DETAILED ACTION
Status of Claims
Claims 1-23 are pending in this application, with claims 1, 20 and 22 being independent.
Notice of 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 .
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 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.
Obligation Under 37 CFR 1.56 – Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Drawings
The drawings were received on May 2, 2024. These drawings are acceptable.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-9, 12-18, 20 and 22-23 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by CORGAN et al. (US 2024/0323719 hereinafter “CORGAN”).
Regarding claim 20, CORGAN discloses a system (FIG. 6. ¶ [0038]: “FIG. 6 is a diagram illustrating an example of a communication system and an RF-RF model training process.”), comprising:
a memory (¶ [0102]: “a remote storage”, See element 618 in FIG. 6) that stores reference radio characteristics (¶ [0102]: “determined channel characteristics”; i.e., ¶ [0113]: “real-world RF captures” ¶ [0108]: “receives data (e.g., and stores the data) in operation 618 and uses the received data for training.”) (¶ [0102]: “As shown in FIG. 6, in some implementations, the determined channel characteristics are sent to another system and/or stored for later retrieval (618). For example, a channel tensor H and/or a PDP determined at the receiver 602 can be sent from the receiver 602 to a remote storage or system, such as the RIC 110, the cloud service 114, etc. In some implementations, other information to be used for training (e.g., the ptx and Prx corresponding to the received signal) can also be sent. The stored data can later be retrieved and used for training.” ¶ [0104]: “As further shown in FIG. 6, the channel characteristics (e.g., directly from the receiver 602 or retrieved from a remote storage/system) and respective positions ptx, Prx of the receiver 602 and transmitter 604 are used to train or retrain an RF-RF model (616). For example, the positions ptx, Prx and the determined channel characteristics can be used as training data, with the determined channel characteristics being ground-truth labels for the positions ptx, Prx” ¶ [0180]: “The computing device 1500 includes a processor 1502, a memory 1504, a storage device 1506, a high-speed interface 1508 connecting to the memory 1504 and multiple high-speed expansion ports 1510, and a low-speed interface 1512 connecting to a low-speed expansion port 1514 and the storage device 1506. Each of the processor 1502, the memory 1504, the storage device 1506, the high-speed interface 1508, the high-speed expansion ports 1510, and the low-speed interface 1512, are interconnected using various busses,”); and
a processor that is connected to the memory (¶ [0180]: “The computing device 1500 includes a processor 1502, a memory 1504, a storage device 1506, a high-speed interface 1508 connecting to the memory 1504 and multiple high-speed expansion ports 1510, and a low-speed interface 1512 connecting to a low-speed expansion port 1514 and the storage device 1506. Each of the processor 1502, the memory 1504, the storage device 1506, the high-speed interface 1508, the high-speed expansion ports 1510, and the low-speed interface 1512, are interconnected using various busses,”), wherein the processor is configured to produce simulated radio characteristics (e.g., ¶ [0005]: “executing a radio frequency radiance field (RF-RF) model characterizing an environment;”) for a three-dimensional scene (3D) (e.g., FIG. 4. ¶ [0005]: “executing a radio frequency radiance field (RF-RF) model characterizing an environment;” ¶ [0067]: “an environment 300 in which a transmission point ptx and a reception point Prx are located.” ¶ [0067]: “selected from within a bounded region 302, e.g., as opposed to from anywhere in the environment 300.” ¶ [0068]: “a three-dimensional shape, such as an ellipsoid, that defines the bounded region 302.” ¶ [0072]: “in 3D space.” ¶ [0096]: “in an environment,” ¶ [0168]: “in very harsh 3D environments”) (e.g., ¶ [0053]: “executing a radio frequency radiance field (RF-RF) model characterizing an environment;” ¶ [0066]: “the process 200 and other ray-tracing-based processes for channel estimation can be performed by any suitable computing device or system and, in particular, by any of the devices and systems shown in FIG. 1.” ¶ [0071]: “The RF-RF model represents an encoding of the environment 300, including absorption characteristics of the environment 300,” ¶ [0072]: “The RF-RF model represents an encoding of the environment 300, including reflection characteristics of the environment 300,” ¶ [0177]: “FIG. 15 is a diagram illustrating an example of a computing system that may be used to implement one or more components of a system that utilizes RF-RF models for RF system operations. The computer system illustrated in FIG. 15 can be, or can include, one or more of the network devices and modules described herein, e.g., UE, DU, RU, CU, and cloud computing system, for example in any of systems 100, 600, and/or 700. The computer system illustrated in FIG. 15, and/or a component or portion thereof, can be used to perform any of the processes described herein, such as processes 200, 610, 800, 900, 1000, 1100, 1200, and/or 1300.”) by:
computing (e.g., ¶ [0066]: “the process 200 and other ray-tracing-based processes for channel estimation can be performed by any suitable computing device or system and, in particular, by any of the devices and systems shown in FIG. 1.“), by a differentiable ray tracer (¶ [0062]: “The radio frequency radiance field (RF-RF) models referred to above are now described. These models can include, for example, neural radiance field (NeRF) models, neural graphics primitives (NGP) models, and Gaussian splatting models, each of which can be used with differentiable ray-tracing methods to estimate RF channel parameters and, in this manner, control RF transmission/reception.” ¶ [0065]: “process 200 is a non-limiting example of the types of ray-tracing-based processes, within the scope of this disclosure, that can be performed using RF-RF models as described herein.” ¶ [0069]: “A ray (e.g., an i-th ray) is traced between the transmission point ptx and the reception point ptx through the selected point pi (204).” ¶ [0080]: “FIG. 4 illustrates an example of a ray-tracing simulation, showing rays traced in an environment 400 between a transmission position 402 and a reception position 404. Rays emanate from each of the positions 402, 404. The dark path 406 represents a path along which a RF receiver was moved during data collection to sample data (e.g., PDP) for subsequent model training. FIG. 4 illustrates sampling locations within the environment which may serve as reflectors between positions 402 and 404, in order to evaluate reflected rays originating at 402, reflecting through the sampled point, and terminating at position 404. By evaluating the ray gain, absorption, time