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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Current Application 18/789,371
Copending Application No. 18/509,428
1. A computer-implemented method, comprising: computing, by a differentiable ray tracer, simulated radio characteristics for a three-dimensional (3D) scene based on at least one trainable parameter corresponding to a scene property, wherein for at least one ray intersection point in the 3D scene, segmentation data associated with the 3D scene is used to estimate the at least one trainable parameter; and updating the at least one trainable parameter using gradient-based optimization to minimize a loss function of the simulated radio characteristics and reference radio characteristics.
1. A computer-implemented method, comprising: initializing configured parameters and at least one trainable parameter corresponding to a scene property; computing, by a differentiable ray tracer, simulated radio characteristics for a three-dimensional scene based on the configured parameters and the at least one trainable parameter; and updating the at least one trainable parameter using gradient-based optimization to minimize a loss function of the simulated radio characteristics and reference radio characteristics.
2. The computer-implemented method of claim 1, wherein the segmentation data comprises an image-based segmentation map, light detection and ranging (LIDAR) based segmentation map, point-cloud segmentation data, surface segmentation map, or a volumetric segmentation map used to obtain the at least one trainable parameter associated with object classes identified in the segmentation mask, and further comprising translating a two-dimensional segmentation mask corresponding to the 3D scene to produce the segmentation data.
3. The computer-implemented method of claim 2, wherein the segmentation mask comprises probability distributions of object class identifiers or the object class identifiers.
4. The computer-implemented method of claim 1, wherein the segmentation data are included in the at least one trainable parameter.
5. The computer-implemented method of claim 1, wherein a neural component estimates the at least one trainable parameter based on the at least one ray intersection point and the segmentation data associated with the at least one ray intersection point.
6. The computer-implemented method of claim 5, wherein the at least one ray intersection point is encoded into a higher dimensional space for input to the neural component.
7. The computer-implemented method of claim 5, wherein the updating adjusts weights that are applied, by the neural component, to the at least one ray intersection point and the segmentation data to estimate the at least one trainable parameter.
8. The computer-implemented method of claim 1, wherein all ray intersection points associated with a given object class identifier, according to the segmentation data, have equal values for the at least one trainable parameter.
9. The computer-implemented method of claim 1, wherein the loss function includes a regularization term based on the segmentation data.
10. The computer-implemented method of claim 1, wherein the at least one trainable parameter includes one or more of the segmentation data, scene geometry, configuration of reconfigurable intelligent surfaces and meta materials, antenna patterns, array geometries, and transmitter and receiver orientations and positions.
3. 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, configuration of reconfigurable intelligent surfaces and meta materials, antenna patterns, array geometries, and transmitter and receiver directivity, orientations, and positions.
11. The computer-implemented method of claim 1, wherein the scene property comprises at least one of relative permittivity, conductivity, effective roughness, and permeability of object surfaces and scattering functions.
4. The computer-implemented method of claim 1, 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.
12. 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.
9. 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.
13. The computer-implemented method of claim 1, wherein the simulated radio characteristics for the 3D scene are computed based on configured parameters.
14. The computer-implemented method of claim 1, wherein at least one of the steps of 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.
14. 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.
15. The computer-implemented method of claim 1, wherein at least one of the steps of computing or updating is performed within a cloud computing environment.
15. 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.
16. The computer-implemented method of claim 1, wherein at least one of the steps of computing or updating is performed for training, testing, or certifying a neural network employed in a machine, robot, or autonomous vehicle.
16. 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.
17. The computer-implemented method of claim 1, wherein at least one of the steps of computing or updating is performed on a virtual machine comprising a portion of a graphics processing unit.
17. The computer-implemented method of claim 1, wherein at least one of the steps of initializing, computing, or updating is performed on a virtual machine comprising a portion of a graphics processing unit.
18. A system, comprising: a memory that stores reference radio characteristics; and a processor that is connected to the memory, wherein the processor is configured to produce simulated radio characteristics for a three-dimensional scene (3D) by: computing, by a differentiable ray tracer, simulated radio characteristics for the 3D scene based on at least one trainable parameter corresponding to a scene property, wherein for at least one ray intersection point in the 3D scene, segmentation data associated with the 3D scene is used to estimate the at least one trainable parameter; 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.
18. A system, comprising: a memory that stores reference radio characteristics; and a processor that is connected to the memory, wherein the processor is configured to produce simulated radio characteristics for a three-dimensional scene by: initializing configured parameters and at least one trainable parameter corresponding to a scene property; computing, by a differentiable ray tracer, the simulated radio characteristics for the three-dimensional scene based on the configured parameters and the at least one trainable parameter; 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.
19. The system of claim 18, wherein the segmentation data comprises an image-based segmentation map, light detection and ranging (LIDAR) based segmentation map, point-cloud segmentation data, surface segmentation map, or a volumetric segmentation map used to obtain the at least one trainable parameter associated with object classes identified in the segmentation mask, and further comprising translating a two-dimensional segmentation mask corresponding to the 3D scene to produce the segmentation data.
20. 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 of: computing, by a differentiable ray tracer, simulated radio characteristics for a three-dimensional (3D) scene based on at least one trainable parameter corresponding to a scene property, wherein for at least one ray intersection point in the 3D scene, segmentation data associated with the 3D scene is used to estimate the at least one trainable parameter; and updating the at least one trainable parameter using gradient-based optimization to minimize a loss function of the simulated radio characteristics and reference radio characteristics.
