidate placements of antenna elements of the array to find an optimal placement, a cost function may be defined. In one embodiment, the cost function may be a function of the 3-db beamwidth 602 of the main lobe and the power level of the side lobes. For example, the cost function may be: cost function=α·SL+β·BW where α, β are weights and either one may be zero, SL may be the power of the maximum side lobe or the average power of all the side lobes, and BW is the 3-dB beamwidth 602 of the main lobe.”) ([0064]: “FIG. 7 is a sample FFT response as a function of azimuth angles for one placement of the antenna elements of a sample MIMO array for determining the cost function in a design method according to one embodiment. As described, a main lobe centered at 0 degree is characterized by a 3-dB beamwidth 602 and a first side lobe is down by a delta 604 from the peak power of the main lobe. In one embodiment, the cost function may be a function of the 3-db beamwidth 602 of the main lobe and the power level of the side lobes.”) ([0069]: “The cost function is used as metrics to compare the FFT response from different candidate placements to find an optimal placement. In one embodiment, the cost function may be a function of the 3-db beamwidth of the main lobe and the power level of the side lobes.”);
adjust the placement area according to a location associated with one of the elements ([0017]: “The method, described as a particle swarm optimization method, moves each of a number of antenna elements to a range of candidate neighboring grid positions starting from an initial random seed placement to iteratively search for a placement of antenna elements that improves upon a cost function.”) ([0051]: “FIG. 5 is a diagram illustrating an initial random seed placement of antenna elements and the neighboring grid positions to which some of the antenna elements may be moved in a design method for a conventional sparse array according to one embodiment. … The design method, referred to as particle swarm optimization method, moves each of a number of antenna elements to a number of candidate neighboring grid positions starting from the initial random seed placement to iteratively search for a placement of antenna elements that improves upon the initial random seed placement using a cost function.”); and
in response to the elements satisfying criteria after predetermined iterations, optimize the positions for a physical layout of the antenna array … according to the side-lobe power ([0017]: “The method, described as a particle swarm optimization method, moves each of a number of antenna elements to a range of candidate neighboring grid positions starting from an initial random seed placement to iteratively search for a placement of antenna elements that improves upon a cost function.”) ([0051]: “FIG. 5 is a diagram illustrating an initial random seed placement of antenna elements and the neighboring grid positions to which some of the antenna elements may be moved in a design method for a conventional sparse array according to one embodiment. … The design method, referred to as particle swarm optimization method, moves each of a number of antenna elements to a number of candidate neighboring grid positions starting from the initial random seed placement to iteratively search for a placement of antenna elements that improves upon the initial random seed placement using a cost function.”) ([0057]: “When the minimum cost function of the candidate placements is not less than the cost function of the last updated array placement, the operations stop and the cost function of the last updated array placement is determined as the minimum cost function of the random seed placement. Thus, the method iteratively searches for a local optimum of the array placement starting from the initial random seed placement.”).
Lei does not explicitly disclose using a gradient operation.
However, Nunn teaches using a gradient in an optimization problem of sidelobe suppression (Col. 9, Lines 40-43: “Such methods may involve taking the gradient of the objective function with respect to each one of the individual components, and performing a 1-D line search in the negative gradient or similar direction to find a minimum.”) (Col. 10, Lines 47-51: “A penalty method may be used to convert the above constrained optimization problem into an unconstrained optimization problem via the construction of an appropriate objective function, which can be solved using the conjugate gradient method, as described below.”) (Col. 10, Line 55 – Col. 11, Line10: “Thus, method 302 may include defining spectral and time constraints as discussed above to further constrain the optimization problem (step 306), and then using a penalty method to convert the constrained optimization problem into an unconstrained optimization problem (step 308). In general, a penalty method may include converting formal constraints into terms of the objective function whose minimization achieves the desired results. For example, if it were desired to minimize ISL, the objective function would be the ISL (i.e., sum of squares of time sidelobes) and penalty terms would be added for additional sidelobe characteristics desired to be suppressed. If one region of spectrum is desired to be suppressed, the spectrum can be discretized, and a penalty term added to the objective function in the form of the square of the difference between the discretized spectral power and the goal when the power is greater than the goal, (i.e. (Pi({right arrow over (x)})−ci)2 where Pi ({right arrow over (x)}) is the power at the l'th discretized component of the spectrum and ci is the goal at that position. Thus, the penalty method can be utilized to supplement the initial objective function in order to formulate an unconstrained optimization problem suitable for use with the conjugate gradient method.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Nunn on using a gradient method with the teachings from Lei on the optimization method. The motivation to combine would have been that such a method allows finding an optimum of a function which allows finding the appropriate sidelobe level for sidelobe suppression (Nunn, Col. 9, Lines 40-43: “Such methods may involve taking the gradient of the objective function with respect to each one of the individual components, and performing a 1-D line search in the negative gradient or similar direction to find a minimum.”).