delay, and/or other properties along the ray path to and from a sample location (e.g. by using a radiance field model which stores these properties), along with the reflectance of the incoming and outgoing angles of the rays reflecting at the sampling locations (e.g., in order to determine the reflectance and gain at that angle), the contribution due to the corresponding reflecting path can be determined. Through summing over many paths and possible reflectors, a sample for the full impulse response or power delay profile may be estimated from the transmission position 402 to the reception position 404.”), the simulated radio characteristics for the 3D scene (¶ [0009]: “determining the one or more characteristics of the wireless channel includes tracing a ray in the environment between the first position and the second position, and the outputs of the RF-RF model comprise metric corresponding to characteristics of interaction between RF signals and the environment at positions along the ray.”) based on configured parameters (e.g., ¶ [0067]: “process 200 including selecting a point pi in the environment (202). For example, as shown in FIG. 3, point pi is selected in an environment 300 in which a transmission point p tx and a reception point p rx are located.” ¶ [0067]: “when the RF-RF model is trained, the points p tx and p rx can correspond to known positions of the transmitter and receiver.” ¶ [0070]: “Sample points r ij = (x,y,z) ij along the i-th ray are selected.” ¶ [0071]: “sampled points along a first ray through p1 can include r1,1 and r1,2,” ¶ [0072]: “one or more angles associated with the ray” ¶ [0072]: “The point pi and the angles θi and ϕi, and/or encoded versions thereof, can be provided as input to the RF-RF model,” ¶ [0009]: “determining the one or more characteristics of the wireless channel includes tracing a ray in the environment between the first position and the second position, and the outputs of the RF-RF model comprise metric corresponding to characteristics of interaction between RF signals and the environment at positions along the ray.”) and at least one trainable parameter corresponding to a scene property (e.g., ¶ [0071]: “absorptions (e.g., values of absorption coefficients) α ij “; ¶ [0071]: “corresponding absorptions α1,1 and α1,2.”) (¶ [0009]: “determining the one or more characteristics of the wireless channel includes tracing a ray in the environment between the first position and the second position, and the outputs of the RF-RF model comprise metric corresponding to characteristics of interaction between RF signals and the environment at positions along the ray.” ¶ [0071]: “The points r ij are evaluated using a trained RF-RF model to determine corresponding absorptions (e.g., values of absorption coefficients) α ij (206). For example, the points r ij and/or encoded versions thereof, can be provided as input to the RF-RF model, which is trained to output α ij or an encoded version thereof. As shown in FIG. 3, sampled points along a first ray through p 1 can include r 1,1 and r 1,2, which can be processed by the RF-RF model to determine corresponding absorptions α1,1 and α1,2. The RF-RF model represents an encoding of the environment 300, including absorption characteristics of the environment 300, such that the absorptions α ij represent predicted degrees of RF absorption at the corresponding points r ij. In some implementations, values of transmittance metrics are determined instead of or in addition to absorptions; it will be understood that the determination or output of one is equivalent to determination or output of the other, because they are related by transmittance=1-absorption.” ¶ [0072]: “In addition, one or more angles associated with the ray are used with the RF-RF model to determine a reflectance (e.g., a reflection coefficient) ρ i associated with the ray (208). For example, as shown in FIG. 3, the ray through p i is associated with a first angle θ1 characterizing the direction of the first segment du1 and a second angle ϕ1 characterizing the direction of the second segment dv1, and the RF-RF model can be used to determine (e.g., a value of) a corresponding reflectance ρ1. The angles θ i and ϕ i can be multi-dimensional angles, e.g., two-dimensional values such as unit vectors in 3D space. The angles θ i and ϕ i are included in the analysis because reflection is a direction-dependent parameter. The point p i and the angles θ i and ϕ i, and/or encoded versions thereof, can be provided as input to the RF-RF model, which is trained to output ρ i or an encoded version thereof. The RF-RF model represents an encoding of the environment 300, including reflection characteristics of the environment 300, such that the reflectance ρ i represents a degree of reflection from p I toward p rx for RF signals from p tx. Notably, the reflectance ρ i can be learned without assuming the applicability of Snell's law or other material and structure-specific assumptions.” ¶ [0050]: “In addition, ray tracing has been employed to attempt to simulate radio wave propagation and reflection throughout an environment. Ray tracing modeling typically relies on a geometric model for an environment, e.g., a locale, geographic region, or facility. For example, the environment can be physically mapped, such as using Lidar-based 3D mapping, to obtain a 3D model of the environment, including terrain, buildings, obstacles, etc. Ray propagation through the 3D model can then be simulated (e.g., using material-based look ups for approximate RF properties of features in the 3D model) to predict wireless channel characteristics.”),
wherein for each ray intersection point in the 3D scene (¶ [0070]: “Sample points rij = (x,y,z)ij along the i-th ray”; ¶ [0071]: “points rij“; ¶ [0071]: “sampled points along a first ray through p1 can include r 1,1 and r 1,2.“ ¶ [0072]: “The point p i and the angles θ i and ϕ I”), a neural component (e.g., ¶ [0071]: “The points rij are evaluated using a trained RF-RF model”; ¶ [0081]: “Various types of machine learning models and machine learning architectures can be used to implement the RF-RF models” ¶ [0081]: “FIG. 5 illustrates a non-limiting example of such an architecture, showing an RF-RF model 500 that includes two concatenated multilayer perceptron (MLP) networks (training of the RF-RF model 500 is discussed below in reference to FIG. 6).”) estimates the at least one trainable parameter (¶ [0015]: “the RF-RF model includes: a first model trained to output absorptions or transmittances associated with a plurality of positions in the environment; and a second model trained to output reflectances associated with the plurality of positions in the environment.” “¶ [0071]: “The points rij are evaluated using a trained RF-RF model to determine corresponding absorptions (e.g., values of absorption coefficients) α ij (206).” ¶ [0072]: “The point p i and the angles θ i and ϕ i, and/or encoded versions thereof, can be provided