DETAILED ACTION
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 .
Response to Amendment
Received 01/21/2026
Claim(s) 1-22 is/are pending.
Claim(s) 1, 3-6, 11, and 18-21 has/have been amended.
Claim(s) 22 has/have been added.
The objections to the Specification have been withdrawn in view of the amendments received on 01/21/2026.
The 35 U.S.C § 103 rejection to claim(s) 1-22 have been fully considered in view of the amendments received on 01/21/2026 and are fully addressed in the prior art rejection below.
Response to Arguments
Received 01/21/2026
Regarding independent claim(s) 1 and 11:
Applicant’s arguments (Remarks, Page 9: ¶ 2 to Page 10: ¶ 3), filed 01/21/2026, with respect to the rejection(s) of claim(s) 1 under 35 U.S.C § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn, necessitated by Applicant's amendments. However, upon further consideration, a new ground(s) of rejection is made in view of Yang et al. (US PGPUB No. 20190150006 A1), in view of Nishikawa (US PGPUB No. 20210329478 A1), in view of Christensen et al. (NPL “Ray Differentials and Multiresolution Geometry Caching for Distribution Ray Tracing in Complex Scenes”), in view of Chatterjee (NPL “Convergence of Gradient Descent For Deep Neural Networks”), and further in view of Fiterman (US PGPUB No. 20220076061 A1).
Applicant’s arguments (Remarks, Page 15: ¶ 2), filed 01/21/2026, with respect to the rejection(s) of claim(s) 11 under 35 U.S.C § 103 have been fully considered and are persuasive due claim 11's similarity to claim 1. Therefore, the rejection has been withdrawn, necessitated by Applicant's amendments. However, upon further consideration, a new ground(s) of rejection is made in view of the prior art as mentioned above.
Regarding dependent claim(s) 11:
Applicant’s arguments (Remarks, Page 15: ¶ 2), filed 01/21/2026, with respect to the rejection(s) of claim(s) 11 under 35 U.S.C § 103 have been fully considered and are persuasive due the dependency upon claim(s) 1 respectively. Therefore, the rejection has been withdrawn, necessitated by Applicant's amendments. However, upon further consideration, a new ground(s) of rejection is made in view of the prior art as mentioned above.
Applicant's arguments filed 01/21/2026 have been fully considered but they are not persuasive; as expressed below.
Applicant argues (Remarks, Page 11, ¶ 1-2), that “The ray differentials disclosed by Christiansen are geometric footprint derivatives for texture filtering in graphics. While texture filtering is important for ray tracing 3D scenes to produce high quality images using texture mapping to determine pixel colors, texture filtering is irrelevant for simulating wireless signal transmissions. Instead, ray hits, including reflections resulting from a ray intersecting 3D geometry is essential for accurate simulation of wireless signal transmissions. See Yang at paragraph [0051]: …
Christensen is clear that the ray differentials do not help for determining visibility, meaning whether or not an object in the scene is intersected (and reflected) by the ray. Accurate simulation of wireless signals depends on accurate simulation of intersections and reflections of the wireless signals and the scene geometry. Therefore, Christensen's ray differentials offer no benefit at all when used for simulating wireless signals which is the stated purpose of Yang's method for predicting received signal strength in a telecommunication network.”
The Examiner disagrees. Applicant’s arguments fail to view the combination as a whole, wherein Applicant’s focus on the visibility of the ray is only an aspect of the teaches of Christensen et al. (NPL “Ray Differentials and Multiresolution Geometry Caching for Distribution Ray Tracing in Complex Scenes”). Moreover, Applicant argument fails to view “…
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method traces single rays, but keeps track of the difference between each ray and two (real or imaginary) ‘neighbor’ rays. These differences give an indication of the cone/beam size that each ray represent. The curvature at surface intersection points determines how those ray differentials are propagated at specular reflections and refractions” (Sec. 2.5: Ray Differentials).
Still further, Applicant fails to view radio waves are EM waves that further correspond to light, thus rays/light and simulated light properties are relevant to signal functionally.
Therefore, Applicant’s arguments are not persuasive.
Applicant argues (Remarks, Page 11, ¶ 3 to Page 13, ¶ 2), that “Like Yang, Nishikawa also performs signal-strength estimation based on geospatial data and a theoretical propagation model, but it is not a differentiable ray tracer. The Examiner has improperly combined the references without providing a teaching that any reference performs ‘computing, by a differentiable ray tracer, simulated radio characteristics’ as claimed. Therefore, the combination of Yang, Nishikawa, and Christensen fails to teach or suggest computing by a differentiable ray tracer simulated radio characteristics for a 3D scene based on the configured and trainable parameters, as claimed. Chatterjee discusses convergence of gradient descent for neural network weights and is silent regarding any differentiable ray tracer. The other cited references, Nanni, Igehy, and Subtil fail to cure these deficiencies of Yang, Nishikawa, Christensen or Chatterjee. For these additional reasons, a notice of allowance for the amended independent claims, as well as all claims dependent thereon, or a specific prior art showing of each of the foregoing claim elements, in combination with the remaining claimed features, is respectfully requested.
