Notice of Pre-AIA or AIA Status
Claims 1-20 are currently presented for Examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 04/27/2026 has been entered and considered by the examiner. By the amendment, claims 1-3, 5, 8, 10-14, 16 and 19 are amended. Following Applicants arguments and amendments made, Examiner modify the prior art rejections. And, the 101 rejection is still maintained. See office action.
Applicant arguments 101 rejection
First, under Step 2A, Prong 1, amended claim 1 now recites "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, and a number of physical phase shifters that are active." Furthermore, amended claim 1 now recites to "optimize the positions for a physical layout during manufacturing of the antenna array using a gradient operation according to the side-lobe power." Here, the features "using randomization that accounts for varying quantities of the elements according to a distance constraint and a side- lobe power" and "optimize the positions for a physical layout during manufacturing of the antenna array using a gradient operation according to the side-lobe power." (Emphasis added). At least these features cannot practically and reasonably perform in the human mind. For instance, such features may demand purpose-built hardware, testing equipment, etc.
Examiner response
Examiner respectfully disagrees. Applicant do not provide the reason why they do not fall under the abstract idea. Examiner rely the combination of “Mathematical Concepts” and “mental process” to reject claim instead of mental process only. The steps of "computing positions," applying "randomization" based on distance constraints and side-lobe power, and performing "gradient operations" to optimize layout positions are pure mathematical algorithms and mental processes. These operations can be performed mentally, with paper and pencil, or by a generic processor executing mathematical formulas. The fact that the claim mentions "physical layout during manufacturing" does not alter the underlying character of the steps, which are generally linking to the field of use or technological environment. (See MPEP 2106.05(h) The claims do not recite a specific, unconventional improvement to the functioning of an antenna or a technological manufacturing process itself; rather, they claim the abstract math of designing or calculating where elements should go. For example, An engineer sitting at a desk with a pen and paper can calculate coordinates for an antenna array, applies a distance constraint rule to space elements out and computes side-lobe power adjustments and tweaks the numbers step-by-step to optimize the layout before any physical manufacturing begins.
Applicant arguments
Second, under Step 2A/Prong Two, amended claim 1 now clearly integrates the system into a practical application at least with "accounts for varying quantities of the elements according
to a distance constraint and a side-lobe power, and a number of physical phase shifters that are active." Unlike Electric Power Group, LLC v. Alstom S.A., at least these features are more than collecting and analyzing information. In particular, amended claim 1 has a technical improvement at least with side-lobe performance for an antenna array by computing "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, and a number of physical phase shifters that are active" and optimizing "the positions for a physical layout during manufacturing of the antenna array using a gradient operation according to the side-lobe power." This clearly constitutes a concrete technological application and an improvement in the field of antenna design.
Examiner response
Examiner respectfully disagrees. Under Step 2A, Prong One, amended claim 1 recites mathematical calculations and optimization algorithms (e.g., "computing positions for elements... using randomization," "optimizing the positions... using a gradient operation according to the side-lobe power"), which fall within the grouping of abstract ideas as mathematical concepts and mental process. Under Step 2A, Prong Two, the claim as a whole does not integrate the abstract idea into a practical application. The recitation of physical components such as an "antenna array" and "phase shifters" merely provides a nominal environment in which the mathematical optimization is performed. The steps of calculating positions and running gradient operations do not improve the physical operation of the phase shifters or the operational capability of the antenna array itself; rather, they result in a designed layout. Calculating a better blueprint or arrangement via mathematical manipulation remains an abstract idea. Furthermore, the reliance on a generic manufacturing context does not transform the abstract calculation into a patent-eligible application. See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016) (collecting, analyzing, and displaying data is abstract regardless of the technological environment). Because the claim does not include significantly more (Step 2B) to turn the abstract optimization into a patent-eligible application, the rejection under 35 U.S.C. § 101 is maintained.
Applicant arguments
Third, amended claim 1 now recites a technical improvement similar to the patent in Enfish, LLC v. Microsoft Corp. relating to optimizing "the positions for a physical layout during manufacturing of the antenna array using a gradient operation according to the side-lobe power." For example, the technical improvement of the optimization system in claims 1-20 may include suppressing "side-lobe power...while forming augmented main-lobes by the optimization system 200 adding, removing, grouping, or ungrouping elements according to a design profile." (Present application, paragraph [0027]). In another example, the optimization system of amended claim 1 as a whole at least solves the technical problem of increasing "fabrication yield while reducing phase shifter quantities. Accordingly, the optimization system uses randomizations with gradient operations to efficiently compute physical layouts for antenna arrays that improves suppression of side-lobe power and increases manufacturing yield while satisfying a design profile." (Id., Paragraph [0019]).
