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 .
DETAILED ACTION
2. This Office Action responds to the Application filed on 12/21/2023.
Claims 1-20 are pending.
Claim Rejections - 35 USC § 102
3. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
4. Claim(s) 1, 14, and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Koziel (“Global EM-driven optimization of multi-band antennas using knowledge-based inverse response-feature surrogates”, by Slawomir Koziel and Anna Pietrenko-Dabrowska, Elsevier B.V @2021).
As per claim 1, Koziel discloses:
A computer-implemented method comprising: receiving:
a set of design specifications associated with an antenna performance characteristic (See Page 2, Section 2.1, i.e. input characteristics of multi-band antennas…target frequencies, See Page 7, i.e. input arguments…target frequencies…objective function);
a set of requirements for an antenna architecture (See Page 7. Section 3. Case studies, i.e. microstrip antennas, a dual-band dipole, and two triple-band structures , See Page 8, i.e. dual-band uniplanar dipole antenna…RO3450 substrate….h=.762mm –[prior art design antenna with specification of substrate and type of antenna considered as the architecture]);
a parameterization that describes parameters of an antenna structure (See Page 7. Section 3. Case studies, i.e. independent geometry parameters…l1…w1…w3 –[prior art specify the geometry parameter of the antenna considered as the parameter of the antenna structure]); and
a set of bounds for the parameterization (See Table 1, i.e. parameter space …. Lower bounds and upper bounds); and
determining, based on the set of design specifications, the set of requirements, the parameterization, and the set of bounds, and in accordance with a global optimization algorithm and a local tuning algorithm, a design for the antenna architecture (See Section 2. Global optimization of multi-band antennas, See Pages 7, i.e. produce initial design x by performing global search … obtain final design x through local tuning, See Pages 2-7, See Figure 4, i.e. local tuning, See Pages 8-10 with section 4. Conclusion, i.e. the knowledge-based inverse surrogate employed in
the decision-making process yields a high-quality initial design that only needs to be locally tuned to render optimal design satisfying the assumed design specifications).
As per claim 14, Koziel discloses:
A system comprising:
a receiving module, stored in memory, that receives (See Page 2, i.e. CPU cost –[prior art directed to reduce of CPU cost, therefore directed to algorithm that operate on computer with memory]):
a set of design specifications associated with an antenna performance characteristic (See Page 2, Section 2.1, i.e. input characteristics of multi-band antennas…target frequencies, See Page 7, i.e. input arguments…target frequencies…objective function);
a set of requirements for an antenna architecture (See Page 7. Section 3. Case studies, i.e. microstrip antennas, a dual-band dipole, and two triple-band structures , See Page 8, i.e. dual-band uniplanar dipole antenna…RO3450 substrate….h=.762mm –[prior art design antenna with specification of substrate and type of antenna considered as the architecture]);
a parameterization that describes parameters of an antenna structure (See Page 7. Section 3. Case studies, i.e. independent geometry parameters…l1…w1…w3 –[prior art specify the geometry parameter of the antenna considered as the parameter of the antenna structure]); and
a set of bounds for the parameterization (See Table 1, i.e. parameter space …. Lower bounds and upper bounds); and
a determining module, stored in memory, that determines, based on the set of design specifications, the set of requirements, the parameterization, and the set of bounds, and in accordance with a global optimization algorithm and a local tuning algorithm, a design for the antenna architecture; and at least one physical processor that executes the receiving module and the determining module (See Section 2. Global optimization of multi-band antennas, See Pages 7, i.e. produce initial design x by performing global search … obtain final design x through local tuning, See Pages 2-7, See Figure 4, i.e. local tuning, See Pages 8-10 with section 4. Conclusion, i.e. the knowledge-based inverse surrogate employed in
the decision-making process yields a high-quality initial design that only needs to be locally tuned to render optimal design satisfying the assumed design specifications).