Furthermore, no combination of the cited references teaches or suggest the limitations recited in each independent claim that a differentiable ray tracer computes simulated radio characteristics that are used, along with reference radio characteristics, to minimize a loss function. The claim elements ‘simulated radio characteristics’ and ‘reference radio characteristics’ are separate and distinct components. The Examiner maps the claimed simulated radio characteristics to Yang's simulated received signal strength (RSS). The Examiner maps the claimed reference radio characteristics to Yang's ground truth simulated RSS. Yang is clear that the ground truth simulated RSS is computed, not by Yang's DNN (ConvNet), but by a separate simulator (e.g., ray tracing based VOLCANO). Yang's DNN (ConvNet) does not perform ray tracing. See Yang at Figures 3 and 4. None of the parameters used by VOLCANO are trainable and the VOLCANO ray tracer is not a differentiable ray tracer. See Yang at paragraphs [0074] and [0075]: …
Yang is clear that weights of ConvNet-Volcano are trained while the Volcano Simulator 305 is not trained and instead provides the supervisory training data. While Yang does describe that ConvNet is differentiable, Yang fails to teach or suggest that VOLCANO is differentiable. See Yang at paragraph [0056]: …
Instead of disclosing a differentiable ray tracer that computes simulated radio characteristics, Yang discloses that a ray tracer (VOLCANO) computes ground truth simulated RSS which are equated with the claimed reference radio characteristics. Yang's simulated RSS are not computed by any ray tracer and is equated with the claimed simulated radio characteristics that are required, but the plain language of the claims, to be computed by a differentiable ray tracer. For these reasons, Applicant respectfully submits that the cited combination fails to teach or suggest the claimed differentiable ray tracer and gradient-based optimization using simulated radio characteristics and reference radio characteristics, and the rejections under 35 U.S.C. § 103 should be withdrawn.”
The Examiner disagrees. Wherein applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, simulated radio characteristics (as taught by Yang et al. (US PGPUB No. 20190150006 A1)) are able to be manipulated by mathematical properties as taught by Nishikawa (US PGPUB No. 20210329478 A1), Christensen et al., or Chatterjee (NPL “Convergence of Gradient Descent For Deep Neural Networks”).
Applicant’s arguments regarding the convNet-Volcano and VOLCANO fails to address the teachings of Christensen et al. regarding differentiable ray tracer within the rejection.
Therefore, Applicant’s arguments are not persuasive.
Applicant argues (Remarks, Page 13, ¶ 4 to Page 14, ¶ 1), that “Additionally, Christensen and Igehy teach computer graphics techniques (texture antialiasing; ray footprints; visual realism) not radio wave propagation modeling, and they are not reasonably pertinent to the problem of learning radio electromagnetic material parameters using gradient-based optimization. Christensen (graphics distribution ray tracing) addresses anti-aliasing and geometry caching for image synthesis, not radio wave propagation or gradient-based optimization. The Examiner's rationale (better memory utilization for graphics) is not reasonably pertinent to the problem addressed, namely learning electromagnetic material parameters by differentiable radio wave path computation and loss minimization against reference radio characteristics. See In re Bigio, 381 F.3d 1320 (Fed. Cir. 2004) and In re Clay, 966 F.2d 656 (Fed. Cir. 1992), and MPEP 2143. For these reasons, Applicant respectfully submits that the rejections under 35 U.S.C. § 103 should be withdrawn.”
The Examiner disagrees. Wherein Applicant fails to view the rejection as a whole. Moreover, Applicant fails to view the manner in which Yang et al. is modified by known modeling techniques. Still further, Applicant's argument that Christensen et al. is nonanalogous art, it has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Applicant fails to view that ray tracing models utilize and benefit from memory optimization. Even further, Applicant's argument that Nishikawa, Christensen et al., and Chatterjee teach additionally features such as memory optilization, the fact that applicant has recognized another advantage which would flow naturally from following the suggestion of the prior art cannot be the basis for patentability when the differences would otherwise be obvious. See Ex parte Obiaya, 227 USPQ 58, 60 (Bd. Pat. App. & Inter. 1985).
Therefore, Applicant’s arguments are not persuasive.
Applicant argues (Remarks, Page 13, ¶ 4 to Page 14, ¶ 1), that “… Yang explicitly separates roles. During training, the ray tracer produces reference RSS, while ConvNet simulates RSS. Yang provides no suggestion to make the ray tracer itself differentiable or to use ray-tracer-generated simulated RSS in a gradient- based training loop of physical electromagnetic scene property parameters. Yang's teachings do not motivate nor suggest using ray tracing to predict the simulated RSS. Yang describes advantages of using a CNN structure for ConvNet compared with ray tracing. Yang asserts that ray tracing methods are less accurate and slower compared with ConvNet, See Yang at paragraphs [0063] and [0064]: …
Yang discourages using ray tracing to perform any operations of ConvNet. MPEP § 2143.03(VI) states that ‘[a] prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention.’ Accordingly, where cited art teaches away from a claimed feature, the cited art is not available for the purposes of an obviousness rejection. Applicant respectfully submits that, when considered in its entirety, Yang teaches away from the claimed invention. Because Yang teaches away from the claimed invention, one of ordinary skill in the art would not modify Yang to incorporate differentiable ray tracing in an effort to arrive at the claimed invention. Accordingly, Applicant respectfully submits that the rejection is improper and respectfully requests that the rejection be withdrawn.”