20. 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 of: initializing configured parameters and at least one trainable parameter corresponding to a scene property; computing, by a differentiable ray tracer, simulated radio characteristics for a three-dimensional scene based on the configured parameters and the at least one trainable parameter; and updating the at least one trainable parameter using gradient-based optimization to minimize a loss function of the simulated radio characteristics and reference radio characteristics.
21. The non-transitory computer-readable media of claim 20, wherein the differentiable ray tracer computes paths of electromagnetic waves.
21. The non-transitory computer-readable media of claim 20, wherein the differentiable ray tracer computes paths of electromagnetic waves.
22. A computer-implemented method for improving wifi performance in a residential or commercial environment represented by a three-dimensional (3D) scene comprises: identifying electromagnetic materials in the 3D scene based on segmentation data associated with the 3D scene; using adifferentiable ray tracer to computesimulated wifi signal characteristics for at least one ray intersection point in the 3D scene; applying gradient-based optimization to update simulation parameters representing the electromagnetic materials to reduce a loss function of the simulated wifi signal characteristics; and adjusting, based on the simulated wifi signal characteristics, an antenna position, antenna direction, or directional signal transmission for an antenna array of the wifi access points to optimize signal coverage.
Claim(s) 1, 10-12, 14-18, 20-21 is/are provisionally rejected on the grounds of nonstatutory double patenting of claim(s) 1, 3-4, 9, 14-18, 20-21 of copending Application No. 18/509,428 in view of Kohli (Semantic Implicit Neural Scene Representations With Semi-Supervised Training), hereinafter referenced as Kohli.
Copending claims 1, 18, and 20 recites each of the limitations (or a trivial variation) of the current claims 1, 18, and 20 except for the currently claimed wherein for at least one ray intersection point in the 3D scene, segmentation data associated with the 3D scene is used to estimate the at least one trainable parameter.
Kohli teaches x is a point along the ray which is an intersection point in the 3D representation (Page 3, Sec 3.1). The feature vector v is a function of x via SRN (Equation 1). SRN encodes w weights via HN, in which hypernetwork HN defines w using z. w is trained by adjusting z (Equation 2). SEG maps the feature vector v to a distribution over class labels to determine the predicted class probabilities outputted as y (Equation 3). y is used to compute per-pixel cross-entropy loss (Equation 4). Cross entropy loss function is then used to optimize z to train w (Page 3 Sec 3.2). Kohli teaches of at least one ray intersection point in the 3D scene x, segmentation data y associated with the 3D scene is used to estimate the at least one trainable parameter w.
Kohli is analogous art with respect to the copending application because they are from the same field of endeavor, namely computational models. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the copending claims 1, 18, and 22 with the feature of Kohli. A person of ordinary skill in the art would do such in order to improve computational model accuracies.
Copending claim(s) 3 recites each of the limitations (or a trivial variation) of the current claim(s) 10 expect for the currently claimed segmentation data.
Kohli teaches segmentation data, value y in equation 3 (Page 4 Sec 3.2).
Kohli is analogous art with respect to the copending application because they are from the same field of endeavor, namely computational modeling. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the copending claim(s) 3 with the feature of Kohli. A person of ordinary skill in the art would do such in order to improve computational model accuracies.
Copending claim(s) 4, 9, 14-17, 21 recites each of the limitations (or a trivial variation) of the current claim(s) 11-12, 14-17, 21.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejection – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-8 and 10-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang (US2019150006A1), hereinafter referenced as Yang, in view of the following: Nishikawa (US2021329478A1), hereinafter referenced as Nishikawa, Christensen (Ray Differentials and Multiresolution Geometry Caching for Distribution Ray Tracing in Complex Scenes), hereinafter referenced as Christensen, Chatterjee (Convergence of Gradient Descent For Deep Neural Networks), hereinafter referenced as Chatterjee, and Kohli (Semantic Implicit Neural Scene Representations With Semi-Supervised Training), hereinafter referenced as Kohli.
Regarding claim 18, Yang teaches a system, comprising: a memory that stores reference radio characteristics;
“FIG. 7 is a block diagram of an example computer system 700 ¶ 0123; The computer 702 also includes a database 706 that can hold data for the computer 702 or other component ¶ 0131; one or more processors receive geographic data representing geographic information of a geographic area ¶ 0083” (Yang)
and a processor that is connected to the memory, wherein the processor is configured to produce simulated radio characteristics for a three-dimensional scene (3D) by: computing, by a ray tracing method, simulated radio characteristics for the 3D scene based on at least one trainable parameter corresponding to a scene property
“geographic information system (GIS) data that includes one or more of a building height map layer, a terrain layer, or a clutter layer ¶ 0014; some layers of the ConvNet contain trainable parameters … For example, the CONV/FC layers perform transformations that are a function of not only the activations in the input volume, but also of trainable parameters (the weights and biases of the neurons) ¶ 0058-0059; A deep neural network can include a convolutional neural network (also referred to as a ConvNet) … the deep neural network 200 receives a GIS map patches 21 ¶ 0068; a deep neural network for received signal strength prediction, both simulated and actual received signal strength data can be used…to generate millions of received signal strength prediction using simulation (e.g., based on the ray tracing method) ¶ 0074; Each of the units can be implemented by one or more processors (e.g., the processor 705) interoperably coupled with one or more computer memory devices (e.g., the memory 707) ¶ 136” (Yang)
Yang teaches a processor connected to memory, wherein the processors implementing the deep neural networks ConvNet is configured to predict signal strength (reads on radio characteristic) for the geographic area by: the ConvNet computing, by ray tracing method, simulated signal strength for the geographic area based on at least one trainable parameter corresponding to geographic information input (reads on a scene property).
and updating the at least one trainable parameter to minimize a loss function of the simulated radio characteristics and the reference radio characteristics.