Therefore, the combination of Lei and Nunn teaches
in response to the elements satisfying criteria after predetermined iterations, optimize the positions for a physical layout of the antenna array using a gradient operation according to the side-lobe power (Lei, [0017]: “The method, described as a particle swarm optimization method, moves each of a number of antenna elements to a range of candidate neighboring grid positions starting from an initial random seed placement to iteratively search for a placement of antenna elements that improves upon a cost function.”) (Lei, [0051]: “FIG. 5 is a diagram illustrating an initial random seed placement of antenna elements and the neighboring grid positions to which some of the antenna elements may be moved in a design method for a conventional sparse array according to one embodiment. … The design method, referred to as particle swarm optimization method, moves each of a number of antenna elements to a number of candidate neighboring grid positions starting from the initial random seed placement to iteratively search for a placement of antenna elements that improves upon the initial random seed placement using a cost function.”) (Lei, [0057]: “When the minimum cost function of the candidate placements is not less than the cost function of the last updated array placement, the operations stop and the cost function of the last updated array placement is determined as the minimum cost function of the random seed placement. Thus, the method iteratively searches for a local optimum of the array placement starting from the initial random seed placement.”) (Nunn, Col. 9, Lines 40-43: “Such methods may involve taking the gradient of the objective function with respect to each one of the individual components, and performing a 1-D line search in the negative gradient or similar direction to find a minimum.”) (Nunn, Col. 10, Lines 47-51: “A penalty method may be used to convert the above constrained optimization problem into an unconstrained optimization problem via the construction of an appropriate objective function, which can be solved using the conjugate gradient method, as described below.”) (Nunn, Col. 10, Line 55 – Col. 11, Line10: “Thus, method 302 may include defining spectral and time constraints as discussed above to further constrain the optimization problem (step 306), and then using a penalty method to convert the constrained optimization problem into an unconstrained optimization problem (step 308). In general, a penalty method may include converting formal constraints into terms of the objective function whose minimization achieves the desired results. For example, if it were desired to minimize ISL, the objective function would be the ISL (i.e., sum of squares of time sidelobes) and penalty terms would be added for additional sidelobe characteristics desired to be suppressed. If one region of spectrum is desired to be suppressed, the spectrum can be discretized, and a penalty term added to the objective function in the form of the square of the difference between the discretized spectral power and the goal when the power is greater than the goal, (i.e. (Pi({right arrow over (x)})−ci)2 where Pi ({right arrow over (x)}) is the power at the l'th discretized component of the spectrum and ci is the goal at that position. Thus, the penalty method can be utilized to supplement the initial objective function in order to formulate an unconstrained optimization problem suitable for use with the conjugate gradient method.”).