Examiner response
Examiner respectfully disagrees. Claim 1 recites the process of optimizing physical layouts for an antenna array using randomizations and gradient operations according to side-lobe power. This is a combination of mathematical concepts and mental process of abstract idea. The applicant's argument on Enfish, LLC v. Microsoft Corp. is unpersuasive. Enfish applies to improvements to computer-related technology, such as the logical structure of a database. The instant claims do not recite an improvement to computer capabilities, data transfer speeds, or execution efficiency. Instead, they use mathematical optimization to design a physical antenna array. Under Step 2B, the elements recited (e.g., an optimization system that adds or removes elements) are high-level functional abstractions performed by generic components. The claim does not recite a specific, unconventional hardware configuration or a non-conventional manufacturing step that transforms the mathematical algorithm into a patent-eligible application." Therefore, the claim is ineligible.
Applicant arguments
As such, the technical improvements in the specification and claimed features and the nexus described above are similar to the facts in Desjardins. Therefore, amended claim 1 is eligible for patenting for at least these reasons.
Examiner response
Examiner respectfully disagrees. Applicant argument using Ex parte Desjardins is unpersuasive. Ex parte Desjardins applies to specific technological enhancements within computational models, such as reducing system complexity or resolving internal training anomalies like catastrophic forgetting within an artificial intelligence framework. In the present case, the purported benefits—increasing fabrication yield and reducing phase shifter quantities—are end-products of a design choice and physical manufacturing result, not an improvement to how a machine-learning or software model internally functions or operates. Performing mathematical randomization and side-lobe calculations does not integrate the abstract idea into a functional tool because it merely yields design data rather than structurally altering the optimization execution framework itself. Because the claim recites an abstract mathematical operation without integrating it into a specific, unconventional operational improvement of computer capabilities, the rejection under 35 U.S.C. § 101 is maintained.
Applicant arguments 103 rejection
Nunn discusses optimization of signal waveforms and transmissions using gradient-based techniques. These techniques include conjugate gradients and penalty functions to suppress sidelobes in spectral domains for communications. (See Nunn, Col. 9, Lines 40-43; Col. 10, Lines 47-51; Col. 10, Line 55 - Col. 11, Line 10). However, Nunn at least does not discuss to "optimize the positions for a physical layout during manufacturing of the antenna array using a gradient operation according to the side-lobe power" where the antenna array is associated "a placement area using randomization that accounts for varying quantities of the elements according to a distance constraint and a side-lobe power, and a number of physical phase shifters that are active" as recited by amended claim 1. As such, the rejection at least lacks a clear technical reasoning and explanation of how a person having ordinary skill in the skill would implement the waveform optimization of Nunn to modify antenna-placement optimization of Lei. Therefore, Lei and Nunn are not combinable without more reasoning by the Examiner.
Examiner response
Examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The rejection relies on Lei, rather than Nunn, for optimization of antenna-element positions. Lei teaches generating candidate physical-antenna placements and optimizing those placements based upon a cost function determined from the antenna FFT response, wherein the cost function expressly includes sidelobe power.
However, in view of Applicant amendments and Arguments, Examiner withdraw the Nunn references and add the new reference Lamontagne that teaches gradient method for optimization in the field of antenna array construction and the physical phasor shifter. See office action for detail.
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 and a number of physical phase shifters that are active” 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 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). The additional elements of “for a physical layout during manufacturing” is no more than generally linking the abstract idea to a field of use or technological environment as discussed in MPEP 2106.05(h).
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). The additional elements of “for a physical layout during manufacturing” is no more than generally linking the abstract idea to a field of use or technological environment as discussed in MPEP 2106.05(h).
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 the number of the physical 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” and “identify a candidate geometry of the antenna array with a subset of the elements being active by sampling a distribution of the positions using the Monte Carlo method” are 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 the physical 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 physical 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 one of the elements upon filling the placement area with the elements and violating a parameter” 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 “modify the positions of the elements furthest from an origin of the placement area and maintain an element position of the positions within a pair proximate to the origin and 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 modifying and 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.
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, 4, 5-7, 9, 10, 12, 15-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lei et al. (US20210041557A1), hereinafter Lei, in view of Lamontagne et al. (US8928524B1), hereinafter Lamontagne and further in view of Lamontagne et al (US20180261917A1)
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 during manufacturing 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 a number of physical phase shifters that are active and optimize the positions for a physical layout during manufacturing of the antenna array using a gradient operation according to the side-lobe power.