As per claim 19, Koziel discloses:
A non-transitory computer-readable medium comprising one or more computer-executable instructions that, when executed by at least one processor of a computing device, cause the computing device to (See Page 2, i.e. CPU cost –[prior art directed to reduce of CPU cost, therefore directed to algorithm that operate on computer with memory]):
receive: a set of design specifications associated with an antenna performance characteristic (See Page 2, Section 2.1, i.e. input characteristics of multi-band antennas…target frequencies, See Page 7, i.e. input arguments…target frequencies…objective function);
a set of requirements for an antenna architecture (See Page 7. Section 3. Case studies, i.e. microstrip antennas, a dual-band dipole, and two triple-band structures , See Page 8, i.e. dual-band uniplanar dipole antenna…RO3450 substrate….h=.762mm –[prior art design antenna with specification of substrate and type of antenna considered as the architecture]);
a parameterization that describes parameters of an antenna structure (See Page 7. Section 3. Case studies, i.e. independent geometry parameters…l1…w1…w3 –[prior art specify the geometry parameter of the antenna considered as the parameter of the antenna structure]); and
a set of bounds for the parameterization (See Table 1, i.e. parameter space …. Lower bounds and upper bounds); and
determine, based on the set of design specifications, the set of requirements, the parameterization, and the set of bounds, and in accordance with a global optimization algorithm and a local tuning algorithm, a design for the antenna architecture (See Section 2. Global optimization of multi-band antennas, See Pages 7, i.e. produce initial design x by performing global search … obtain final design x through local tuning, See Pages 2-7, See Figure 4, i.e. local tuning, See Pages 8-10 with section 4. Conclusion, i.e. the knowledge-based inverse surrogate employed in
the decision-making process yields a high-quality initial design that only needs to be locally tuned to render optimal design satisfying the assumed design specifications).
Claim Rejections - 35 USC § 103
5. 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.
6. Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koziel (“Global EM-driven optimization of multi-band antennas using knowledge-based inverse response-feature surrogates”, by Slawomir Koziel and Anna Pietrenko-Dabrowska, Elsevier B.V @2021 ) in view of Chen (U.S. Pub. No. 2022/0100935 A1).
As per claim 2, Koziel discloses all of the features of claim 1 as discloses above.
Koziel does not teach: wherein the method operates unsupervised without human interaction.
However, Chen teach: wherein the method operates unsupervised without human interaction (See Para [0013], i.e. performed using unsupervised machine learning, See Para [0098]-[0099], i.e. performed using unsupervised machine learning).
Therefore, it would have been obvious to a person of ordinary skill in the
art at the effective filing date of the invention to incorporate the teaching of Chen into
the teaching of Koziel because it would allow for reduction of time in pattern selection in
a circuit design system (See Para [0093]).
7. Claim(s) 3-13, 15-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koziel (“Global EM-driven optimization of multi-band antennas using knowledge-based inverse response-feature surrogates”, by Slawomir Koziel and Anna Pietrenko-Dabrowska, Elsevier B.V @2021 ) in view of Brown et al. (U.S. Pub. No. 2012/0065946 A1).
As per claim 3, Koziel discloses all of the features of clam 1 as discloses above wherein Kozielalso discloses wherein determining the design for the antenna architecture comprises: automatically generating a plurality of antenna geometries based on the design specifications using an algorithm within a scalable antenna parameterization framework; selecting an initial antenna geometry from the plurality of generated geometries; refining the selected antenna geometry by applying a gradient-based optimization procedure to fine-tune antenna dimensions; and outputting the refined antenna geometry (See Section 2. Global optimization of multi-band antennas, See Pages 7, i.e. produce initial design x by performing global search … obtain final design x through local tuning, See Pages 2-7, See Figure 4, i.e. local tuning, See Pages 8-10 with section 4. Conclusion, i.e. the knowledge-based inverse surrogate employed in the decision-making process yields a high-quality initial design that only needs to be locally tuned to render optimal design satisfying the assumed design specifications).
Koziel does not teach that the algorithm that automatically generate antenna geometries is a nature-inspired algorithm.
However Brown teach automatically generating a plurality of antenna geometries based on the design specifications using a nature-inspired algorithm (See Para [0112]-[0113], i.e. a genetic algorithm can be used to optimize antenna designs that meet the set of optimization goals).
Therefore, it would have been obvious to a person of ordinary skill in the
art at the effective filing date of the invention to incorporate the teaching of Brown into
the teaching of Koziel because it would enables users who are not antenna
engineers, and who have no experience regarding antenna design, to find antenna
designs that meet a set of conditions or user requirements (See Para [0003]).
As per claim 4, Koziel and Brown discloses all of the features of claim 3 as discloses above wherein Brown also discloses wherein automatically generating the plurality of antenna geometries comprises: initializing a population of antenna designs within the scalable antenna parameterization framework based on the set of design specifications; executing an iterative process for evolving the antenna designs using the nature-inspired algorithm, wherein the iterative process comprises: evaluating the performance of each antenna design against the set of design specifications; selecting a superior antenna design based on a performance evaluation; generating a new antenna designs by applying variation operators within the bounds of the parameterization; and repeating the iterative process until a predetermined criterion is met, resulting in a diverse set of optimized antenna geometries for selection (See Para [0112]-[0113], i.e. a genetic algorithm can be used to optimize antenna designs that meet the set of optimization goals).