The Examiner disagrees. Wherein, Applicant's argument that the teachings of Yang and Moon are technologically incompatible, according to the MPEP 2123:
Disclosed examples and preferred embodiments do not constitute a teaching away from a broader disclosure or nonpreferred embodiments. In re Susi, 440 F.2d 442, 169 USPQ 423 (CCPA 1971). "A known or obvious composition does not become patentable simply because it has been described as somewhat inferior to some other product for the same use." In re Gurley, 27 F.3d 551, 554, 31 USPQ2d 1130, 1132 (Fed. Cir. 1994) (The invention was directed to an epoxy impregnated fiber-reinforced printed circuit material. The applied prior art reference taught a printed circuit material similar to that of the claims but impregnated with polyester-imide resin instead of epoxy. The reference, however, disclosed that epoxy was known for this use, but that epoxy impregnated circuit boards have "relatively acceptable dimensional stability" and "some degree of flexibility," but are inferior to circuit boards impregnated with polyester-imide resins. The court upheld the rejection concluding that applicant’s argument that the reference teaches away from using epoxy was insufficient to overcome the rejection since "Gurley asserted no discovery beyond what was known in the art." Id. at 554, 31 USPQ2d at 1132.). Furthermore, "[t]he prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed…." In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004).
Wherein, non-preferred and alternative embodiments constitute prior art, does not teach away.
And, according to MPEP 2131.05:
A reference is no less anticipatory if, after disclosing the invention, the reference then disparages it. The question whether a reference "teaches away" from the invention is inapplicable to an anticipation analysis. Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998) (The prior art was held to anticipate the claims even though it taught away from the claimed invention. "The fact that a modem with a single carrier data signal is shown to be less than optimal does not vitiate the fact that it is disclosed."). See Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005)(claimed composition that expressly excluded an ingredient held anticipated by reference composition that optionally included that same ingredient); see also Atlas Powder Co. v. IRECO, Inc., 190 F.3d 1342, 1349, 51 USPQ2d 1943, 1948 (Fed. Cir. 1999) (Claimed composition was anticipated by prior art reference that inherently met claim limitation of "sufficient aeration" even though reference taught away from air entrapment or purposeful aeration.).
Wherein, non-analogous or disparaging prior art, such that less than optimal results does not teach away.
Therefore, Applicant’s arguments are not persuasive.
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).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-9 , 11, 13-16, and 18-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al., US PGPUB No. 20190150006 A1, hereinafter Yang, in view of Nishikawa, US PGPUB No. 20210329478 A1, hereinafter Nishikawa, in view of Christensen et al., NPL “Ray Differentials and Multiresolution Geometry Caching for Distribution Ray Tracing in Complex Scenes”, hereinafter Christensen, in view of Chatterjee, NPL “Convergence of Gradient Descent For Deep Neural Networks”, hereinafter Chatterjee, and further in view of Fiterman, US PGPUB No. 20220076061 A1, hereinafter Fiterman.
Regarding claim 18, Yang discloses a system (Yang; a system [¶ 0123-0127], as illustrated within Fig. 7), comprising:
a memory that stores reference radio characteristics (Yang; the system [as addressed above] comprises a memory that stores reference radio characteristics [¶ 0131]; moreover, data items related with radio characteristics [¶ 0083-0086]); and
a processor that is connected to the memory (Yang; the system [as addressed above] comprises a processor that is connected to the memory [¶ 0130-0131], as illustrated within Fig. 7), wherein the processor is configured to produce simulated radio characteristics for a three-dimensional scene (Yang; the processor [as addressed above] is configured to produce simulated radio characteristics for an implicit 3D scene (given the scene is a geographic area) [¶ 0071-0072, ¶ 0074, and ¶ 0139-0141]; wherein, simulated received signal strength [¶ 0024-0026] is enabled using a NN/ML [¶ 0061] in relation with a geographic area implicitly corresponding to a 3D scene [¶ 0083, ¶ 0085-0087, and ¶ 0089]; moreover, automatic site planning (ASP) to predict signal coverage [¶ 0050-0051]; and moreover, training for predicting RSS [¶ 0075-0077]) by:
initializing configured parameters and at least one trainable parameter corresponding to a scene property (Yang; the processor [as addressed above] is configured to initializing configured parameters and at least one trainable parameter corresponding to a scene (i.e. geo area or AOI) property [¶ 0083-0086]; wherein, data/info is provided to a NN/ML to model signal-strength [¶ 0051 and ¶ 0106-0109]; such that, adjusting antenna configurations (e.g., the antenna locations, antenna beam directions, transmitting power, and in some cases antenna radiation pattern) or other parameters of a base station in the geographic area based on the predicted received signal strength [¶ 0110]; additionally, prediction of RSS [¶ 0071] using NN/ML model training [¶ 0074-0077]; i.e., DNN [¶ 0053-0054 and ¶ 0057-0058]);