Regarding claim 4, Lei/Nunn teaches
adjust the placement area further includes adapting a diameter for the placement area according to the distance constraint being unmet by the location, wherein the diameter is dynamically selected (Lei, [0068]: “At block 803, the method generates a random seed placement for up to N antenna elements on a grid whose spacing provides the spacing resolution for placing the antenna elements of an antenna array. The method may determine the aperture size of the antenna array and the number of antenna elements of the antenna array. In one embodiment, the array aperture may be determined based on the desired beamwidth or the desired angle resolution of the antenna beam. For example, based on the array aperture, the linear distance between the two outermost Tx antenna elements or the two outermost Rx antenna elements may be determined.”) (Lei, [0052]: “As the antenna aperture and the number of antenna elements of a sparse array increase to achieve better angle resolution afforded by a smaller beamwidth, the search for an optimal placement of the antenna elements becomes exponentially more burdensome. The goal of the design method is to find an optimal placement of the antenna elements to minimize the cost function in a computationally efficient manner. … Based on the array aperture, the linear distance between the two outermost antenna elements, denoted as element 1 and element 8, is determined. In one embodiment, the array aperture may be expressed in unit of the wavelength of the radar operating frequency, λ. For example, element 1 and element 8 may be placed M λ apart to yield the desired array aperture of M λ. The method searches for the placement of the remaining 6 antenna elements within the array aperture to minimize the cost function.”) (Lei, [0053]: “In one embodiment, the distance between element 1 and element 8, or the array aperture, may be divided into grids, where the grid spacing provides the spacing resolution for placing the remaining 6 antenna elements. … Using a random seed, the remaining 6 antenna elements are randomly placed on the grids, as shown by the initial placement of elements 2-7 in FIG. 5.”) (Lei, [0055]: “In one embodiment, the neighboring region may be in two dimensions, e.g. in both the azimuth and elevation directions. For example, in addition to dividing the array aperture into grids along the azimuth x-direction as possible placement locations of the elements, grids may also be placed along the elevation y-direction. An element may be moved to a grid encompassed within a two-dimensional region surrounding the element.”).
Examiner notes that Lei discloses adjusting the placement area of the antenna elements considering the distance between elements. Lei discloses that this placement area may be in azimuth directions including both x- and y-directions. Therefore, this amounts to the distance being a diameter.
Regarding claim 6, Lei/Sun teaches
add an additional element while maintaining or increasing the diameter and satisfying the distance constraint (Lei, [0068]: “The method may determine the aperture size of the antenna array and the number of antenna elements of the antenna array. … The number of antenna elements may be determined based on a tradeoff between the performance, power, and cost of the array. In one embodiment, the N randomly placed antenna elements may exclude the two outermost Rx antenna elements of a conventional array used to determine the array aperture. In one embodiment, the N randomly placed antenna elements may include all the Tx and Rx elements of a MIMO array.”) (Lei, [0055]: “To search for candidate placements with a lower cost function, the method may move one of the 6 randomly placed antenna elements (i.e., one of elements 2-7) to grids in a neighboring region while keeping the locations of the other antenna elements the same.”).
Regarding claim 9, Lei/Sun teaches
wherein the positions are initial positions according to a manufacturing specification associated with a radar system for a vehicle (Lei, [0017]: “According to some embodiments, a method is disclosed for designing a sparse array for an automotive radar of a specified array aperture and a specified number of antenna elements. … The method may be used to efficiently design MIMO arrays as well as conventional sparse arrays of an arbitrary array aperture and number of antenna elements for automotive radars.”).
Regarding claim 10, claim 10 is substantially similar to claim 1. Therefore, the similar analysis is applicable.
Furthermore, Lei/Sun teaches
A non-transitory computer-readable medium comprising: instructions that when executed by a processor cause the processor (Lei, [0082]: “Such a computer program is stored in a non-transitory computer readable medium. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer).”).
Claims 12, 15, 17, and 20 are substantially similar to claims 1, 4, 6, and 9. Therefore, they are rejected for the similar reasons.
Claim(s) 2, 3, 11, 13, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lei in view of Nunn, in further view of Cui et al. (CN111551923A), hereinafter Cui, in further view of Goto et al. (“Phase-Shifter Thinning and Sidelobe Reduction for Large Phased Arrays”), hereinafter Goto.