“some layers of the ConvNet contain trainable parameters (i.e., parameters whose values are update or otherwise derived via the training or learning process) … The trainable parameters in the CONV/FC layers can be trained with gradient descent ¶ 0058; the supervised learning 304 can receive the GIS map patches 302 as the input and the corresponding received signal strength matrices 306 as the ground truth (or a supervisor) and determine the parameters (e.g., the weights and biases of the neurons) of the ConvNet-VOLCANO 308 that minimize a loss function. ¶ 00075; the one or more processors can calculate path loss values corresponding to the predicted received signal strength. ¶ 0110” (Yang)
Yang teaches updating the at least one trainable parameter to minimize a loss function of the calculated path loss values corresponding to the predicted received signal strength (reads on simulated radio characteristics) and GIS map patches input (reads on the reference radio characteristics).
Yang fails to teach the following: radio characteristics for a three-dimensional scene; a differentiable ray tracer; using gradient-based optimization; and wherein for at least one ray intersection point in the 3D scene, segmentation data associated with the 3D scene is used to estimate the at least one trainable parameter.
Nishikawa teaches a processor is configured to produce simulated radio characteristics for a three-dimensional scene.
“the computer 100 has a processor 101 ¶ 0171; simulated radio wave strength calculation unit 43 calculates the simulated radio wave strength at each position in the target space for the simulation signal based on the geospatial information on the target space using the theoretical model of radio wave propagation ¶ 0082” (Nishikawa)
Nishikawa teaches of a processor which simulated radio wave strength (reads on radio characteristics) for geospatial information (reads on three-dimensional scene).
Nishikawa BASE is analogous art with respect to Yang because they are from the same field of endeavor, namely computational models. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang with the feature of Nishikawa to incorporate a processor is configured to produce simulated radio characteristics for a three-dimensional scene. A person of ordinary skill in the art would do such in order to improve computer model accuracies.
Yang in view of Nishikawa fail to teach the following: a differentiable ray tracer; using gradient-based optimization; and at least one ray intersection point in the 3D scene, segmentation data associated with the 3D scene is used to estimate the at least one trainable parameter.
Christensen teaches computing, by a differentiable ray tracer, the simulated characteristics for the three-dimensional scene
“Igehy’s ray differential method11 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 represents. The curvature at surface intersection points determines how those ray differentials are propagated at specular reflection and refraction.” (Christensen, Page 3, Sec 2.5)
PNG
media_image1.png
624
1447
media_image1.png
Greyscale
(Christensen)
Christensen computing the simulated characteristics for the 3D scene by a differentiable ray tracer.
Christensen BASE is analogous art with respect to Yang in view of Nishikawa because they are from the same field of endeavor, namely computational models. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of Nishikawa with the feature of Christensen to incorporate computing, by a differentiable ray tracer, the simulated characteristics for the three-dimensional scene. A person of ordinary skill in the art would do such in order to improve memory utilization for high computationally intensive graphics.
Yang in view of Nishikawa and Christensen fail to teach using gradient-based optimization to minimize a loss function and wherein for at least one ray intersection point in the 3D scene, segmentation data associated with the 3D scene is used to estimate the at least one trainable parameter.
Chatterjee teaches updating the at least one trainable parameter using gradient-based optimization to minimize a loss function.
“The goal of gradient descent is to find a minimum of a differentiable function… For example, it has been observed that gradient descent can often find global minima of training loss in deep learning [29, 62], which is one of the reasons behind great success of the ‘deep learning revolution’ [14, 40]” ( Page 1-2 Sec 1, Chatterjee)
Chatterjee teaches updating the at least one trainable parameter using gradient descent optimization to minimize a loss function via finding global minima of training loss.
Chatterjee BASE is analogous art with respect to Yang in view of Nishikawa and Christensen because they are from the same field of endeavor, namely computational models. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of Nishikawa and Christensen with the feature of Chatterjee to incorporate updating the at least one trainable parameter using gradient descent optimization to minimize a loss function via finding global minima of training loss. A person of ordinary skill in the art would do such in order to improve optimize learning models.
Yang in view of Nishikawa, Christensen, and Chatterjee fail to teach wherein for at least one ray intersection point in the 3D scene, segmentation data associated with the 3D scene is used to estimate the at least one trainable parameter.
Kohli teaches wherein for at least one ray intersection point in the 3D scene, segmentation data associated with the 3D scene is used to estimate the at least one trainable parameter.