computing, by a ray tracer, the simulated radio characteristics for the three-dimensional scene based on the configured parameters and the at least one trainable parameter (Yang; the processor [as addressed above] is configured to computing the simulated radio characteristics for the implicit 3D scene (given the scene is a geo area) based on the configured parameters and the at least one trainable parameter [as addressed above] by a ray tracer [¶ 0005 and ¶ 0074]; moreover, computing the simulated radio characteristics for the scene [¶ 0068-0069, ¶ 0071-0072, and ¶ 0074]; additionally, calculating/determining RSS / path loss [¶ 0002-0005 and ¶ 0050-0051], and ray tracing [as addressed above] implicitly associated with pixel(s) related computing is further corelated to a differentiable score function given DNN/ConvNet [¶ 0053-0056]); and
updating the at least one trainable parameter using gradient-based optimization to minimize a loss function of the simulated radio characteristics and the reference radio characteristics (Yang; the processor [as addressed above] is configured to updating the at least one trainable parameter using implicit gradient-based optimization (given DNN/ConvNet) [¶ 0051-0054] to minimize a loss function of the simulated radio characteristics and the reference radio characteristics [¶ 0075-0077]; wherein, adjusting antenna configurations (e.g., the antenna locations, antenna beam directions, transmitting power, and in some cases antenna radiation pattern) or other parameters of a base station in the geographic area based on the predicted received signal strength [¶ 0072 and ¶ 0110]; and wherein, DNN/ConvNet can be used to express a single differentiable score function (i.e. gradient-based optimization) [¶ 0054-0056] associated with parameters as well as trainable parameters [¶ 0057-0059]).
Yang fails to explicitly disclose radio characteristics for a three-dimensional scene;
an electromagnetic material scene property;
a differentiable ray tracer; and
using gradient-based optimization.
However, Nishikawa teaches the processor is configured to produce simulated radio characteristics for a three-dimensional scene (Nishikawa; the processor [¶ 0171-0172] is configured to produce simulated radio characteristics for a 3D scene [¶ 0081-0084 and ¶ 0166]; wherein, 3D scene corresponds to geospatial information [¶ 0081 and ¶ 0083]; moreover, position, size, shape, and material data [¶ 0102-0105] within a 3D coordinate system [¶ 0118]; additionally, RSS visualization [¶ 0169-0170] and radio wave propagation [¶ 0100, ¶ 0106, and ¶ 0108]).
Yang and Nishikawa are considered to be analogous art because both pertain to generating and/or managing data in relation with computational models, wherein one or more computerized units are utilized in order to produce a prediction/forecasting effect.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang, to incorporate the processor is configured to produce simulated radio characteristics for a three-dimensional scene (as taught by Nishikawa), in order to provide improved accuracy for estimating electromagnetic wave strength (Nishikawa; [¶ 0003-0007]).
Yang as modified by Nishikawa fails to explicitly disclose an electromagnetic material;
a differentiable ray tracer; and
using gradient-based optimization.
However, Christensen teaches computing, by a differentiable ray tracer, the simulated characteristics for the three-dimensional scene (Christensen; computing the simulated characteristics for the 3D scene by a differentiable ray tracer [Page 3, Sec. 2.5 and Page 4, Sec. 4.1], as illustrated within Fig. 1).
Yang in view of Nishikawa and Christensen are considered to be analogous art because they pertain to generating and/or managing data in relation with computational models, wherein one or more computerized units are utilized in order to produce a prediction/forecasting effect.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, to incorporate computing, by a differentiable ray tracer, the simulated characteristics for the three-dimensional scene (as taught by Christensen), in order to provide improved memory utilization for high computationally intensive graphics (Christensen; [Page 1, Sec. 1]).
Yang as modified by Nishikawa and Christensen fails to disclose an electromagnetic material; and
using gradient-based optimization.
However, Chatterjee teaches updating the at least one trainable parameter using gradient-based optimization to minimize a loss function (Chatterjee; updating the at least one trainable parameter using gradient-based optimization to minimize a loss function [Pages 1 to 2, Sec. 1]; moreover, DNN with feedforward corresponding to trainable parameters using gradient based optimization [Page 3 to Page 4, Sec. 2]).
Yang in view of Nishikawa, Christensen, and Chatterjee are considered to be analogous art because they pertain to generating and/or managing data in relation with computational models, wherein one or more computerized units are utilized in order to produce a prediction/forecasting effect.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa and Christensen, to incorporate updating the at least one trainable parameter using gradient-based optimization to minimize a loss function (as taught by Chatterjee), in order to provide optimization for large or dynamic learning models (Chatterjee; [Abstract and Page 1, Sec. 1]).
Yang as modified by Nishikawa, Christensen, and Chatterjee fails to disclose an electromagnetic material.