as input to the RF-RF model, which is trained to output ρ i“) (¶ [0009]: “determining the one or more characteristics of the wireless channel includes tracing a ray in the environment between the first position and the second position, and the outputs of the RF-RF model comprise metric corresponding to characteristics of interaction between RF signals and the environment at positions along the ray.” ¶ [0071]: “The points r ij are evaluated using a trained RF-RF model to determine corresponding absorptions (e.g., values of absorption coefficients) α ij (206). For example, the points r ij and/or encoded versions thereof, can be provided as input to the RF-RF model, which is trained to output α ij or an encoded version thereof. As shown in FIG. 3, sampled points along a first ray through p 1 can include r 1,1 and r 1,2, which can be processed by the RF-RF model to determine corresponding absorptions α1,1 and α1,2. The RF-RF model represents an encoding of the environment 300, including absorption characteristics of the environment 300, such that the absorptions α ij represent predicted degrees of RF absorption at the corresponding points r ij. In some implementations, values of transmittance metrics are determined instead of or in addition to absorptions; it will be understood that the determination or output of one is equivalent to determination or output of the other, because they are related by transmittance=1-absorption.” ¶ [0072]: “In addition, one or more angles associated with the ray are used with the RF-RF model to determine a reflectance (e.g., a reflection coefficient) ρ i associated with the ray (208). For example, as shown in FIG. 3, the ray through p i is associated with a first angle θ1 characterizing the direction of the first segment du1 and a second angle ϕ1 characterizing the direction of the second segment dv1, and the RF-RF model can be used to determine (e.g., a value of) a corresponding reflectance ρ1. The angles θ i and ϕ i can be multi-dimensional angles, e.g., two-dimensional values such as unit vectors in 3D space. The angles θ i and ϕ i are included in the analysis because reflection is a direction-dependent parameter. The point p i and the angles θ i and ϕ i, and/or encoded versions thereof, can be provided as input to the RF-RF model, which is trained to output ρ i or an encoded version thereof. The RF-RF model represents an encoding of the environment 300, including reflection characteristics of the environment 300, such that the reflectance ρ i represents a degree of reflection from p I toward p rx for RF signals from p tx. Notably, the reflectance ρ i can be learned without assuming the applicability of Snell's law or other material and structure-specific assumptions.” ¶ [0073]: “The process 200 further includes determining a power level Pi for RF transmission from ptx to prx along the i-th ray (210). As an example of this calculation, in some implementations, Pi can be calculated as Pi = Ptx Ti Li ρi, where Ptx is the transmission power, Ti is the transmittance along the i-th ray, Li is the path loss along the i-th ray, and ρi is the reflectance associated with the i-th ray, as described above. The inclusion of Ptx in the foregoing expression permits the estimation of absolute received power levels, e.g., in addition to (in some cases) relative/proportional received power levels. Moreover, in some implementations, the Ptx term is used to include directivity/angular power of emission in the expression, e.g., where different emission angles have different corresponding Ptx.” ¶ [0075]: “Another parameter of relevance is the time-of-arrival associated with the i-th ray,” ¶ [0075]: “However, for purposes of this disclosure, it has been recognized that temporal factors should be integrated into radiance field modeling for channel estimation, e.g., for estimation of time-domain characteristics such as impulse responses, PDPs, interference estimation, and other channel effects.” ¶ [0075]: “The systems and devices described herein can be configured to generate PDPs and other time-domain channel characteristics using RF-RF models for useful and efficient RF analysis.” Also. see ¶ [0076]-[0077]. ¶ [0081]: “Various types of machine learning models and machine learning architectures can be used to implement the RF-RF models described herein. FIG. 5 illustrates a non-limiting example of such an architecture, showing an RF-RF model 500 that includes two concatenated multilayer perceptron (MLP) networks (training of the RF-RF model 500 is discussed below in reference to FIG. 6). A material MLP 502 is trained to receive, as input, positions 508 and/or positional embeddings 506 (an example of encoded positions), and to output (directly or in encoded form) absorptions α. For example, the density MLP 502 can be used to determine the absorptions αij in operation 206 discussed above, e.g., by receiving, as inputs, positions along traced rays. The material MLP 502 can be referred to as a “material” MLP because, in some implementations, the material MLP 502 encodes the material properties for different points in space (for example, absorption and/or reflectivity). A reflection MLP 504 is trained to receive, as input, directions of arrival θ and/or direction of departure ϕφ(512 and 514), along with output 518 of the material MLP 502, and to output (directly or in encoded form) reflectances ρ. For example, the reflection MLP 504 can be used to determine the reflectances ρi in operation 208 discussed above, e.g., by receiving, as inputs, positions pi that define traced rays. In some implementations, the concatenated processing by the MLPs 502, 504 can be understood as the material MLP 502 outputting (based on embedding in the material MLP 502) properties of a material at the positions 508 (e.g., reflectivity, absorption, material/object orientation, and/or one or more other properties), and the output 518 is then conditioned on the direction(s) θ and/or ϕ, which represent angle of arrival (AOA) and angle of departure (AOD) using the reflection MLP 504.” ¶ [0050]: “In addition, ray tracing has been employed to attempt to simulate radio wave propagation and reflection throughout an environment. Ray tracing modeling typically relies on a geometric model for an environment, e.g., a locale, geographic region, or facility. For example, the environment can be physically mapped, such as using Lidar-based 3D mapping, to obtain a 3D model of the environment, including terrain, buildings, obstacles, etc. Ray propagation through the 3D model can then be simulated (e.g., using material-based look ups for approximate RF properties of features in the 3D model) to predict wireless channel characteristics.” ¶ [0104]: “the RF-RF model is trained to estimate channel characteristics (e.g., channel impulse response, PDP, etc.) for transmission between any two positions in the environment in which the receiver 602 and transmitter 604 are located.” ); and