In the related field of invention, Lamontagne teaches a number of physical phase shifters that are active. (See fig 3 (element 405) and fig 4 (element 605) and [0051]- For example the antenna parameters may include the antenna coefficients that indicate phase shift and/or gain to be applied to the antenna elements of the TX antenna array 225 and RX antenna array 235. In cases in which a position of at least some of the antenna elements may be movable, the antenna parameters may include a position to place the antenna elements. In some cases, the control information may turn on or off an antenna element, such that the antenna element is not used for transmission and/or reception. See [0054]- each antenna element may be associated with a phase shifter. See also [0063-0065]-A phase shifter 405 and an amplifier 410 (e.g., a power amplifier) may be part of a TX distribution circuit. The phase shifter 405 may receive a signal (e.g., a signal converted to radio frequency) and apply phase shift to the signal. A phase shifter 605 and an amplifier 610 (e.g., a low noise amplifier) may be part of an RX distribution circuit.)
optimize the positions for a physical layout during manufacturing of the antenna array using a gradient operation according to the side-lobe power.(see para 27- any parameters that may affect the construction and/or application of the antenna array and, thus, the radiation pattern exhibited by the antenna array. see para 85-91-The TX and/or RX antenna parameters may be adjusted based on optimization methods including, by way of non-limiting example, steepest descent, conjugate gradients, simulated annealing, genetic algorithm, and/or other optimization methods such that the antenna pattern (and/or associated characteristic(s)) effectuated by the TX and RX antenna parameters converges toward the desired antenna pattern (and/or associated characteristic(s)). At block 925, the device 800 determines a score based on the antenna system radiation pattern determined at block 920 and reference information. In an aspect, the reference information may be a reference radiation pattern, such as a desired antenna pattern set based on a design specification. In this aspect, the score may be indicative of (e.g., based on) a difference between the determined antenna pattern and the reference antenna pattern. Alternatively, or in addition, the reference information may be one or more desired characteristics (e.g., from antenna design specifications/requirements). Other examples of characteristics may include location and/or gain of the main lobe, back lobe, side lobes, and/or nulls; half-power beamwidth; For instance, the score may be defined such that a score of a lower value is desired, such as when a lower value represents a difference between the desired antenna pattern and the determined antenna pattern being small. In another aspect, the blocks 910, 915, 920, 925, 930, and/or 940 may be repeated until the score is above the threshold value or until a threshold number of iterations is exceeded (e.g., signifying that the design of the antenna system is not converging to the desired antenna pattern. See para 70- The processing circuit 805 may be configured to generate TX and RX antenna parameters to be utilized to physically construct and/or configure a TX antenna array and an RX antenna array of an antenna system)
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 Lamontagne to include using a number of physical phaser shifters that are active and optimize the positions for a physical layout during manufacturing of the antenna array using a gradient operation according to the side-lobe power with the teachings of Lei. The motivation to combine would have been that such a method allows making adjustments to the parameters such as gain, phase shift, and/or position associated with the antenna elements and to use the adjusted TX and RX antenna parameters to physically construct and/or configure a TX antenna array and an RX antenna array of an antenna system. (see Lamontagne [0036], [0070])
Regarding claim 4, Lei/Lamontagne 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 5, Lei/Lamontagne teaches
remove the one of the elements upon filling the placement area with the elements and violating a parameter; (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.”. see also para 73-76-At block 817, if there are no more random seed placements to generate, the method compares the cost functions of the respective local optimums corresponding to each of the random seed placements to find the global minimum cost function. At block 819, the method updates the array placement to the placement corresponding to the global minimum cost function. The final array placement represents the best array placement among all the candidate placements evaluated.”); 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.”).
according to a difference between a min-lobe power and the side-lobe power; (see Lei para 54 and fig 7- A main lobe centered at 0 degree is characterized by a 3-dB beamwidth 602. 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.)
Regarding claim 6, Lei/Lamontagne 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 7, Lei/Lamontagne/ teaches
wherein the criteria is a difference between a main-lobe power and the side-lobe power (Lei, see para 54 and fig 7- A main lobe centered at 0 degree is characterized by a 3-dB beamwidth 602. 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.)
Lei does not teach the gradient operation minimizes a penalty associated with the side-lobe power for the elements.