Regarding claim 2, Lei/Nunn teaches
wherein the instructions to compute the positions further include instructions to move … the positions randomly (Lei, [0017]: “The method, described as a particle swarm optimization method, moves each of a number of antenna elements to a range of candidate neighboring grid positions starting from an initial random seed placement to iteratively search for a placement of antenna elements that improves upon a cost function.”) (Lei, [0051]: “FIG. 5 is a diagram illustrating an initial random seed placement of antenna elements and the neighboring grid positions to which some of the antenna elements may be moved in a design method for a conventional sparse array according to one embodiment. … The design method, referred to as particle swarm optimization method, moves each of a number of antenna elements to a number of candidate neighboring grid positions starting from the initial random seed placement to iteratively search for a placement of antenna elements that improves upon the initial random seed placement using a cost function.”),
a distribution size (Lei, [0058]: “The method may generate a number of random seed placements of elements 2-7 and may perform the operations described to iteratively search for a local optimum of the array placement starting from each of the random seed placements. … In one embodiment, the number of random seeds may be determined by the number of antenna elements”) (Lei, [0065]: “Starting from the updated array placement, the method may iteratively search for a local optimum of the array placement starting from the initial random seed placement by repeating the operations of moving one element to grids in its neighboring region while keeping the other elements in their current locations in the last updated array placement to obtain a number of candidate placements”).
Lei/Nunn does not explicitly teach using a Monte Carlo method and considering the number of active phase shifters and the elements grouped according to one of a shape and a size.
However, Cui teaches using a Monte Carlo method in sidelobe suppression ([0128]: “Figure 5 shows the array gain results corresponding to the optimal weighting coefficients obtained using the ADPM and ADMM algorithms after 500 Monte Carlo simulation experiments … Figure 5 shows the results of the two algorithms for obtaining the maximum array gain after 500 Monte Carlo simulations”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Cui on the Monte Carlo method with the teachings from Lei/Nunn on the optimization method. The motivation to combine would have been that doing so allows solving for maximum array gain (Cui, [0128]: “Figure 5 shows the results of the two algorithms for obtaining the maximum array gain after 500 Monte Carlo simulations, indicating that the ADPM algorithm is better than the ADMM algorithm in solving for the maximum array gain.”).
Therefore, the combination of Lei/Nunn and Cui teaches
wherein the instructions to compute the positions further include instructions to move, using a Monte Carlo method (Lei, [0017]: “The method, described as a particle swarm optimization method, moves each of a number of antenna elements to a range of candidate neighboring grid positions starting from an initial random seed placement to iteratively search for a placement of antenna elements that improves upon a cost function.”) (Lei, [0051]: “FIG. 5 is a diagram illustrating an initial random seed placement of antenna elements and the neighboring grid positions to which some of the antenna elements may be moved in a design method for a conventional sparse array according to one embodiment. … The design method, referred to as particle swarm optimization method, moves each of a number of antenna elements to a number of candidate neighboring grid positions starting from the initial random seed placement to iteratively search for a placement of antenna elements that improves upon the initial random seed placement using a cost function.”) (Cui, [0128]: “Figure 5 shows the array gain results corresponding to the optimal weighting coefficients obtained using the ADPM and ADMM algorithms after 500 Monte Carlo simulation experiments … Figure 5 shows the results of the two algorithms for obtaining the maximum array gain after 500 Monte Carlo simulations”).
Lei/Nunn/Cui does not explicitly teach considering the number of active phase shifters and the elements grouped according to one of a shape and a size.
However, Goto teaches reducing the number of active phase shifters by grouping neighboring elements (Pg. 139, Abstract: “This paper introduces a method for determining the region and the extent of pattern deterioration when each phase shifter is used to control the phase of two elements in a linear array.”) (Pg. 139, Right column: “The many phase shifters lead to high cost as well as to great complexity in control circuitry, and it would be highly desirable if their number could be reduced without impairing the array performance beyond tolerable limits”) (Pg. 140, Left column: “In the case of a 50- percent thinning, two adjacent elements are excited through a common phase shifter”).
Examiner notes that Lei/Nunn/Cui teaches separating/moving elements based on their geometries (Lei, [0060]: “To obtain the placement of virtual Rx elements 5-8, virtual Rx elements 1-4 are shifted by the spacing between Tx element 1 and Tx element 2 of the MIMO array to account for the difference in the geometry from the physical Rx elements 1-4 to Tx element 1 and from Rx elements 1-4 to Tx element 2 of the MIMO array. Similarly, to obtain the placement of virtual Rx elements 9-12, virtual Rx elements 1-4 are shifted by the spacing between Tx element 1 and Tx element 3 of the MIMO array to account for the different in the geometry from the physical Rx elements 1-4 to the two Tx elements 2 and 3.”).