However, Fiterman teaches initializing configured parameters and at least one trainable parameter corresponding to an electromagnetic material scene property (Fiterman; initializing configured parameters and at least one trainable parameter corresponding to an electromagnetic material scene property [¶ 0006-0007 and ¶ 0010]; moreover, EM radiation simulator [¶ 0017, ¶ 0019, ¶ 0033, and ¶ 0035]).
Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman are considered to be analogous art because they pertain to generating and/or managing data in relation with computational models, wherein one or more computerized units are utilized in order to produce a data model.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, Christensen, and Chatterjee, to incorporate initializing configured parameters and at least one trainable parameter corresponding to an electromagnetic material scene property (as taught by Fiterman), in order to provide high performance and accurate artificial intelligence models utilizing generating large amounts of training data (Fiterman; [¶ 0002-0005]).
Regarding claim 19, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the system of claim 18, wherein the scene property comprises at least one of distance-dependent path loss, relative permittivity, conductivity, effective roughness, and permeability of object surfaces and scattering, and diffraction functions (Yang; the scene property [as addressed within the parent claim(s)] comprises at least one of distance-dependent path loss, relative permittivity, conductivity, effective roughness, and permeability of object surfaces and scattering, and diffraction functions [¶ 0083-0086]; moreover, RSS tensor(s) [¶ 0089-0090, ¶ 0092-0093, and ¶ 0098-0100]).
Fiterman further teaches the electromagnetic material scene property comprises at least one of distance-dependent path loss, relative permittivity, conductivity, effective roughness, and permeability of object surfaces and scattering, and diffraction functions (Fiterman; the electromagnetic material scene property comprises at least one of distance-dependent path loss, relative permittivity, conductivity, effective roughness, and permeability of object surfaces and scattering, and diffraction functions [¶ 0006-0007 and ¶ 0010-0011]; moreover, users may also specify parameters regarding radiation energy, energy binning, frequency, amplitude, wavelength, propagation, absorption, reflection, and refraction of electromagnetic waves and particles with various bodies in a scene [¶ 0019]; additionally, metadata attributes may include swatch class/type (e.g. fabric, food, metal), state (e.g. solid, liquid, gas), density, color, transparency, attenuation, refraction and reflection coefficients for a particular electromagnetic energy profile, temperature, text data/description, or other custom attributes defined by the user. [¶ 0028 and ¶ 0033-0035]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, Christensen, Chatterjee, and Fiterman, to incorporate the electromagnetic material scene property comprises at least one of distance-dependent path loss, relative permittivity, conductivity, effective roughness, and permeability of object surfaces and scattering, and diffraction functions (as taught by Fiterman), in order to provide high performance and accurate artificial intelligence models utilizing generating large amounts of training data (Fiterman; [¶ 0002-0005]).
Regarding claim 20, the rejection of claim 20 is addressed within the rejection of claim 18, due to the similarities claim 20 and claim 18 share, therefore refer to the rejection of claim 18 regarding the rejection of claim 20. Although, claim 1 and claim 18 may not be identical, they are considerably comparable or substantially equivalent given their overlapping subject matter. Thus, the subject matter/limitations not addressed by claim 18 is/are addressed below.
Yang discloses a non-transitory computer-readable media storing computer instructions that, when executed by one or more processors, cause the one or more processors to perform the steps (Yang; a non-transitory CRM storing computer instructions that cause the one or more processors to perform when executed by one or more processors [¶ 0166 and ¶ 0168-0172]; more, non-transitory CRM stored instructions to perform a computer implemented method [¶ 0008, ¶ 0021, and ¶ 0030]).
(further refer to the rejection of claim 18)
Regarding claim 21, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the non-transitory computer-readable media of claim 20, wherein the ray tracer computes paths of electromagnetic waves (Yang; the ray tracer (i.e. deterministic methods) computes paths of electromagnetic waves (i.e. RSS, propagated signal) [¶ 0050, ¶ 0061-0063, and ¶ 0074]; moreover, EM wave corresponding to signal propagation [¶ 0002, ¶ 0005, and ¶ 0090]).
Christensen further teaches the differentiable ray tracer computes paths (Christensen; differentiable ray tracer computes paths [Page 3, Sec. 2.5 and Page 4, Sec. 4.1], as illustrated within Fig. 1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, Christensen, Chatterjee, and Fiterman, to incorporate the differentiable ray tracer computes paths (as taught by Christensen), in order to provide improved memory utilization for high computationally intensive graphics (Christensen; [Page 1, Sec. 1]).
Regarding claim 22, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further disclose the computer-implemented method of claim 1, wherein the updating comprises computing a gradient with respect to the electromagnetic material scene property for the gradient-based optimization (Chatterjee; updating comprises computing a gradient with respect to the scene property for the gradient-based optimization [Pages 1 to 2, Sec. 1]; moreover, DNN with feedforward corresponding to trainable parameters using gradient based optimization [Page 3 to Page 4, Sec. 2]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, Christensen, Chatterjee, and Fiterman, to incorporate the updating comprises computing a gradient with respect to the electromagnetic material scene property for the gradient-based optimization (as taught by Chatterjee), in order to provide optimization for large or dynamic learning models (Chatterjee; [Abstract and Page 1, Sec. 1]).