However, Lamontagne further teaches the gradient operation minimizes a penalty associated with the side-lobe power for the elements ((see para 27- any parameters that may affect the construction and/or application of the antenna array and, thus, the radiation pattern exhibited by the antenna array. see para 85-91-The TX and/or RX antenna parameters may be adjusted based on optimization methods including, by way of non-limiting example, steepest descent, conjugate gradients, simulated annealing, genetic algorithm, and/or other optimization methods such that the antenna pattern (and/or associated characteristic(s)) effectuated by the TX and RX antenna parameters converges toward the desired antenna pattern (and/or associated characteristic(s)). At block 925, the device 800 determines a score based on the antenna system radiation pattern determined at block 920 and reference information. In an aspect, the reference information may be a reference radiation pattern, such as a desired antenna pattern set based on a design specification. In this aspect, the score may be indicative of (e.g., based on) a difference between the determined antenna pattern and the reference antenna pattern. Alternatively, or in addition, the reference information may be one or more desired characteristics (e.g., from antenna design specifications/requirements). Other examples of characteristics may include location and/or gain of the main lobe, back lobe, side lobes, and/or nulls; half-power beamwidth; For instance, the score may be defined such that a score of a lower value is desired, such as when a lower value represents a difference between the desired antenna pattern and the determined antenna pattern being small. In another aspect, the blocks 910, 915, 920, 925, 930, and/or 940 may be repeated until the score is above the threshold value or until a threshold number of iterations is exceeded (e.g., signifying that the design of the antenna system is not converging to the desired antenna pattern.
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 Lamontagne to include minimizing the penalty associated with the side-lobe power for the elements with the teachings of Lei. The motivation to combine would have been that such a method allows making adjustments to the parameters such as gain, phase shift, and/or position associated with the antenna elements and to use the adjusted TX and RX antenna parameters to physically construct and/or configure a TX antenna array and an RX antenna array of an antenna system. (see [0036], [0070])
Claims 16 and 18 are substantially similar to claims 5 and 7. Therefore, they are rejected for the similar reasons.
Regarding claim 9, Lei/Lamontagne 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, Lei 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).”).
The rest of the claim 10 is substantially similar to claim 1. Therefore, the similar analysis is applicable in view of Lei and Lamontagne.
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 Lamontagne, in further view of Cui et al. (CN111551923A), hereinafter Cui.
Regarding claim 2, Lei/Lamontagne 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 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 and identify a candidate geometry of the antenna array with a subset of the elements being active by sampling a distribution of the positions using the Monte Carlo method.
In the related field of invention, Lamontagne teaches a number of the physical phase shifters that are active. (See fig 3 (element 405) and fig 4 (element 605) and [0051]- For example the antenna parameters may include the antenna coefficients that indicate phase shift and/or gain to be applied to the antenna elements of the TX antenna array 225 and RX antenna array 235. In cases in which a position of at least some of the antenna elements may be movable, the antenna parameters may include a position to place the antenna elements. In some cases, the control information may turn on or off an antenna element, such that the antenna element is not used for transmission and/or reception. See [0054]- each antenna element may be associated with a phase shifter. See also [0063-0065]-A phase shifter 405 and an amplifier 410 (e.g., a power amplifier) may be part of a TX distribution circuit. The phase shifter 405 may receive a signal (e.g., a signal converted to radio frequency) and apply phase shift to the signal. A phase shifter 605 and an amplifier 610 (e.g., a low noise amplifier) may be part of an RX distribution circuit.)
The combination of Lei/Lamontagne does not teach Monte Carlo method.
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/Lamontagne 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/Lamontagne/Cui teaches
wherein the instructions to compute the positions further include instructions to move, using a Monte Carlo method, the positions randomly until the number of the physical 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”) ( See Lamontagne fig 3 (element 405) and fig 4 (element 605) and [0051]- For example the antenna parameters may include the antenna coefficients that indicate phase shift and/or gain to be applied to the antenna elements of the TX antenna array 225 and RX antenna array 235. In cases in which a position of at least some of the antenna elements may be movable, the antenna parameters may include a position to place the antenna elements. In some cases, the control information may turn on or off an antenna element, such that the antenna element is not used for transmission and/or reception. See [0054]- each antenna element may be associated with a phase shifter. See also [0063-0065]-A phase shifter 405 and an amplifier 410 (e.g., a power amplifier) may be part of a TX distribution circuit. The phase shifter 405 may receive a signal (e.g., a signal converted to radio frequency) and apply phase shift to the signal. A phase shifter 605 and an amplifier 610 (e.g., a low noise amplifier) may be part of an RX distribution circuit.)