As per claim 5, Koziel and Brown discloses all of the features of claim 3 as discloses above wherein Koziel also discloses adjusting the scalable antenna parameterization framework to accommodate multiple levels of structural complexity in antenna designs (See Page 1, i.e. Adequate evaluation of geometrically complex antennas requires full-wave electromagnetic (EM) analysis –See Pages 7-10 - [prior art address complexity in antenna design, considered as the accommodate of the multiple level of structural complexity]).
As per claim 6, Koziel and Brown discloses all of the features of claim 3 as discloses above wherein Koziel also discloses wherein the scalable antenna parameterization framework comprises: defining a set of geometric parameters for the antenna structure, the set of geometric parameters comprising dimensions, shapes, and configurations of antenna elements; establishing a range for each geometric parameter within the set of bounds for the parameterization; and dynamically adjusting the set of geometric parameters (See Page 1, i.e. Adequate evaluation of geometrically complex antennas requires full-wave electromagnetic (EM) analysis –See Pages 7-10 & Figure 5 & Table 1).
As per claim 7, Koziel and Brown discloses all of the features of claim 6 as discloses above wherein Koziel also discloses Incorporating into the scalable antenna parameterization framework both continuous and discrete parameters for the antenna structure (See Pages 7-10 & Figure 5 & Table 1).
As per claim 8, Koziel and Brown discloses all of the features of claim 3 as discloses above wherein Brown also discloses wherein the nature-inspired algorithm comprises an algorithm that models natural processes to optimize antenna geometries, comprising one or more of: a genetic algorithm; a particle swarm optimization; or an ant colony optimization (See Para [0112]-[0113], i.e. a genetic algorithm can be used to optimize antenna designs that meet the set of optimization goals).
As per claim 9, Koziel and Brown discloses all of the features of claim 8 as discloses above wherein Brown also discloses implementing at least one algorithmic enhancement to the nature-inspired algorithm, the at least one algorithmic enhancement comprising at least one of: adaptive mutation rates in genetic algorithms; targeted selection criteria based on desired antenna performance characteristics; or dynamic population management strategies (See Para [0112]-[0113], i.e. a genetic algorithm can be used to optimize antenna designs that meet the set of optimization goals).
As per claim 10, Koziel and Brown discloses all of the features of claim 3 as discloses above wherein Koziel also discloses employing a multi-objective optimization approach within the nature-inspired algorithm, wherein the multi-objective optimization simultaneously targets multiple antenna performance characteristics (See Page 2, i.e. multi-objective design procedures).
As per claim 11, Koziel and Brown discloses all of the features of claim 10 as discloses above wherein Koziel also discloses wherein the multiple antenna performance characteristics comprise gain; bandwidth; and radiation pattern (See Page 3, i.e. maximize the fractional bandwidth around the target frequencies, See Page 7).
As per claim 12, Koziel and Brown discloses all of the features of claim 3 as discloses above wherein Koziel also discloses wherein determining the design for the antenna architecture further comprises adapting the design for the antenna based on physical and/or electrical properties unique to an antenna type (See Section 2. Global optimization of multi-band antennas, See Pages 7, i.e. produce initial design x by performing global search … obtain final design x through local tuning, See Pages 2-7, See Figure 4, i.e. local tuning, See Pages 8-10 with section 4. Conclusion, i.e. the knowledge-based inverse surrogate employed in the decision-making process yields a high-quality initial design that only needs to be locally tuned to render optimal design satisfying the assumed design specifications).
As per claim 13, Koziel and Brown discloses all of the features of claim 12 as discloses above wherein Koziel also discloses wherein the antenna type comprises a single-layer microstrip antenna (See Page 7, i.e. microstrip antennas).
As per claim 15, Koziel discloses all of the features of clam 14 as discloses above wherein Koziel also discloses wherein the determining module determines the design for the antenna architecture by: automatically generating a plurality of antenna geometries based on the design specifications using a algorithm within a scalable antenna parameterization framework; selecting an initial antenna geometry from the plurality of generated geometries; refining the selected antenna geometry by applying a gradient-based optimization procedure to fine-tune antenna dimensions; and outputting the refined antenna geometry.(See Section 2. Global optimization of multi-band antennas, See Pages 7, i.e. produce initial design x by performing global search … obtain final design x through local tuning, See Pages 2-7, See Figure 4, i.e. local tuning, See Pages 8-10 with section 4. Conclusion, i.e. the knowledge-based inverse surrogate employed in the decision-making process yields a high-quality initial design that only needs to be locally tuned to render optimal design satisfying the assumed design specifications).