updating weights applied by the neural component (e.g., ¶ [0022]: “training the RF-RF model.” During training, parameters (such as weights, hyperparameters, coefficients, and/or the like) of the RF-RF model, such as parameters of the MLPs 502, 504 (and including, in some implementations, parameters of a positional embedding module such as learned positioning embedding module 510, parameters of a voxel representation of the environment, etc.) can be adjusted“ ¶ [0120]: “updates to the RF-RF model (e.g., model weights and/or weight update gradients)”) to estimate the at least one trainable parameter (e.g., ¶ [0022]: “one or more characteristics of the second wireless channel that are estimated by the RF-RF model”) using gradient-based optimization (¶ [0086]: “The RF-RF model is trained to learn a series of basis coefficients (e.g., through a gradient descent or other training approach, as described with respect to operation 616 below),” ¶ [0107]: “During training, parameters (such as weights, hyperparameters, coefficients, and/or the like) of the RF-RF model, such as parameters of the MLPs 502, 504 (and including, in some implementations, parameters of a positional embedding module such as learned positioning embedding module 510, parameters of a voxel representation of the environment, etc.) can be adjusted to decrease/minimize a value of a loss function based on differences between (i) outputs of the RF-RF model during training, e.g., outputs obtained according to the process 200 discussed above, where the outputs are obtained using the inputs of the training data, and (ii) the channel characteristics of the training data.” That is, the RF-RF model can be trained based on actual RF measurements, e.g., actual measurement data of a channel impulse response or a measured PDP, in comparison to predicted values. When the loss function between estimated values and measured values (e.g., estimated PDPs and measured PDPs) is computed, the estimation is differentiable (e.g., based on the equations provided above in reference to FIG. 2), such that gradients of the loss can be used to update the parameters of the RF-RF model.” ¶ [0103]: “gradient descent or other optimization can be performed to optimize one or more estimation variables”) to minimize a loss function (e.g., ¶ [0022]: “training the RF-RF model based on a difference between (i) the determined one or more characteristics of the second wireless channel and (ii) one or more characteristics of the second wireless channel that are estimated by the RF-RF model based on the first known position and the second known position.” ¶ [0107]:” to decrease/minimize a value of a loss function”) of the simulated radio characteristics (e.g., ¶ [0022]: “(ii) one or more characteristics of the second wireless channel that are estimated by the RF-RF model” ¶ [0107]: “(i) outputs of the RF-RF model during training, e.g., outputs obtained according to the process 200 discussed above, where the outputs are obtained using the inputs of the training data” ¶ [0107]: “predicted values”) and the reference radio characteristics (e.g., ¶ [0022]: “(i) the determined one or more characteristics of the second wireless channel” ¶ [0099]: “The characteristics of the wireless channel can include, for example, a channel tensor H, a PDP, an impulse response, and/or another estimate of how RF signals propagate from the transmitter 604 to the receiver 602. These estimates are “measured” estimates based on measured/collected RF data, as opposed to outputs of the RF-RF model which are predictive estimates.” ¶ [0104]: “the channel characteristics (e.g., directly from the receiver 602 or retrieved from a remote storage/system) and respective positions ptx, Prx of the receiver 602 and transmitter 604 are used to train or retrain an RF-RF model (616). For example, the positions ptx, Prx and the determined channel characteristics can be used as training data, with the determined channel characteristics being ground-truth labels for the positions ptx, Prx . It will be understood that the training data also includes many other sets of training data and labels corresponding to other receiver and/or transmitter positions and corresponding channel characteristics.” ¶ [0107]: “and (ii) the channel characteristics of the training data.” ¶ [0107]: “actual RF measurements, e.g., actual measurement data of a channel impulse response or a measured PDP, in comparison to predicted values.”) (¶ [0022]: “training the RF-RF model. Training the RF-RF model includes: receiving an RF signal, at a first known position in the environment, from an emitter at a second known position in the environment; determining, based on the received RF signal, one or more characteristics of a second wireless channel between the first known position and the second known position; and training the RF-RF model based on a difference between (i) the determined one or more characteristics of the second wireless channel and (ii) one or more characteristics of the second wireless channel that are estimated by the RF-RF model based on the first known position and the second known position.” ¶ [0027]: “receiving a radio frequency (RF) signal, at a first known position in an environment, from an emitter at a second known position in the environment; determining, based on the received RF signal, one or more characteristics of a wireless channel between the first known position and the second known position; and training a radio-frequency radiance-field (RF-RF) model based on a difference between (i) the determined one or more characteristics of the second wireless channel and (ii) one or more characteristics of the wireless channel that are estimated by the RF-RF model based on the first known position and the second known position.” ¶ [0107]: “During training, parameters (such as weights, hyperparameters, coefficients, and/or the like) of the RF-RF model, such as parameters of the MLPs 502, 504 (and including, in some implementations, parameters of a positional embedding module such as learned positioning embedding module 510, parameters of a voxel representation of the environment, etc.) can be adjusted to decrease/minimize a value of a loss function based on differences between (i) outputs of the RF-RF model during training, e.g., outputs obtained according to the process 200 discussed above, where the outputs are obtained using the inputs of the training data, and (ii) the channel characteristics of the training data. That is, the RF-RF model can be trained based on actual RF measurements, e.g., actual measurement data of a channel impulse response or a measured PDP, in comparison to predicted values. When the loss function between estimated values and measured values (e.g., estimated PDPs and measured PDPs) is computed, the estimation is differentiable (e.g., based on the equations provided above in reference to FIG. 2), such that gradients of the loss can be used to update the parameters of the RF-RF model.”).