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”)
and identify a candidate geometry of the antenna array with a subset of the elements being active by sampling a distribution of the positions using the Monte Carlo method. (Lei, [0017-0018]: “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 further includes generating multiple candidate placements of the antenna elements from each of the multiple random seed placements.”) (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”) ( See Lamontagne fig 3 (element 405) and fig 4 (element 605) and [0051]- For example the antenna parameters may include the antenna coefficients that indicate phase shift and/or gain to be applied to the antenna elements of the TX antenna array 225 and RX antenna array 235. In cases in which a position of at least some of the antenna elements may be movable, the antenna parameters may include a position to place the antenna elements. In some cases, the control information may turn on or off an antenna element, such that the antenna element is not used for transmission and/or reception. See [0054]- each antenna element may be associated with a phase shifter. See also [0063-0065]-A phase shifter 405 and an amplifier 410 (e.g., a power amplifier) may be part of a TX distribution circuit. The phase shifter 405 may receive a signal (e.g., a signal converted to radio frequency) and apply phase shift to the signal. A phase shifter 605 and an amplifier 610 (e.g., a low noise amplifier) may be part of an RX distribution circuit.)
Regarding claim 3, Lei/Lamontagne/Cui teaches
reduce, using the Monte Carlo method, a number of the elements and the number of the physical 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, [0052-0054]: “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. A main lobe centered at 0 degree is characterized by a 3-dB beamwidth 602. 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.”) (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.”) (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”)(See Lamontagne fig 3 (element 405) and fig 4 (element 605) and [0051]- For example the antenna parameters may include the antenna coefficients that indicate phase shift and/or gain to be applied to the antenna elements of the TX antenna array 225 and RX antenna array 235. In cases in which a position of at least some of the antenna elements may be movable, the antenna parameters may include a position to place the antenna elements. In some cases, the control information may turn on or off an antenna element, such that the antenna element is not used for transmission and/or reception. See Lamontagne [0054]- each antenna element may be associated with a phase shifter. See also [0063-0065]-A phase shifter 405 and an amplifier 410 (e.g., a power amplifier) may be part of a TX distribution circuit. The phase shifter 405 may receive a signal (e.g., a signal converted to radio frequency) and apply phase shift to the signal. A phase shifter 605 and an amplifier 610 (e.g., a low noise amplifier) may be part of an RX distribution circuit. See also Lamontagne [0081-0082]- In some cases, one or more antenna elements of the TX antenna array 225 and/or RX antenna array 235 may be turned off, e.g. not utilized for transmission and/or reception. In an aspect, if a peak antenna gain is needed at an orientation other than broadside, an initial phase may be set to direct the peak antenna gain at the desired orientation)
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) 8 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lei in view of Lamontagne in further view of Zolesio et al. (WO2007147768A1), hereinafter Zolesio.
Regarding claim 8,
Lei further teaches modify the positions of the elements and maintain an element position of the positions ( see para 51- 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. see para 63- n this embodiment, any of the elements may be moved to grids in its neighboring region to search for candidate placements, in contrast to the conventional array of FIG. 5 where the placement of the two outermost Rx elements remain fixed through the search.)
Lei does not explicitly teach modify the positions of the elements furthest from an origin of the placement area and maintain an element position of the positions within a pair proximate to the origin 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, Lamontagne further teaches modify the positions of the elements furthest from an origin of the placement area and maintain an element position of the positions within a pair proximate to the origin; (see para 67-- In this regard, in FIG. 7A, the antenna elements 315A-E are arranged along the X-axis, with adjacent antenna elements being 0.752 away from each other. An origin (e.g., a reference point) of the X-axis is set at the position of the antenna element 315C. In FIG. 7B, a ray r provides a distance of a point (Y.sub.1, X.sub.1) from the origin (set at the antenna element 315C) and an angle θ provides an angular rotation from the ray r to the Y-axis. See para 51- In cases in which a position of at least some of the antenna elements may be movable, the antenna parameters may include a position to place the antenna elements. See para 91- For instance, the gain and/or phase shift of the antenna elements may be adjusted at block 940, whereas the number of antenna elements, position of the antenna elements, material properties of the antenna elements may be considered to be fixed (e.g., and not adjusted at block 940). )
Examiner note: Element 315E and 315A are the elements further from an origin and Element 315B and 315D proximate to the origin.
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 Lamontagne to include modify the positions of the elements furthest from an origin of the placement area and maintain an element position of the positions within a pair proximate to the origin with the teachings of Lei. The motivation to combine would have been that such a method allows making adjustments to the parameters such as gain, phase shift, and/or position associated with the antenna elements and to use the adjusted TX and RX antenna parameters to physically construct and/or configure a TX antenna array and an RX antenna array of an antenna system. (see Lamontagne [0036], [0070])
Lei/Lamontagne 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/Lamontagne 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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PURSOTTAM GIRI/Examiner, Art Unit 2186
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186
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