Fiterman further teaches to the electromagnetic material scene property (Fiterman; electromagnetic material scene property [¶ 0006, ¶ 0017, and ¶ 0019]; moreover, neural network based processing [¶ 0033-0036]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, Christensen, Chatterjee, and Fiterman, to incorporate the electromagnetic material scene property (as taught by Fiterman), in order to provide high performance and accurate artificial intelligence models utilizing generating large amounts of training data (Fiterman; [¶ 0002-0005]).
Regarding claim 1, the rejection of claim 1 is addressed within the rejection of claim 18, due to the similarities claim 1 and claim 18 share, therefore refer to the rejection of claim 18 regarding the rejection of claim 1. Although, claim 1 and claim 18 may not be identical, they are considerably comparable or substantially equivalent given their overlapping subject matter. Thus, it is reasonable to reject claim 1 based on the teachings and rational in relation with the prior art within the rejection of claim 18.
Regarding claim 2, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 1, further comprising updating the at least one trainable parameter to modify at least one of a meta material, a reconfigurable intelligent surface, an antenna pattern, an antenna orientation, and an antenna position (Yang; updating the at least one trainable parameter to modify at least one of a meta material, a reconfigurable intelligent surface, an antenna pattern, an antenna orientation, and an antenna position [¶ 0050-0051 and ¶ 0110]; moreover, trainable parameters [¶ 0058 and ¶ 0076] in relation with site planning [¶ 0062-0063 and ¶ 0136-0137] and predicting RSS [¶ 0080-0081]; wherein, base station and antenna are linked [¶ 0085-0087]).
Regarding claim 3, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 1, wherein the configured parameters or the at least one trainable parameter include one or more of scene geometry of a physical environment, configuration of reconfigurable intelligent surfaces and meta materials, antenna patterns, array geometries, and transmitter and receiver directivity, orientations, and positions (Yang; the configured parameters or the at least one trainable parameter [as addressed within the parent claim(s)] include one or more of scene geometry of a physical environment, configuration of reconfigurable intelligent surfaces and meta materials, antenna patterns, array geometries, and transmitter and receiver directivity, orientations, and positions [¶ 0075-0076 and ¶ 0083-0086]; moreover, RSS tensor(s) [¶ 0087-0090] and horizontal and vertical lines-of-sight angles [¶ 0095 and ¶ 0097]; wherein, a multi-dimensional tensor based on the geographic data (and the antenna and power information of the base station into as the input to the convolutional neural network, and the convolutional neural network can be a trained convolutional neural network that receives the geographic data and the antenna and transmit power information as inputs and returns received signal strength as outputs [¶ 0106-0107 and ¶ 0110]).
Regarding claim 4, the rejection of claim 4 is addressed within the rejection of claim 19, due to the similarities claim 4 and claim 19 share, therefore refer to the rejection of claim 19 regarding the rejection of claim 4.
Regarding claim 5, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 4, wherein the at least one trainable parameter is related to scene geometry of a physical environment (Yang; the at least one trainable parameter [as addressed within the parent claim(s)] is related to the scene geometry of a physical environment [¶ 0050-0051 and ¶ 0110]; moreover, trainable parameters [¶ 0058 and ¶ 0076] in relation with site planning [¶ 0062-0063 and ¶ 0136-0137] and predicting RSS [¶ 0080-0081]).
Regarding claim 6, the rejection of claim 6 is addressed within the rejection of claim 5, due to the similarities claim 6 and claim 5 share, therefore refer to the rejection of claim 5 regarding the rejection of claim 6.
Regarding claim 7, the rejection of claim 7 is addressed within the rejection of claim 21, due to the similarities claim 7 and claim 21 share, therefore refer to the rejection of claim 21 regarding the rejection of claim 7.
Regarding claim 8, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 1, wherein the simulated radio characteristics estimate qualities of a transmitted electromagnetic wave at a receiver (Yang; the simulated radio characteristics [as addressed within the parent claim(s)] estimate qualities of a transmitted EM wave (i.e. RSS, propagated signal) at a receiver [¶ 0050-0051 and ¶ 0074]; additionally, VOLCANO simulator [¶ 0061-0062 and ¶ 0075-0077], i.e. RSS tools [¶ 0005]; moreover, signal strength (e.g., power or energy) between the transmitted wireless signal and the received wireless signal strength corresponding to signal propagation [¶ 0002-0005, ¶ 0024-0025, and ¶ 0090]).