Therefore, the combination of Lei/Nunn/Cui and Goto would teach grouping neighboring elements that are grouped based on their geometries, which would reduce the number of phase shifters used.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Goto on reducing the number of active phase shifters by grouping neighboring elements with the teachings from Lei/Nunn/Cui on the constraints associated with the optimization method. The motivation to combine would have been that doing so allows reducing the high cost and complexity associated with the number of phase shifters used (Goto, Pg. 139, Right column: “The many phase shifters lead to high cost as well as to great complexity in control circuitry, and it would be highly desirable if their number could be reduced without impairing the array performance beyond tolerable limits”).
Therefore, the combination of Lei/Nunn/Cui and Goto teaches
wherein the instructions to compute the positions further include instructions to move, using a Monte Carlo method, the positions randomly until a number of phase shifters are active (Lei, [0017]: “The method, described as a particle swarm optimization method, moves each of a number of antenna elements to a range of candidate neighboring grid positions starting from an initial random seed placement to iteratively search for a placement of antenna elements that improves upon a cost function.”) (Lei, [0051]: “FIG. 5 is a diagram illustrating an initial random seed placement of antenna elements and the neighboring grid positions to which some of the antenna elements may be moved in a design method for a conventional sparse array according to one embodiment. … The design method, referred to as particle swarm optimization method, moves each of a number of antenna elements to a number of candidate neighboring grid positions starting from the initial random seed placement to iteratively search for a placement of antenna elements that improves upon the initial random seed placement using a cost function.”) (Cui, [0128]: “Figure 5 shows the array gain results corresponding to the optimal weighting coefficients obtained using the ADPM and ADMM algorithms after 500 Monte Carlo simulation experiments … Figure 5 shows the results of the two algorithms for obtaining the maximum array gain after 500 Monte Carlo simulations”) (Goto, Pg. 139, Abstract: “This paper introduces a method for determining the region and the extent of pattern deterioration when each phase shifter is used to control the phase of two elements in a linear array.”) (Goto, Pg. 139, Right column: “The many phase shifters lead to high cost as well as to great complexity in control circuitry, and it would be highly desirable if their number could be reduced without impairing the array performance beyond tolerable limits”) (Goto, Pg. 140, Left column: “In the case of a 50- percent thinning, two adjacent elements are excited through a common phase shifter”),
wherein the Monte Carlo method is associated with a distribution size and the elements are grouped according to one of a shape and a size (Lei, [0017]: “The method, described as a particle swarm optimization method, moves each of a number of antenna elements to a range of candidate neighboring grid positions starting from an initial random seed placement to iteratively search for a placement of antenna elements that improves upon a cost function.”) (Lei, [0051]: “FIG. 5 is a diagram illustrating an initial random seed placement of antenna elements and the neighboring grid positions to which some of the antenna elements may be moved in a design method for a conventional sparse array according to one embodiment. … The design method, referred to as particle swarm optimization method, moves each of a number of antenna elements to a number of candidate neighboring grid positions starting from the initial random seed placement to iteratively search for a placement of antenna elements that improves upon the initial random seed placement using a cost function.”) (Lei, [0058]: “The method may generate a number of random seed placements of elements 2-7 and may perform the operations described to iteratively search for a local optimum of the array placement starting from each of the random seed placements. … In one embodiment, the number of random seeds may be determined by the number of antenna elements”) (Lei, [0065]: “Starting from the updated array placement, the method may iteratively search for a local optimum of the array placement starting from the initial random seed placement by repeating the operations of moving one element to grids in its neighboring region while keeping the other elements in their current locations in the last updated array placement to obtain a number of candidate placements”) (Lei, [0060]: “To obtain the placement of virtual Rx elements 5-8, virtual Rx elements 1-4 are shifted by the spacing between Tx element 1 and Tx element 2 of the MIMO array to account for the difference in the geometry from the physical Rx elements 1-4 to Tx element 1 and from Rx elements 1-4 to Tx element 2 of the MIMO array. Similarly, to obtain the placement of virtual Rx elements 9-12, virtual Rx elements 1-4 are shifted by the spacing between Tx element 1 and Tx element 3 of the MIMO array to account for the different in the geometry from the physical Rx elements 1-4 to the two Tx elements 2 and 3.”) (Cui, [0128]: “Figure 5 shows the array gain results corresponding to the optimal weighting coefficients obtained using the ADPM and ADMM algorithms after 500 Monte Carlo simulation experiments … Figure 5 shows the results of the two algorithms for obtaining the maximum array gain after 500 Monte Carlo simulations”) (Goto, Pg. 139, Abstract: “This paper introduces a method for determining the region and the extent of pattern deterioration when each phase shifter is used to control the phase of two elements in a linear array.”) (Goto, Pg. 139, Right column: “The many phase shifters lead to high cost as well as to great complexity in control circuitry, and it would be highly desirable if their number could be reduced without impairing the array performance beyond tolerable limits”) (Goto, Pg. 140, Left column: “In the case of a 50- percent thinning, two adjacent elements are excited through a common phase shifter”).