“a scene is modeled as a function that maps world coordinates x to a feature representation of local scene properties v:
PNG
media_image2.png
110
1312
media_image2.png
Greyscale
Images are synthesized from this 3D representation via a differentiable neural renderer consisting of two parts. The first is a differentiable ray marcher which finds intersections of camera rays with scene geometry by marching along a ray away from a camera … A hypernetwork [25] HN maps embedding vectors zj to the weights wj of the respective scene representation network:
PNG
media_image3.png
106
1326
media_image3.png
Greyscale
… z is optimized to obtain a new scene embedding via minimizing image reconstruction error. (Page 3-4 Section 3.1); We formalize dense 3D semantic segmentation as a function that maps a world coordinate x to a distribution over semantic labels y … we define the Segmentation Renderer SEG, a function that maps a feature vector v to a distribution over class labels y:
PNG
media_image4.png
110
1351
media_image4.png
Greyscale
Since v is a function of x, we may enforce a per-pixel cross-entropy loss on the SEG output at any world coordinate x:
PNG
media_image5.png
201
1185
media_image5.png
Greyscale
In any of these cases, a new code vector is inferred by freezing all network weights, initializing a new code vector z, and optimizing z to minimize image reconstruction and/or cross entropy losses (Page 4 Sec 3.2)” (Kohli)
x is a point along the ray which is an intersection point in the 3D representation (reads on as least one ray intersection point in the 3D scene). The feature vector v is a function of x via SRN, see equation 1. SRN encodes w weights via HN, in which hypernetwork HN defines w using z. w is trained by adjusting z, see equation 2. (w reads on at least trainable parameter). SEG maps the feature vector v to a distribution over class labels to determine the predicted class probabilities outputted as y, see equation 3 (y reads on segmentation data). y is used to compute per-pixel cross-entropy loss, see equation 4. Cross entropy loss function is then used to optimize z to train w. Kohli teaches of at least one ray intersection point in the 3D scene x, segmentation data y associated with the 3D scene is used to estimate the at least one trainable parameter w.
Kohli BASE is analogous art with respect to Yang in view of the following: Nishikawa, Christensen, and Chatterjee because they are from the same field of endeavor, namely computational models. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, and Chatterjee with the feature of Kohli to use at least one ray intersection point in the 3D scene x, segmentation data y associated with the 3D scene is used to estimate the at least one trainable parameter w. A person of ordinary skill in the art would do such in order to improve computational model accuracies.
Claim 1 is rejected using the same rationale or bases as applied to claim 18.
Regarding claim 2, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teach the computer-implemented method of claim 1, and additionally teaches the following. Kohli teaches wherein the segmentation data comprises an image-based segmentation map, light detection and ranging (LIDAR) based segmentation map, point-cloud segmentation data, surface segmentation map, or a volumetric segmentation map used to obtain the at least one trainable parameter associated with object classes identified in the segmentation mask, and further comprising translating a two-dimensional segmentation mask corresponding to the 3D scene to produce the segmentation data.
“Here we view the recently proposed scene representation networks (SRNs) from a representation learning perspective in order to infer multi-modal, compact 3D representations of objects from 2D images. We take the latent 3D feature representation of SRNs, learned in an unsupervised manner given only posed 2D RGB images, and map them to a set of labeled semantic segmentation maps (Page 2 Sec 1); This training is supervised with human-labeled, posed semantic segmentation masks of a small subset of the training images (Page 5, Sec 3.3); We formalize dense 3D semantic segmentation as a function that maps a world coordinate x to a distribution over semantic labels y. This can be seen as a generalization of point cloud- and voxel-grid-based semantic segmentation approaches (Page 4 Sec 3.2)” (Kohli)
Kohli teaches of point cloud- and voxel-grid-based semantic segmentation approaches (reads on point-cloud segmentation data) wherein 2D RGB images are mapped to labeled semantic segmentation maps (reads on two-dimensional segmentation mask). These semantic segmentation masks are used as training inputs (one trainable parameter associated with object classes identified in the segmentation mask) and translated into 3D segmentation data via the SRN feature representation (reads on translating a two-dimensional segmentation mask corresponding to the 3D scene to produce the segmentation data).
Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Kohli to incorporate point cloud- and voxel-grid-based semantic segmentation approaches wherein posed 2D RGB images are mapped to labeled semantic segmentation masks for training input and translated into 3D segmentation data via the SRN feature representation. A person of ordinary skill in the art would do such in order to improve computational models.
Claim 19 is rejected using the same rationale or bases as applied to claim 2.
Regarding claim 3, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teach the computer-implemented method of claim 2, and additionally teaches the following. Kohli teaches wherein the segmentation mask comprises probability distributions of object class identifiers or the object class identifiers.
PNG
media_image5.png
201
1185
media_image5.png
Greyscale
“yj is a one-hot ground-truth class label with c number of classes” (Kohli, Page 4 Sec 3.2)
yj is the true mapping of segmentation masks and class labels values (reads on segmentation masks) corresponds to j to c class labels (reads on object class identifiers) according to the true distribution with the structure a cross-entropy loss function, see equation 4 (reads on the segmentation mask comprise of object class identifiers).
Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Kohli to wherein the segmentation mask yj comprise of object class identifiers. A person of ordinary skill in the art would do such in order to improve computational model accuracies.
Regarding claim 4, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 1, and additionally teaches the following. Kohli teaches wherein the segmentation data are included in the at least one trainable parameter.
“For an object with c semantic classes, the optimization parameters are matrix W ∈ R256xc and bias b ∈ Rc. (Page 12 Supplemental Sec 1); The key idea of SRNs is to encode a scene in the weights w ∈ Rl of a fully connected neural network, the SRN itself … To this end, a scene is modeled as a function that maps world coordinates x to a feature representation of local scene properties v:
PNG
media_image2.png
110
1312
media_image2.png
Greyscale
… A hypernetwork [25] HN maps embedding vectors zj to the weights wj of the respective scene representation network:
PNG
media_image3.png
106
1326
media_image3.png
Greyscale
… z is optimized to obtain a new scene embedding via minimizing image reconstruction error.