Regarding claim 1, claim 1 is directed to the computer-implemented method implemented by the system of claim 20 and, as such, is rejected for the same reasons applied above in the rejection of claim 20.
Regarding claim 2 (depends on claim 1), CORGAN discloses:
wherein the neural component generates an embedding vector (e.g., ¶ [0081]: “positional embeddings 506”; ¶ [0082]: “the positional embeddings 506 can be higher-dimensional embeddings of the positions 508. In some implementations, the learned position embedding module 510 performs a transmitter- and receiver-independent vector mapping between the positions 508 (e.g., (x, y, z)) and a learned, higher-dimensional vector space”) used to estimate the at least one trainable parameter (¶ [0081]: “Various types of machine learning models and machine learning architectures can be used to implement the RF-RF models described herein. FIG. 5 illustrates a non-limiting example of such an architecture, showing an RF-RF model 500 that includes two concatenated multilayer perceptron (MLP) networks (training of the RF-RF model 500 is discussed below in reference to FIG. 6). A material MLP 502 is trained to receive, as input, positions 508 and/or positional embeddings 506 (an example of encoded positions), and to output (directly or in encoded form) absorptions α.” ¶ [0082]: “The RF-RF model 500 further includes a learned position embedding module 510 that is configured to receive, as inputs, positions 508 in the environment (e.g., positions of points pi and positions rij along rays) and to output positional embeddings 506 that represent the positions 508. For example, the positional embeddings 506 can be higher-dimensional embeddings of the positions 508. In some implementations, the learned position embedding module 510 performs a transmitter- and receiver-independent vector mapping between the positions 508 (e.g., (x, y, z)) and a learned, higher-dimensional vector space that encodes properties discovered through back-propagation from the material MLP 502. For example, the positional embeddings 506 can be n-dimensional, where n is an integer greater than 3. In some implementations, the use of the positional embeddings 506 can allow the RF-RF model 500 to better model abrupt changes or high-frequency features of the environment that occur along slowly-changing positional inputs.” ¶ [0083]: “The learned position embedding module 510 can be trained during training of the MLPs 502 and/or 504, e.g., by adjusting weights and/or other parameters of the position embedding module 510 that determine a mapping between the positions 508 and the positional embeddings 506. In some implementations, the learned position embedding module 510 is trained as a multiresolution hash encoder (to perform multiresolution hashing to generating the positional embeddings 506 as discussed below) or to generate the positional embeddings 506 based on a set of random Fourier features. The positions 508 can be provided as volumetric locations (x,y,z) or in another suitable coordinate system.”).
Regarding claim 3 (depends on claim 1), CORGAN discloses:
wherein each ray intersection point (e.g., ¶ [0082]: “positions 508 in the environment (e.g., positions of points pi and positions rij along rays)”) is encoded into a higher dimension space (e.g., ¶ [0081]: “positional embeddings 506 (an example of encoded positions),” ¶ [0082]: “positional embeddings 506 that represent the positions 508. For example, the positional embeddings 506 can be higher-dimensional embeddings of the positions 508.”) for input to the neural component (e.g., ¶ [0081]: “material MLP 502 is trained to receive, as input, positions 508 and/or positional embeddings 506 (an example of encoded positions),”) (¶ [0081]: “A material MLP 502 is trained to receive, as input, positions 508 and/or positional embeddings 506 (an example of encoded positions), and to output (directly or in encoded form) absorptions α.” ¶ [0082]: “The RF-RF model 500 further includes a learned position embedding module 510 that is configured to receive, as inputs, positions 508 in the environment (e.g., positions of points pi and positions rij along rays) and to output positional embeddings 506 that represent the positions 508. For example, the positional embeddings 506 can be higher-dimensional embeddings of the positions 508. In some implementations, the learned position embedding module 510 performs a transmitter- and receiver-independent vector mapping between the positions 508 (e.g., (x, y, z)) and a learned, higher-dimensional vector space that encodes properties discovered through back-propagation from the material MLP 502. For example, the positional embeddings 506 can be n-dimensional, where n is an integer greater than 3. In some implementations, the use of the positional embeddings 506 can allow the RF-RF model 500 to better model abrupt changes or high-frequency features of the environment that occur along slowly-changing positional inputs.” ¶ [0083]: “The learned position embedding module 510 can be trained during training of the MLPs 502 and/or 504, e.g., by adjusting weights and/or other parameters of the position embedding module 510 that determine a mapping between the positions 508 and the positional embeddings 506. In some implementations, the learned position embedding module 510 is trained as a multiresolution hash encoder (to perform multiresolution hashing to generating the positional embeddings 506 as discussed below) or to generate the positional embeddings 506 based on a set of random Fourier features. The positions 508 can be provided as volumetric locations (x,y,z) or in another suitable coordinate system.”).