Regarding claim 3, Lei/Nunn/Cui/Goto teaches
reduce, using the Monte Carlo method, a number of the elements and the number of phase shifters to steer a main beam at a main-lobe power and the side-lobe power (Lei, [0003]: “The size of the array of antenna elements as determined from the cumulative linear spacing between the antenna elements, called the array aperture, is inversely proportional to the beamwidth of the antenna beam.”) (Lei, [0052]: “In one embodiment, the array aperture may be determined based on the desired beamwidth or the desired angle resolution of the beam. … For example, element 1 and element 8 may be placed M λ apart to yield the desired array aperture of M λ.”) (Lei, [0059]: “Based on the relative geometry of the Tx and Rx elements, a virtual array may be determined based on the MIMO array to yield an effective array aperture.”) (Lei, [0061]: “MIMO array thus has the advantage of achieving a desired array aperture and a desired array response using fewer elements and a more compact design than an equivalently performing conventional array.”) (Goto, Pg. 139, Abstract: “This paper introduces a method for determining the region and the extent of pattern deterioration when each phase shifter is used to control the phase of two elements in a linear array.”) (Goto, Pg. 139, Right column: “The many phase shifters lead to high cost as well as to great complexity in control circuitry, and it would be highly desirable if their number could be reduced without impairing the array performance beyond tolerable limits”) (Goto, Pg. 140, Left column: “In the case of a 50- percent thinning, two adjacent elements are excited through a common phase shifter”).
The already provided combination is applicable.
Claims 11, 13, and 14 are substantially similar to claims 2 and 3. Therefore, they are rejected for the similar reasons.
Claim(s) 5, 7, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lei in view of Nunn in further view of Cui.
Regarding claim 5, Lei/Nunn teaches
remove the one of the elements (Lei, [0061]: “MIMO array thus has the advantage of achieving a desired array aperture and a desired array response using fewer elements and a more compact design than an equivalently performing conventional array.”) (Lei, [0068]: “The method may determine the aperture size of the antenna array and the number of antenna elements of the antenna array. … The number of antenna elements may be determined based on a tradeoff between the performance, power, and cost of the array. In one embodiment, the N randomly placed antenna elements may exclude the two outermost Rx antenna elements of a conventional array used to determine the array aperture. In one embodiment, the N randomly placed antenna elements may include all the Tx and Rx elements of a MIMO array.”); and
reduce the diameter randomly (Lei, [0068]: “At block 803, the method generates a random seed placement for up to N antenna elements on a grid whose spacing provides the spacing resolution for placing the antenna elements of an antenna array. The method may determine the aperture size of the antenna array and the number of antenna elements of the antenna array. In one embodiment, the array aperture may be determined based on the desired beamwidth or the desired angle resolution of the antenna beam. For example, based on the array aperture, the linear distance between the two outermost Tx antenna elements or the two outermost Rx antenna elements may be determined.”) (Lei, [0055]: “In one embodiment, the neighboring region may be in two dimensions, e.g. in both the azimuth and elevation directions. For example, in addition to dividing the array aperture into grids along the azimuth x-direction as possible placement locations of the elements, grids may also be placed along the elevation y-direction. An element may be moved to a grid encompassed within a two-dimensional region surrounding the element.”).