Segmentation Renderer SEG, a function that maps a feature vector v to a distribution over class labels y:
PNG
media_image4.png
110
1351
media_image4.png
Greyscale
Page 3-4 Section 3.1-3.2” (Kohli)
SEG maps the feature vector to a distribution over class labels to determine the predicted class probabilities outputted as y, see equation 3 (y reads on segmentation data). The feature vector v inputted into SEG is derived from SRN which encodes w weights. Hypernetwork HN defines w using z, see equation 2. w is trained by adjusting z. (w reads on at least one trainable parameter). Kohli teaches of segmentation data y that includes at least one trainable parameter w because v = SRN (x; w) and y = SEG(v), the output of y is a function of w through v. w are used in generating the vector feature v, which is then inputted into SEG to produce the segmentation data y.
Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Kohli to incorporate the segmentation data, y, to include in the at least one trainable parameter, w. A person of ordinary skill in the art would do such in order to improve computational model accuracies.
Regarding claim 5, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 1, and additionally teaches the following. Kohli teaches wherein a neural component estimates the at least one trainable parameter based on the at least one ray intersection point and the segmentation data associated with the at least one ray intersection point.
“a scene is modeled as a function that maps world coordinates x to a feature representation of local scene properties v:
PNG
media_image2.png
110
1312
media_image2.png
Greyscale
The key idea of SRNs is to encode a scene in the weights w ∈ Rl of a fully connected neural network, the SRN itself … A hypernetwork [25] HN maps embedding vectors zj to the weights wj of the respective scene representation network:
PNG
media_image3.png
106
1326
media_image3.png
Greyscale
… We formalize dense 3D semantic segmentation as a function that maps a world coordinate x to a distribution over semantic labels y … we define the Segmentation Renderer SEG, a function that maps a feature vector v to a distribution over class labels y:
PNG
media_image4.png
110
1351
media_image4.png
Greyscale
… Since v is a function of x, we may enforce a per-pixel cross-entropy loss on the SEG output at any world coordinate x:
PNG
media_image5.png
201
1185
media_image5.png
Greyscale
… In any of these cases, a new code vector is inferred by freezing all network weights, initializing a new code vector z, and optimizing z to minimize image reconstruction and/or cross entropy losses (Page 4 Sec 3.2)” (Kohli)
Hypernetwork estimates trainable parameter w by optimizing z. Cross entropy losses is used to optimize z. Cross-entropy loss, see equation 4, is calculated using SEG. SEG maps the feature vector v to a distribution over class labels to determine the predicted class probabilities to get y, see equation 3 (y reads on segmentation data). The feature vector v is a function of x via SRN, see equation 1, which x is a point along the ray which is an intersection point in the 3D representation. (reads as least one ray intersection point in the 3D scene).
Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Kohli to incorporate hypernetwork estimates trainable parameter w based on the at least one ray intersection point x and the segmentation data y associated with the at least one ray intersection point x. A person of ordinary skill in the art would do such in order to improve computational model accuracies.
Regarding claim 6, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 5, and additionally teaches the following. Kohli teaches wherein the at least one ray intersection point is encoded into a higher dimensional space for input to the neural component.
“The key idea of SRNs is to encode a scene in the weights w ∈ Rl of a fully connected neural network, the weights w ∈ Rl of a fully connected neural network, the SRN itself. To this end, a scene is modeled as a function that maps world coordinates x to a feature representation of local scene properties v (Page 3-4 Sec 3.1);
PNG
media_image2.png
110
1312
media_image2.png
Greyscale
… we take the pre-trained features v ∈ R256 from the neural scene representation and use a simple linear transformation to map those features to class probabilities for each pixel. (Page 12 Supplemental Sec 1)
The feature vector v is a function of x via SRN, see equation 1. The SRN maps a 3D world coordinate x to a n-dimensional feature vector v, lifting from R3 to Rn. This is a higher dimensional encoding of the ray intersection point before it is inputted into the segmentation renderer neural component.
Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Kohli to incorporate at least one ray intersection point is encoded into a higher dimensional space for input inputted into the segmentation renderer neural component. A person of ordinary skill in the art would do such in order to improve computational model accuracies.
Regarding claim 7, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 5, and additionally teaches the following. Kohli teaches wherein the updating adjusts weights that are applied, by the neural component, to the at least one ray intersection point and the segmentation data to estimate the at least one trainable parameter.
“We train our models using ADAM with a learning rate of 4e−4… we take the pre-trained features v ∈ R256 from the neural scene representation and use a simple linear transformation to map those features to class probabilities for each pixel…Image reconstruction loss and cross-entropy loss are weighted 200 : 8, such that their magnitudes are approximately equal. (Page 12 Supplemental Sec 1); Since v is a function of x, we may enforce a per-pixel cross-entropy loss on the SEG output at any world coordinate x:
PNG
media_image5.png
201
1185
media_image5.png
Greyscale
… We can now train the segmentation renderer end-to-end composed with the same architecture used to pre-train the scene representation. In any of these cases, a new code vector is inferred by freezing all network weights, initializing a new code vector z, and optimizing z to minimize image reconstruction and/or cross entropy losses (Page 4 Sec 3.2)” (Kohli)
SEG(v) or y (reads on segmentation data) is used to compute per-pixel cross-entropy loss, see equation 4. Cross entropy loss function is then used to optimize z to train weight w see equation 2. (reads on the segmentation data estimating the at least one trainable parameter). Moreover, the training process adjusts the weights of the linear segmentation render via the ADAM optimizer (ADAM optimizer reads on neural component). These wights are applied to the feature vectors v, which are derived from ray intersection points see equation 1 and 2.
Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Kohli with the feature of Kohli to updating adjusts weights that are applied, by the ADAM optimizer, to the at least one vector v, which is derived from ray intersection points and the segmentation data, to estimate the at least one trainable parameter w. A person of ordinary skill in the art would do such in order to improve computational model accuracy.
Regarding claim 10, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teach the computer-implemented method of claim 1, and additionally teaches the following. Yang teaches wherein the at least one trainable parameter includes one or more of the segmentation data, scene geometry, configuration of reconfigurable intelligent surfaces and meta materials, antenna patterns, array geometries, and transmitter and receiver orientations and positions.
“Supervised learning 304 can be performed to generate a trained deep neural network, ConvNet-VOLCANO 308, based on the simulated training data obtained from the VOLCANO simulator 305 . For example, the supervised learning 304 can receive the GIS map patches 302 as the input. ¶ 0075; The horizontal LOS angle θ 1210 can refer to the horizontal angle θ (in the x-y plane) of an LOS path 1240 from the antenna 1235 of the base station 1230 to the cube 1220 … A vertical LOS angle tensor can include vertical LOS angles at different locations in a geographic area with respect to a base station in the geographic area. ¶ 0095-0096; the one or more processors use a multi-dimensional tensor (e.g., the above-described 4D tensor that includes five channels of 3D tensors) based on the geographic data (and the antenna and power information of the base station into as the input to the convolutional neural network. ¶ 0106” (Yang)
Yang teaches wherein the at least one trainable parameter includes one or more GIS map patches (GIS inputs encode structured spatial information regarding the environment reads on segmentation data and scene geometry), antenna and transmit power information, spatial tensors representing line of sight angles, input data corresponding to environmental geometry antenna configurations, and transmitter and receiver spatial orientation and positions.
Regarding claim 11, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teach the computer-implemented method of claim 1, and additionally teaches the following. Yang teaches wherein the scene property comprises at least one of relative permittivity, conductivity, effective roughness, and permeability of object surfaces and scattering functions.
“The geographic data can include geographic image data such as geographic information system (GIS) data (e.g., the GIS map patches 110 , 210 , 302 , or 402 ) that includes one or more of a building height map layer, a terrain layer, or a clutter layer representing geographic information of a geographic area. ¶ 0083; the one or more processors further compute or otherwise determine a multi-dimensional … based on the geographic data…and the antenna and power information of the base station, for example, based on known physical laws (e.g., free-space path loss and signal attenuation based on material permittivity). ¶ 0086” (Yang)
Yang teaches the scene property comprises geographic data, antenna, and power information of the base station based on known physical laws of electromagnetic properties of the surfaces (reads on permittivity, conductivity, effective roughness, and permeability of object surfaces and scattering functions).
Regarding claim 12, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 1, and additionally teaches the following. Yang teaches 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.
“The simulator can be, for example, a commercial received signal strength simulator such as VOLCANO simulator 305 , or any other simulators that implement one or more of a statistical method, a deterministic method, or a combination of these and other methods for received signal strength prediction. ¶ 0117; the simulated received signal strength representing simulated signal strength of wireless signals received at different locations in the second geographic area ¶ 0119”
Yang teaches wherein the simulated received signal strength (reads on simulated radio characteristics) comprise of signal strength of wireless signals received at different locations (reads on powers).
Regarding claim 13, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 1, and additionally teaches the following. Yang teaches wherein the simulated radio characteristics for the 3D scene are computed based on configured parameters.
“a ConvNet takes GIS data as an input, and predicts received signal strength matrix as an output… A ConvNet can have hundreds of millions of parameters (or weights in neural network terminology) that are learned from training ¶ 59” (Yang)
Yang teaches received signal strength (reads on radio characteristics) are computed based on a ConvNet can have hundreds of millions of parameters which can be earned and trained (reads on configured parameters)
Nishikawa teaches the simulated radio characteristics for the 3D scene
“simulated radio wave strength calculation unit 43 calculates the simulated radio wave strength at each position in the target space for the simulation signal based on the geospatial information on the target space using the theoretical model of radio wave propagation ¶ 0082(Nishikawa)
Nishikawa teaches of simulated radio wave strength (reads on radio characteristics) for geospatial information (reads on three-dimensional scene).
Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Nishikawa to incorporate a simulated radio wave for geospatial information. A person of ordinary skill in the art would do such in order to improve computational model accuracies and precisions.
Regarding claim 14, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 1, and additionally teaches the following. Yang teaches: wherein at least one of the steps of 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.
“computer 702 is intended to encompass any computing device such as a server, desktop ¶ 0125; The computer 702 can receive requests over network 730 from a client application … and respond to the received requests by processing the received requests using an appropriate software application(s). In addition, requests may also be sent to the computer 702 from internal users ¶ 0127; a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification ¶ 0175; The computing system can include clients and servers …The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. ¶ 0176”
Yang teaches of wherein at least one of the steps of computing or updating is performed on a server and the simulated radio characteristics or an image generated from the simulated radio characteristics is streamed to a user device via a client server relationship.