Koziel does not teach that the algorithm that automatically generate antenna geometries is a nature-inspired algorithm.
However Brown teach automatically generating a plurality of antenna geometries based on the design specifications using a nature-inspired algorithm (See Para [0112]-[0113], i.e. a genetic algorithm can be used to optimize antenna designs that meet the set of optimization goals).
Therefore, it would have been obvious to a person of ordinary skill in the
art at the effective filing date of the invention to incorporate the teaching of Brown into
the teaching of Koziel because it would enables users who are not antenna
engineers, and who have no experience regarding antenna design, to find antenna
designs that meet a set of conditions or user requirements (See Para [0003]).
As per claim 16, Koziel and Brown discloses all of the features of claim 15 as discloses above wherein Brown also discloses wherein the determining module automatically generates the plurality of antenna geometries by: initializing a population of antenna designs within the scalable antenna parameterization framework based on the set of design specifications; executing an iterative process for evolving the antenna designs using the nature-inspired algorithm, wherein the iterative process comprises: evaluating the performance of each antenna design against the set of design specifications; selecting a superior antenna design based on a performance evaluation; generating a new antenna designs by applying variation operators within the bounds of the parameterization; and repeating the iterative process until a predetermined criterion is met, resulting in a diverse set of optimized antenna geometries for selection (See Para [0112]-[0113], i.e. a genetic algorithm can be used to optimize antenna designs that meet the set of optimization goals).
As per claim 17, Koziel and Brown discloses all of the features of claim 15 as discloses above wherein Koziel also discloses wherein the determining module automatically generates the plurality of antenna geometries based on the design specifications using the nature-inspired algorithm within the scalable antenna parameterization framework by: defining a set of geometric parameters for the antenna structure, the set of geometric parameters comprising dimensions, shapes, and configurations of antenna elements; establishing a range for each geometric parameter within the set of bounds for the parameterization; and dynamically adjusting the set of geometric parameters (See Page 1, i.e. Adequate evaluation of geometrically complex antennas requires full-wave electromagnetic (EM) analysis –See Pages 7-10 & Figure 5 & Table 1).
As per claim 18, Koziel and Brown discloses all of the features of claim 15 as discloses above wherein Koziel also discloses wherein the determining module employs a multi-objective optimization approach within the nature-inspired algorithm, wherein the multi-objective optimization simultaneously targets multiple antenna performance characteristics (See Page 2, i.e. multi-objective design procedures).
As per claim 20, Koziel discloses all of the features of clam 19 as discloses above wherein Koziel also discloses wherein the computer-executable instructions, when executed by the at least one processor of the computing device cause the computing device to determine the design for the antenna architecture by: automatically generating a plurality of antenna geometries based on the design specifications using a algorithm within a scalable antenna parameterization framework; selecting an initial antenna geometry from the plurality of generated geometries; refining the selected antenna geometry by applying a gradient-based optimization procedure to fine-tune antenna dimensions; and outputting the refined antenna geometry. (See Section 2. Global optimization of multi-band antennas, See Pages 7, i.e. produce initial design x by performing global search … obtain final design x through local tuning, See Pages 2-7, See Figure 4, i.e. local tuning, See Pages 8-10 with section 4. Conclusion, i.e. the knowledge-based inverse surrogate employed in the decision-making process yields a high-quality initial design that only needs to be locally tuned to render optimal design satisfying the assumed design specifications).
Koziel does not teach that the algorithm that automatically generate antenna geometries is a nature-inspired algorithm.
However Brown teach automatically generating a plurality of antenna geometries based on the design specifications using a nature-inspired algorithm (See Para [0112]-[0113], i.e. a genetic algorithm can be used to optimize antenna designs that meet the set of optimization goals).
Therefore, it would have been obvious to a person of ordinary skill in the
art at the effective filing date of the invention to incorporate the teaching of Brown into
the teaching of Koziel because it would enables users who are not antenna
engineers, and who have no experience regarding antenna design, to find antenna
designs that meet a set of conditions or user requirements (See Para [0003]).
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHA T NGUYEN whose telephone number is (571)270-1405. The examiner can normally be reached M-F 8:00AM-5:00PM.
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/NHA T NGUYEN/Primary Examiner, Art Unit 2851
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