Regarding claim 4 (depends on claim 1), CORGAN discloses:
wherein each ray intersection point is encoded (e.g., a given position (e.g., ¶ [0084]: “ptx, prx, pi, and/or rij) can be encoded”) using a multiresolution hash grid (e.g., ¶ [0083]: “a multiresolution hash encoder (to perform multiresolution hashing to generating the positional embeddings 506 as discussed below)” ¶ [0084]: “a multiresolution hash encoder as the learned position embedding module 510”) for input to the neural component (e.g., ¶ [0081]: “material MLP 502 is trained to receive, as input, positions 508 and/or positional embeddings 506 (an example of encoded positions),”) (¶ [0083]: “The learned position embedding module 510 can be trained during training of the MLPs 502 and/or 504, e.g., by adjusting weights and/or other parameters of the position embedding module 510 that determine a mapping between the positions 508 and the positional embeddings 506. In some implementations, the learned position embedding module 510 is trained as a multiresolution hash encoder (to perform multiresolution hashing to generating the positional embeddings 506 as discussed below) or to generate the positional embeddings 506 based on a set of random Fourier features. The positions 508 can be provided as volumetric locations (x,y,z) or in another suitable coordinate system.” ¶ [0084]: “In some implementations, the learned position embedding module 510 is configured as a multiresolution hash encoder, e.g., as set forth by Muller et al. in “Instant Neural Graphics Primitives with a Multiresolution Hash Encoding” (2022). For example, the environment can be represented in voxel form, and a given position (e.g., ptx, prx, pi, and/or rij) can be encoded based on a linear interpolation of feature vectors corresponding to corners of surrounding voxels. Parameters of the voxels and encoding can be determined through training/back-propagation in operation 616 discussed below. In some implementations, the use of a multiresolution hash encoding can significantly speed up training and estimation. In some implementations, a multiresolution hash encoder as the learned position embedding module 510 can be configured to output spherical harmonic basis coefficients which are processed by the MLPs 502 and/or 504 to produce absorption and reflection as a function of the coefficients and angles θ i and ϕ i. For example, the spherical harmonic basis coefficients can be associated with the absorption coefficient and/or the reflection coefficient, such that an output of the learned position embedding module 510 can output absorption and/or reflection as a function of one or both of θ and/or ϕ, e.g., as opposed to conditioning a trained model output on θ and/or ϕ as shown for MLP 504. In some implementations, the RF-RF model 500 does not include a material MLP 502. For example, the positional embedding (e.g. the multiresolution hash encoding, or other embedding) may directly output properties like absorption or reflection coefficient for one specific location in space. Various embeddings can be used by the learned position embedding module 510 in various implementations, for example, using random Fourier features, to provide a non-limiting example.” ¶ [0131]: “The process 800 includes executing an RF-RF model characterizing an environment (802). For example, the RF-RF model can be a neural RF-RF model including one or more MPLs, a Gaussian splat-based radiance field model, an “instant” RF-RF model incorporating a learned multiresolution hash encoder, etc. The RF-RF model can be executed as described in reference to FIGS. 2-3, e.g., by providing, as inputs to the RF-RF model, positions in the environment, and obtaining, as outputs of the RF-RF model, corresponding parameters such as absorption, transmission, reflection, etc. For example, executing the RF-RF model can include providing, as input to the RF-RF model, positions along a ray in the environment and, in some implementations, additional information such as RF signal frequency, weather information, time, etc., and obtaining, as output of the RF-RF model, absorptions and reflections corresponding to the positions or the ray.”).