Regarding claim 15, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 1, and additionally teaches the following. Yang teaches: wherein at least one of the steps of computing or updating is performed within a cloud computing environment.
“In some implementations, one or more components of the computer 702 may be configured to operate within environments, including cloud-computing-based, local, global, or other environment (or a combination of environments). ¶ 0125” (Yang)
Yang teaches wherein at least one of the steps of computing or updating within cloud-computing-based environments.
Regarding claim 16, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 1, and additionally teaches the following. Yang teaches wherein at least one of the steps of computing or updating is performed for training, testing, or certifying a neural network employed in a machine, robot, or autonomous vehicle.
“method 500 for predicting received signal strength in a telecommunication network using a deep neural network…The method 500 can be implemented by one or more processors that execute a convolutional neural network that includes a number of convolution layers ¶ 0078; The convolutional neural network can be a deep neural network (e.g., the deep neural network 120 or 200 , the ConvNet-VOLCANO 308 , and ConvNet-Real 408). ¶ 0079” (Yang)
Yang teaches wherein at least one of the steps of computing or updating is performed for training, testing, or certifying a neural network employed in processor(s).
Regarding claim 17, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 1, and additionally teaches the following. Yang teaches wherein at least one of the steps of computing or updating is performed on a virtual machine comprising a portion of a graphics processing unit.
“the described techniques can be 10,000 times faster in computation compared to ray tracing methods using normal workstations with a graphic processing unit (GPU). ¶ 64; the illustrated computer 702 is intended to encompass any computing device such as … any other suitable processing device, including physical or virtual instances (or both) of the computing device ¶ 124;
Yang teaches wherein at least one of the steps of computing or updating is performed on a virtual instances comprising a portion of a graphics processing unit.
Claim 20 is rejected using the same rationale or bases as applied to claim 18 and the mentioned structure.
Additionally, claim 20 recites the following structure: 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 as taught by Yang.
“a non-transitory computer-readable media storing computer instructions for predicting received signal strength in a telecommunication network, that when executed by one or more processors, cause the one or more processors to perform the steps of ¶ 0008” (Yang);
Regarding claim 21, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches non-transitory computer-readable media of claim 20, and additionally teaches the following. Yang teaches wherein the ray tracer computes paths of electromagnetic waves.
“In some implementations, the described techniques can improve accuracy of received signal strength prediction. For example, the described techniques using the deep neural network can capture complex relations between the GIS data and the received signal strength, whereas the statistical models, such as HATA, has only a few parameters and can only capture simple relations between the GIS data and the received signal strength. In some implementations, the described techniques can obtain more accurate results than deterministic methods such as ray tracing methods, for example, in the case where the input is incomplete GIS data.”
Yang teaches of ray tracer computes electromagnetic waves, received signal strength.
Christensen teaches differentiable ray tracer computes paths.
“Igehy’s ray differential method11 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 represents. The curvature at surface intersection points determines how those ray differentials are propagated at specular reflection and refraction.” (Christensen, Page 3, Sec 2.5)
PNG
media_image1.png
624
1447
media_image1.png
Greyscale
(Christensen)
Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Christensen to incorporate the differentiable ray tracer computes paths. A person of ordinary skill in the art would do such in order to improve memory utilization for high computationally intensive graphics.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of the following: Nishikawa, Christensen, Chatterjee, Kohli, and Ravanbakhsh (Equivariance Through Parameter-Sharing), hereinafter referenced as Ravanbakhsh.
Regarding claim 8, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 1, and additionally teaches the following. Kohli teaches wherein all ray intersection points associated with a given object class identifier, according to the segmentation data, have association for the at least one trainable parameter.
“a scene is modeled as a function that maps world coordinates x to a feature representation of local scene properties v:
PNG
media_image2.png
110
1312
media_image2.png
Greyscale
…The key idea of SRNs is to encode a scene in the weights w ∈ Rl of a fully connected neural network, the SRN itself … A hypernetwork [25] HN maps embedding vectors zj to the weights wj of the respective scene representation network:
PNG
media_image3.png
106
1326
media_image3.png
Greyscale
… We formalize dense 3D semantic segmentation as a function that maps a world coordinate x to a distribution over semantic labels y … we define the Segmentation Renderer SEG, a function that maps a feature vector v to a distribution over class labels y:
PNG
media_image4.png
110
1351
media_image4.png
Greyscale
(Page 4 Sec 3.2)” (Kohli)
x is a point along the ray which is an intersection point in the 3D representation (ray intersection point). x is used to determine the feature vector v through SRN, see equation 1. SRN which encodes w weights (reads all ray intersection points having association with trainable parameters). Segmentation Renderer SEG, maps a feature vector to a distribution over class labels to produce y (reads on ray intersection points associated with a given object class identifier in accordance with segmentation data).
Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Kohli to incorporate all ray intersection points x associated with a given class label, according to the segmentation data y, have association for the at least one trainable parameter w. A person of ordinary skill in the art would do such in order to improve computational efficiency.
Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli fail to teach all ray intersection points having have equal values for the at least one trainable parameter.
Ravanbakhsh teaches all ray intersection have equal values for the at least one trainable parameter.