Regarding claim 9, Yang in view of Nishikawa, Christensen, Chatterjee and Fiterman further discloses the computer-implemented method of claim 1, wherein the simulated radio characteristics comprise one or more of channel impulse responses, channel frequency responses, path delays, path losses, angles of arrival, angles of departure, amplitudes, powers, delay spread, Doppler spread, angular spread, power-delay-angular profile, and a number of paths (Yang; the simulated radio characteristics [as addressed within the parent claim(s)] comprise one or more of channel impulse responses, channel frequency responses, path delays, path losses, angles of arrival, angles of departure, amplitudes, powers, delay spread, Doppler spread, angular spread, power-delay-angular profile, and a number of paths [¶ 0117 and ¶ 0119]; wherein, simulated RSS data corresponds to parameters for a NN [¶ 0074-0077 and ¶ 0118]; and, wherein RSS corresponds to path loss [¶ 0002-0005]; moreover, the CNN is trained by using both simulated path loss and actual path loss data [¶ 0024-0026, ¶ 0061, and ¶ 0079]).
Regarding claim 11, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 1, wherein the at least one scene property is generated by a differentiable parametric function and a parameter input to the differentiable parametric function is adjusted to update the at least one scene property (Yang; the at least one scene property [as addressed within the parent claim(s)] is generated by a differentiable parametric function [¶ 0053-0056] and a parameter input to the differentiable parametric function [¶ 0050-0051 and ¶ 0054-0056] is adjusted to update the at least one scene property [¶ 0075-0077]; moreover, data inputs to a NN for adjusting/updating a prediction of a scene [¶ 0106-0108 and ¶ 0110]).
Christensen further teaches wherein the at least one scene property is generated by a differentiable parametric function and a parameter input to the differentiable parametric function is adjusted to update the at least one scene property (Christensen; the at least one scene property (i.e. surface reflection) is generated by a differentiable parametric function and a parameter input to the differentiable parametric function is adjusted to update the at least one scene property (i.e. surface reflection) [Page 4, Sec. 4.1 and Sec. 4.2 and Page 5, Sec. 4.4]; moreover, ray differentials [Page 3, Sec. 2.5]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, Christensen, and Chatterjee, to incorporate wherein the at least one scene property is generated by a differentiable parametric function and a parameter input to the differentiable parametric function is adjusted to update the at least one scene property (as taught by Christensen), in order to provide improved memory utilization for high computationally intensive graphics (Christensen; [Page 1, Sec. 1]).
Fiterman further teaches electromagnetic material scene property is generated by a function and a parameter input to the function is adjusted to update the at least one scene property (Fiterman; electromagnetic material scene property is generated by a function and a parameter input to the function [¶ 0006, ¶ 0017, and ¶ 0019] is adjusted to update the at least one scene property [¶ 0033-0035]; moreover, neural network based processing [¶ 0012 and ¶ 0036]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, Christensen, Chatterjee, and Fiterman, to incorporate the electromagnetic material scene property is generated by a function and a parameter input to the function is adjusted to update the at least one scene property (as taught by Fiterman), in order to provide high performance and accurate artificial intelligence models utilizing generating large amounts of training data (Fiterman; [¶ 0002-0005]).
Regarding claim 13, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 1, wherein the initializing comprises extracting the at least one trainable parameter from images of the scene (Yang; the initializing [as addressed within the parent claim(s)] comprises extracting the at least one trainable parameter from images of the scene [¶ 0054-0056]; wherein, kernels of a NN model identify particular image contours, shapes, or colors [¶ 0057-0058]; and wherein, input of a DNN includes satellite, aviation, or other image data [¶ 0065-0066]).
Regarding claim 14, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 1, wherein at least one of the steps of initializing, computing, or updating is performed on a server or in a data center and the simulated radio characteristics or an image generated from the simulated radio characteristics is streamed to a user device (Yang; at least one of the steps of initializing, computing, or updating is performed [as addressed within the parent claim(s)] on a server or in a data center [¶ 0123-0126] and the simulated radio characteristics [as addressed within the parent claim(s)] or an image generated from the simulated radio characteristics is streamed to a user device [¶ 0125-0127]; moreover, client web browser application UI associated with interactions [¶ 0173-0175] and client-server relationship [¶ 0176]).
Regarding claim 15, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 1, wherein at least one of the steps of initializing, computing, or updating is performed within a cloud computing environment (Yang; at least one of the steps of initializing, computing, or updating is performed [as addressed within the parent claim(s)] within a cloud computing environment [¶ 0078 and ¶ 0112]; moreover, cloud-computing based environment [¶ 0125]).
Regarding claim 16, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 1, wherein at least one of the steps of initializing, computing, or updating is performed for training, testing, or certifying a neural network employed in a machine, robot, or autonomous vehicle (Yang; at least one of the steps of initializing, computing, or updating is performed [as addressed within the parent claim(s)] for training, testing, or certifying a neural network [¶ 0072-0074 and ¶ 0079] employed in a machine (i.e. computer), robot (i.e. server), or autonomous vehicle [¶ 0123-0126]; moreover, RSS simulators [¶ 0061-0062 and ¶ 0075-0076]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman as applied to claim(s) 1 above, and further in view of Nanni et al., US PGPUB No. 20200106477 A1, hereinafter Nanni.