Regarding claim 5 (depends on claim 1), CORGAN discloses:
wherein each ray intersection point (e.g., ¶ [0084]: “a given position (e.g., ptx, prx, pi, and/or rij)”) is normalized to a unit cube (e.g., ¶ [0084]: “the environment can be represented in voxel form,” Note: Voxels are unit cubes. ¶ [0084]: “encoded based on a linear interpolation of feature vectors corresponding to corners of surrounding voxels.”) and encoded into a higher dimension space ( e.g., ¶ [0082]: “the positional embeddings 506 can be higher-dimensional embeddings”) for input to the neural component (e.g., ¶ [0081]: “material MLP 502 is trained to receive, as input, positions 508 and/or positional embeddings 506 (an example of encoded positions),”) (¶ [0082]: “The RF-RF model 500 further includes a learned position embedding module 510 that is configured to receive, as inputs, positions 508 in the environment (e.g., positions of points pi and positions rij along rays) and to output positional embeddings 506 that represent the positions 508. For example, the positional embeddings 506 can be higher-dimensional embeddings of the positions 508. In some implementations, the learned position embedding module 510 performs a transmitter- and receiver-independent vector mapping between the positions 508 (e.g., (x, y, z)) and a learned, higher-dimensional vector space that encodes properties discovered through back-propagation from the material MLP 502. For example, the positional embeddings 506 can be n-dimensional, where n is an integer greater than 3. In some implementations, the use of the positional embeddings 506 can allow the RF-RF model 500 to better model abrupt changes or high-frequency features of the environment that occur along slowly-changing positional inputs.” ¶ [0084]: “In some implementations, the learned position embedding module 510 is configured as a multiresolution hash encoder, e.g., as set forth by Muller et al. in “Instant Neural Graphics Primitives with a Multiresolution Hash Encoding” (2022). For example, the environment can be represented in voxel form, and a given position (e.g., ptx, prx, pi, and/or rij) can be encoded based on a linear interpolation of feature vectors corresponding to corners of surrounding voxels. Parameters of the voxels and encoding can be determined through training/back-propagation in operation 616 discussed below. In some implementations, the use of a multiresolution hash encoding can significantly speed up training and estimation. In some implementations, a multiresolution hash encoder as the learned position embedding module 510 can be configured to output spherical harmonic basis coefficients which are processed by the MLPs 502 and/or 504 to produce absorption and reflection as a function of the coefficients and angles θ i and ϕ i. For example, the spherical harmonic basis coefficients can be associated with the absorption coefficient and/or the reflection coefficient, such that an output of the learned position embedding module 510 can output absorption and/or reflection as a function of one or both of θ and/or ϕ, e.g., as opposed to conditioning a trained model output on θ and/or ϕ as shown for MLP 504. In some implementations, the RF-RF model 500 does not include a material MLP 502. For example, the positional embedding (e.g. the multiresolution hash encoding, or other embedding) may directly output properties like absorption or reflection coefficient for one specific location in space. Various embeddings can be used by the learned position embedding module 510 in various implementations, for example, using random Fourier features, to provide a non-limiting example.” ¶ [0131]: “The process 800 includes executing an RF-RF model characterizing an environment (802). For example, the RF-RF model can be a neural RF-RF model including one or more MPLs, a Gaussian splat-based radiance field model, an “instant” RF-RF model incorporating a learned multiresolution hash encoder, etc. The RF-RF model can be executed as described in reference to FIGS. 2-3, e.g., by providing, as inputs to the RF-RF model, positions in the environment, and obtaining, as outputs of the RF-RF model, corresponding parameters such as absorption, transmission, reflection, etc. For example, executing the RF-RF model can include providing, as input to the RF-RF model, positions along a ray in the environment and, in some implementations, additional information such as RF signal frequency, weather information, time, etc., and obtaining, as output of the RF-RF model, absorptions and reflections corresponding to the positions or the ray.”).
Regarding claim 6 (depends on claim 1), CORGAN discloses:
wherein each ray intersection point (¶ [0072]: “The point p i” ) comprises at least one angle relative to a surface in the 3D scene at the intersection point (¶ [0072]: “The point p i and the angles θ i and ϕ I” ) (¶ [0072]: “The point pi and the angles θi and ϕi, and/or encoded versions thereof, can be provided as input to the RF-RF model,” ¶ [0072]: “In addition, one or more angles associated with the ray are used with the RF-RF model to determine a reflectance (e.g., a reflection coefficient) ρ i associated with the ray (208). For example, as shown in FIG. 3, the ray through p i is associated with a first angle θ1 characterizing the direction of the first segment du1 and a second angle ϕ1 characterizing the direction of the second segment dv1, and the RF-RF model can be used to determine (e.g., a value of) a corresponding reflectance ρ1. The angles θ i and ϕ i can be multi-dimensional angles, e.g., two-dimensional values such as unit vectors in 3D space. The angles θ i and ϕ i are included in the analysis because reflection is a direction-dependent parameter. The point p i and the angles θ i and ϕ i, and/or encoded versions thereof, can be provided as input to the RF-RF model, which is trained to output ρ i or an encoded version thereof. The RF-RF model represents an encoding of the environment 300, including reflection characteristics of the environment 300, such that the reflectance ρ i represents a degree of reflection from p I toward prx for RF signals from ptx. Notably, the reflectance ρ i can be learned without assuming the applicability of Snell's law or other material and structure-specific assumptions.”).
Regarding claim 7 (depends on claim 1), CORGAN discloses:
updating the weights (e.g., ¶ [0088]: “set of coefficients”, “the coefficients can be modified during the training”) to modify at least one of a meta material, a reconfigurable intelligent surface, an antenna pattern (¶ [0088]: “In some implementations, the RF-RF model can be trained to produce different outputs for different emitters and/or receivers. For example, each UE, base station, etc., can have an antenna pattern that can be learned and encoded in the RF-RF model, to predict propagation from p.sub.tx to p.sub.rx of signals emitted by and/or received by a particular UE or base station, or type/model of UE or base station. An identifier of the emitter and/or receiver can be provided in the additional input 516 so that the RF-RF model provides emitter and/or receiver-specific outputs, e.g., absorption and reflection, to result in emitter and/or receiver-specific channel characteristics such as PDP. In some implementations, to configure the RF-RF model in this manner, training (in operation 616 discussed below) can start with an initial set of coefficients representing an emitter's or receiver's antenna pattern (e.g., start by assuming spherically-uniform transmission/reception or assuming a standard pattern for a type of an antenna, such as a 120-degree base station sector antenna), and the coefficients can be modified during the training (based on back-propagation) to reduce loss and produce an estimate for the emitter's and/or receiver's antenna pattern.”), an antenna orientation, and an antenna position.