“We propose to study equivariance in deep neural networks through parameter symmetries. In particular, given a group G that acts discretely on the input and output of a standard neural network layer φW ∶ RM → RN, we show that φW is equivariant with respect to G-action iff G explains the symmetries of the network parameters W” (Page 1 Abstract, Ravanbakhsh)
Multiple inputs (reads on all ray intersection points) belong to the same structured group are processed using identical trainable weights (reads on have equal values for the at least one trainable parameter). Moreover, the neural network can be designed so that the elements within the same group share the same parameter values thereby ensuring consisted equivariance.
Ravanbakhsh BASE is analogous art with respect to Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli because they are from the same field of endeavor, namely computational modeling. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Ravanbakhsh to incorporate all ray intersection points x associated with a given object class identifier c, according to the segmentation data y, have equal values for the at least one trainable parameter w. A person of ordinary skill in the art would do such in order to improve computational efficiency.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of the following: Nishikawa, Christensen, Chatterjee, Kohli, and Nair (An Introduction to Regularization), hereinafter referenced as Nair.
Regarding claim 9, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches the computer-implemented method of claim 1, and additionally teaches the following. Kohli teaches:
A loss function based on the segmentation data.
we define the Segmentation Renderer SEG, a function that maps a feature vector v to a distribution over class labels y:
PNG
media_image4.png
110
1351
media_image4.png
Greyscale
Since v is a function of x, we may enforce a per-pixel cross-entropy loss on the SEG output at any world coordinate x:
PNG
media_image5.png
201
1185
media_image5.png
Greyscale
In any of these cases, a new code vector is inferred by freezing all network weights, initializing a new code vector z, and optimizing z to minimize image reconstruction and/or cross entropy losses (Page 4 Sec 3.2)” (Kohli)
SEG maps the feature vector v to a distribution over class labels to determine the predicted class probabilities outputted as y, see equation 3 (y reads on segmentation data). y is used to compute per-pixel cross-entropy loss, see equation 4. Kohli teaches of a loss function based on the segmentation data.
Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Kohli to incorporate a loss function based on segmentation data. A person of ordinary skill in the art would do such in order to improve computational accuracies.
Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli fail to teach wherein the loss function includes a regularization term based on the segmentation data.
Nair does. Nair teaches wherein the loss function includes a regularization term based on the unseen data.
“Regularization ensures that the loss function not only considers the difference between the predicted and actual values but also considers how much importance is being assigned to the features. With this technique, users can limit model complexity and train models that can make accurate predictions with unseen data (Page 3, Nair)
Nair teaches of a loss function which includes regularization term based on unseen data.
Nair BASE is analogous art with respect to Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli because they are from the same field of endeavor, namely computational models. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Nair to incorporate a loss function based on the segmentation data. A person of ordinary skill in the art would do such in order to improve computational models’ accuracies.
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of the following: Nishikawa, Christensen, Chatterjee, Kohli, and Ferrari (US11742965B2), hereinafter referenced as Ferrari.
Claim 22 is rejected using the same rationale or bases as applied to claim 18 and the mentioned structure.
Additionally, regarding claim 22, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli teaches adjusting, based on the simulated signal characteristics, an antenna position, antenna direction, or directional signal transmission for an antenna array of the access points to optimize signal coverage.
“The predicted path loss values can represent propagation attenuation of the wireless signals at the different locations in the geographic area. Automatic site planning can include, for example, improving, optimizing, or otherwise 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” (Yang)
Yang teaches of adjusting antenna configurations (reads on an antenna position) for a base station (reads on antenna array of the wifi access points) based on predicted path loss values (simulated signal characteristics).
However, Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli fail to teach improving wifi performance in a residential or commercial environment and wifi signal characteristics;
Ferrari does. Ferrari teaches improving wifi performance in a residential or commercial environment and wifi signal characteristics;
“A visualization of the wireless signal propagation can help understanding the signal propagation (i.e., assessing the signal propagation behavior) and validating the signal propagation based on signal level measurements from APs and sensors so that an optimized wireless network can be designed as to where to place or how to configure Wi-Fi APs. ¶ 0005; FIG. 5 illustrates an example 3-D visualization 300 of Wi-Fi AP RF signal propagation. In the 3-D visualization 300, the 3-D visualization of a building plan (e.g., floor plan) is overlaid with RF propagation patterns. ¶ 0069” (Ferrari).
PNG
media_image6.png
562
936
media_image6.png
Greyscale
Ferrari teaches optimizing wireless network ((reads on improving wifi performance) moreover Wi-Fi access points (reads on wifi signal characteristics) in building plans of commercial building, see figure 5 (reads on commercial environment).
Ferrari BASE is analogous art with respect to Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli because they are from the same field of endeavor, namely computational modeling. Before the effective filling date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Yang in view of the following: Nishikawa, Christensen, Chatterjee, and Kohli with the feature of Ferrari to optimizing wireless moreover Wi-Fi access points in building plans of commercial building. A person of ordinary skill in the art would do such in order to improve computational usage and efficacy.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUNE NGUYEN whose telephone number is (571)272-8919. The examiner can normally be reached M-TH 7:00AM - 5:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Devona E Faulk can be reached at (571) 272-7515. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DUNE NGOC NGUYEN/Examiner, Art Unit 2618
/DEVONA E FAULK/Supervisory Patent Examiner, Art Unit 2618
Prosecution Insights