Lei/Nunn does not explicitly teach a difference between a main-lobe power and the side-lobe power.
However, Cui teaches calculating the difference between a main-lobe power and the side-lobe power in the optimization of low sidelobe beamforming (Cui, [0081]: “The optimization problem of low sidelobe beamforming is to maximize the objective function while satisfying the constraints of main lobe level, sidelobe level, and weight amplitude. The optimal weighting coefficients of the array are then solved, and the optimization problem P”) (Cui, [0071]: “Set a range for the main lobe level to facilitate the measurement of the effect of the side lobe level (the effect of a low side lobe is measured by the difference between the main lobe level and the side lobe level).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Cui on calculating the difference between a main-lobe power and the side-lobe power with the teachings from Lei/Nunn on the method for achieving the optimal or desired the antenna beam and array response. The motivation to combine would have been that doing so allows solving the optimization for low sidelobe beamforming while considering relevant constraints (Cui, [0081]: “The optimization problem of low sidelobe beamforming is to maximize the objective function while satisfying the constraints of main lobe level, sidelobe level, and weight amplitude. The optimal weighting coefficients of the array are then solved, and the optimization problem P”).
Therefore, the combination of Lei/Nunn and Cui teaches
reduce the diameter randomly according to a difference between a main-lobe power and the side-lobe power (Lei, [0068]: “At block 803, the method generates a random seed placement for up to N antenna elements on a grid whose spacing provides the spacing resolution for placing the antenna elements of an antenna array. The method may determine the aperture size of the antenna array and the number of antenna elements of the antenna array. In one embodiment, the array aperture may be determined based on the desired beamwidth or the desired angle resolution of the antenna beam. For example, based on the array aperture, the linear distance between the two outermost Tx antenna elements or the two outermost Rx antenna elements may be determined.”) (Lei, [0055]: “In one embodiment, the neighboring region may be in two dimensions, e.g. in both the azimuth and elevation directions. For example, in addition to dividing the array aperture into grids along the azimuth x-direction as possible placement locations of the elements, grids may also be placed along the elevation y-direction. An element may be moved to a grid encompassed within a two-dimensional region surrounding the element.”) (Cui, [0071]: “Set a range for the main lobe level to facilitate the measurement of the effect of the side lobe level (the effect of a low side lobe is measured by the difference between the main lobe level and the side lobe level).”).
Regarding claim 7, Lei/Nunn/Cui teaches
wherein the criteria is a difference between a main-lobe power and the side-lobe power (Cui, [0081]: “The optimization problem of low sidelobe beamforming is to maximize the objective function while satisfying the constraints of main lobe level, sidelobe level, and weight amplitude. The optimal weighting coefficients of the array are then solved, and the optimization problem P”) (Cui, [0071]: “Set a range for the main lobe level to facilitate the measurement of the effect of the side lobe level (the effect of a low side lobe is measured by the difference between the main lobe level and the side lobe level).”).
The already provided combination is applicable.
Nunn further teaches the gradient operation minimizes a penalty associated with the side-lobe power for the elements (Nunn, Col. 3, Lines 58-61: “Lengthening the signal duration provides additional degrees of freedom which makes it easier to meet sideband requirements with minimal PAPR penalties.”) (Nunn, Col. 4, Lines 54-57: “This may avoid the very large peak-to-average power penalties seen, for example, in Orthogonal Frequency division Multiplexing (OFDM), which can be as high as 15 dB.”) (Nunn, Col. 10 Line 66 – Col. 11 Line 10: “If one region of spectrum is desired to be suppressed, the spectrum can be discretized, and a penalty term added to the objective function in the form of the square of the difference between the discretized spectral power and the goal when the power is greater than the goal, (i.e. (Pi({right arrow over (x)})−ci)2 where Pi ({right arrow over (x)}) is the power at the l'th discretized component of the spectrum and ci is the goal at that position. Thus, the penalty method can be utilized to supplement the initial objective function in order to formulate an unconstrained optimization problem suitable for use with the conjugate gradient method.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Nunn on minimizing the penalty associated with the side-lobe power for the elements with the teachings from Lei/Nunn/Cui on the gradient operation. The motivation to combine would have been that doing so allows suppressing a desired region which supplements the objective function for use in gradient method (Nunn, Col. 10 Line 66 – Col. 11 Line 10: “If one region of spectrum is desired to be suppressed, the spectrum can be discretized, and a penalty term added to the objective function in the form of the square of the difference between the discretized spectral power and the goal when the power is greater than the goal, (i.e. (Pi({right arrow over (x)})−ci)2 where Pi ({right arrow over (x)}) is the power at the l'th discretized component of the spectrum and ci is the goal at that position. Thus, the penalty method can be utilized to supplement the initial objective function in order to formulate an unconstrained optimization problem suitable for use with the conjugate gradient method.”).