Regarding claim 10, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 1, wherein the reference radio characteristics are computed using a solver (Yang; the reference radio characteristics [as addressed within the parent claim(s)] are computed using a NN (i.e. solver) [¶ 0106-0109]; additionally, actual data, simulated, and/or trained data [¶ 0024-0029] associated with one or more radio characteristic data types [¶ 0083-0086 and ¶ 0131]; wherein, RSS prediction utilizes both simulated and actual data [¶ 0074]).
Chatterjee further teaches using an integral solver (Chatterjee; using an integral solver in relation with determining a gradient decent [Page 7 to Page 9, Sec, 3]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, Christensen, Chatterjee and Fiterman, to incorporate using an integral solver (as taught by Chatterjee), in order to provide optimization for large or dynamic learning models (Chatterjee; [Abstract and Page 1, Sec. 1]).
Yang as modified by Nishikawa, Christensen, Chatterjee, and Fiterman fails to disclose characteristics computed using an integral solver (Nanni; characteristics computed using an integral solver [¶ 0163 and ¶ 0172-0174]; additionally, EFIE [¶ 0181-0183 and ¶ 0185], loss calculations [¶ 0228-0231], and loss from propagation [¶ 0232-0234]).
Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman and Nanni are considered to be analogous art because they pertain to generating and/or managing data in relation with computational models, wherein one or more computerized units are utilized in order to calculate electromagnetic properties.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, Christensen, Chatterjee, and Fiterman, to incorporate updating the at least one trainable parameter using gradient-based optimization to minimize a loss function (as taught by Nanni), in order to provide improved electromagnetic wave propagation (Nanni; [¶ 0070-0072 and ¶ 0084-0086]).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Nishikawa, Christensen, and Chatterjee and Fiterman as applied to claim(s) 1 above, and further in view of Igehy, NPL Tracing Ray Differentials, hereinafter Igehy.
Regarding claim 12, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 11, wherein the differentiable parametric function outputs (Yang; the differentiable parametric function outputs (class scores) [¶ 0054 and ¶ 0056]).
Christensen further teaches the differentiable parametric function outputs a phase shift that is applied to the outgoing ray (Christensen; the differentiable parametric function outputs a phase shift (i.e. propagation) that is applied to the outgoing ray [Page 3, Sec. 2.5]).
Yang as modified by Nishikawa, Christensen, Chatterjee, and Fiterman fails to explicitly disclose a phase shift.
However, Igehy teaches the differentiable parametric function outputs a phase shift that is applied to the outgoing ray (Igehy; the differentiable parametric function [Page 2 to Page 3, Sec. 3] outputs a phase shift (i.e. propagation) that is applied to the outgoing ray [Page 3, Sec. 3.1.1]; moreover, transfer, reflection, or refraction [Page 3 to Page 4, Sec. 3.1 to Sec. 3.1.3]).
Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman and Igehy are considered to be analogous art because they pertain to generating and/or managing data in relation with computational models, wherein one or more computerized units are utilized in order to calculate propagation properties.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, Christensen, Chatterjee, and Fiterman, to incorporate the differentiable parametric function outputs a phase shift that is applied to the outgoing ray (as taught by Igehy), in order to provide improved realism of a scene (Igehy; [Abstract and Page 1, Sec. 1]).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman as applied to claim(s) 1 above, and further in view of Raposo Subtil et al., US PGPUB No. 20200372699 A1, hereinafter Subtil.
Regarding claim 17, Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman further discloses the computer-implemented method of claim 1, wherein at least one of the steps of initializing, computing, or updating is performed on a system (Yang; at least one of the steps of initializing, computing, or updating is performed [as addressed within the parent claim(s)] on an application [¶ 0127-0129 and ¶ 0133] of a computer [¶ 0123-0126] comprising a portion of a processor [¶ 0130]; moreover, processor [¶ 0168 and ¶ 0170-0171] implementation of an program/application [¶ 0169]).
Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman fails to disclose a virtual machine comprising a portion of a graphics processing unit.
However, Subtil teaches a virtual machine comprising a portion of a graphics processing unit (Subtil; a VM comprising a portion of a graphics processing unit [¶ 0023-0025]; moreover, ray tracing within a scene [¶ 0020-0021 and ¶ 0035]).
Yang in view of Nishikawa, Christensen, Chatterjee, and Fiterman and Subtil are considered to be analogous art because they pertain to generating and/or managing data in relation with computational models, wherein one or more computerized units are utilized in order to produce graphical computational effects.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Yang as modified by Nishikawa, Christensen, Chatterjee, and Fiterman, to incorporate updating the at least one trainable parameter using gradient-based optimization to minimize a loss function (as taught by Subtil), in order to provide improved high quality and low latency computational performance techniques (Subtil; [¶ 0005-0006 and ¶ 0020-0022]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892, Notice of Reference Cited for a listing of analogous art.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Charles Lloyd Beard whose telephone number is (571)272-5735. The examiner can normally be reached Monday - Friday, 8:00 AM - 5: 00 PM, alternate Fridays EST.
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CHARLES LLOYD. BEARD
Primary Examiner
Art Unit 2611
/CHARLES L BEARD/ Primary Examiner, Art Unit 2611
Prosecution Insights