Claims 16 and 18 are substantially similar to claims 5 and 7. Therefore, they are rejected for the similar reasons.
Claim(s) 8 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lei in view of Nunn in further view of Zolesio et al. (WO2007147768A1), hereinafter Zolesio.
Regarding claim 8, Lei/Nunn does not explicitly teach
group the elements using a pattern according to a manufacturing specification for the antenna array; and
manufacture the antenna array for a radar system according to the physical layout and the pattern.
However, Zolesio teaches
group the elements using a pattern according to a manufacturing specification for the antenna array (Pg. 2, “The invention relates to a method for manufacturing an antenna with an optimized radiation pattern according to the constraints. In particular, the invention applies to the manufacture of antennas comprising radiating elements grouped into networks. The antenna obtained by the manufacturing method according to the invention has a geometrical configuration and a power supply to which corresponds a radiation pattern of the antenna whose secondary lobes and lattice lobes are at the lowest possible level according to the constraints, while maintaining maximum power in the main lobe.”) (Pg. 2, “An optimum said active antenna, in particular an active antenna comprising sub-networks, is defined by characteristics making it possible in particular to minimize the secondary lobes and the network lobes of the antenna radiation pattern. Among the optimized characteristics of the optimum antenna, there may be mentioned the position on said antenna radiating elements (or transmit and receive modules), the weighting of the supply of radiating elements (or modules of emission and of reception), or the form groupings in subnets of the radiating elements (or modules emission and reception) of the active antenna and their power supplies.”) (Pg. 3: “In one embodiment, the radiating elements being grouped into subnetworks”); and
manufacture the antenna array for a radar system according to the physical layout and the pattern (Pg. 2: “The invention relates to a method for manufacturing an antenna with an optimized radiation pattern according to the constraints.”) (Pg. 2: “There are methods of manufacturing antennas whose characteristics are calculated using templates for the conformation of the radiation pattern.”) (Pg. 8: “The initial data is data describing the main parameters of the network antenna Ω to be manufactured.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from Zolesio on grouping the elements using a pattern according to a manufacturing specification and manufacture the antenna array according to the physical layout and the pattern with the teachings from Lei/Nunn on the method for optimizing the antenna design. The motivation to combine would have been that allows manufacturing an antenna whose secondary lobes and lattice lobes are at the lowest possible level according to the constraints while maintaining maximum power in the main lobe, and which can be used in various applications such as in the field of radar or telecommunications (Zolesio, Pg. 2: “The invention relates to a method for manufacturing an antenna with an optimized radiation pattern according to the constraints. In particular, the invention applies to the manufacture of antennas comprising radiating elements grouped into networks. The antenna obtained by the manufacturing method according to the invention has a geometrical configuration and a power supply to which corresponds a radiation pattern of the antenna whose secondary lobes and lattice lobes are at the lowest possible level according to the constraints, while maintaining maximum power in the main lobe. The invention can be applied to various antennas used in various devices such as the field of radar or telecommunications.”).
Claim 19 is substantially similar to claim 8. Therefore, it is rejected for the similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sun (CN104102775A) discloses side-lobe suppression based beam optimization method for antennas using a gradient method.
Lei et al. (CN108446437A) discloses an array antenna wide beam power gain optimization method using Monte Carlo simulations.
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/HEIN JEONG/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186