Regarding claim 8 (depends on claim 1), CORGAN discloses:
wherein the configured parameters (e.g., ¶ [0089]: “the antenna pattern is known (e.g., for a known emitter) and can be used directly, e.g., as an input for predicting channel characteristics for emission from the antenna.”) or the at least one trainable parameter include one or more of scene geometry, configuration of reconfigurable intelligent surfaces and meta materials, antenna patterns (e.g., ¶ [0089]: “the antenna pattern is known (e.g., for a known emitter) and can be used directly, e.g., as an input for predicting channel characteristics for emission from the antenna.”), array geometries, and transmitter and receiver orientations and positions (¶ [0089]: “The learned antenna pattern can be encoded in weights of the RF-RF model. In some implementations, the antenna pattern is known (e.g., for a known emitter) and can be used directly, e.g., as an input for predicting channel characteristics for emission from the antenna. The antenna pattern need not be (though can be) incorporated directly into the architecture of the RF-RF model.”).
Regarding claim 9 (depends on claim 1), CORGAN discloses:
wherein the scene property comprises at least one of relative permittivity, reflection coefficients (¶ [0072]: “In addition, one or more angles associated with the ray are used with the RF-RF model to determine a reflectance (e.g., a reflection coefficient) ρ i associated with the ray (208).” ), transmission coefficients (¶ [0071]: “The points r ij are evaluated using a trained RF-RF model to determine corresponding absorptions (e.g., values of absorption coefficients) α ij (206). For example, the points r ij and/or encoded versions thereof, can be provided as input to the RF-RF model, which is trained to output α ij or an encoded version thereof. As shown in FIG. 3, sampled points along a first ray through p 1 can include r 1,1 and r 1,2, which can be processed by the RF-RF model to determine corresponding absorptions α1,1 and α1,2. The RF-RF model represents an encoding of the environment 300, including absorption characteristics of the environment 300, such that the absorptions α ij represent predicted degrees of RF absorption at the corresponding points r ij. In some implementations, values of transmittance metrics are determined instead of or in addition to absorptions; it will be understood that the determination or output of one is equivalent to determination or output of the other, because they are related by transmittance = 1-absorption.” ), conductivity, effective roughness, and permeability of object surfaces and scattering functions (¶ [0099]: “For example, the determined characteristics of the wireless channel can represent the effects of one or more of scattering, fading, or power decay with distance, e.g., the characteristics of the wireless channel can include a PDP or other CSI.”).
Regarding claim 12 (depends on claim 1), CORGAN discloses:
wherein the differentiable ray tracer computes paths of electromagnetic waves (¶ [0050]: “ray tracing has been employed to attempt to simulate radio wave propagation and reflection throughout an environment.” ¶ [0062]: “used with differentiable ray-tracing methods to estimate RF channel parameters and, in this manner, control RF transmission/reception.” ¶ [0065]: “process 200 is a non-limiting example of the types of ray-tracing-based processes, within the scope of this disclosure, that can be performed using RF-RF models” ¶ [0069]: “A ray (e.g., an i-th ray) is traced between the transmission point ptx and the reception point prx through the selected point pi (204).”).
Regarding claim 13 (depends on claim 1), CORGAN discloses:
wherein the simulated radio characteristics estimate qualities of a transmitted electromagnetic wave at a receiver (¶ [0132]: “The process 800 further includes determining, based on outputs of the RF-RF model, one or more characteristics of a wireless channel between a first position and a second position in the environment (804). For example, the first position and the second position can be positions between which rays are traced, and positions on the rays can be provided as input to the RF-RF model in operation 802. Determining the one or more characteristics of the wireless channel can be performed as described in reference to FIG. 2, e.g., operations 210 and 212 of FIG. 2. For example, the one or more characteristics can include a PDP, impulse response, transmitted energy for the wireless channel, and/or other CSI for transmission between the first position and the second position, and the characteristics can be determined by computing time-of-arrival-dependent rendering functions based on transmitted power levels for rays traced through the environment based on RF-RF outputs. In some implementations, as discussed below in reference to FIGS. 9-10, the one or more characteristics include key performance indicators (KPI) that characterize a quality of the wireless transmission. KPIs can be determined, for example, based on PDPs determined based on the model outputs, and/or based on the model outputs without intermediate computation of PDPs.” ¶ [0132]: “RF-RF model-based processes according to this disclosure can predict channel characteristics for signal scenarios not previously recorded (e.g., for different Prx and/or ptx, for a different frequency, for different modulation, for different time/weather, for different beam pattern, etc.).” ).
Regarding claim 14 (depends on claim 1), CORGAN discloses:
wherein the simulated radio characteristics comprise one or more of channel impulse responses (¶ [0132]: “impulse response” ), channel frequency responses, path delays, path losses, angles of arrival ( ), angles of departure (¶ [0127]: “A Gaussian splatting RF-RF model trained as a relightable model can encode a function that maps angle of arrival to angle of departure, and reflected intensity, for each splat.”), amplitudes (¶ [0127]: “A Gaussian splatting RF-RF model trained as a relightable model can encode a function that maps angle of arrival to angle of departure, and reflected intensity, for each splat.”), powers, delay spread, Doppler spread, angular spread, power-delay-angular profile (¶ [0132]: “a PDP”, ¶ [0008]: “a power delay profile (PDP)”), and a number of paths (¶ [0132]: “The process 800 further includes determining, based on outputs of the RF-RF model, one or more characteristics of a wireless channel between a first position and a second position in the environment (804). For example, the first position and the second position can be positions between which rays are traced, and positions on the rays can be provided as input to the RF-RF model in operation 802. Determining the one or more characteristics of the wireless channel can be performed as described in reference to FIG. 2, e.g., operations 210 and 212 of FIG. 2. For example, the one or more characteristics can include a PDP, impulse response, transmitted energy for the wireless channel, and/or other CSI for transmission between the first position and the second position, and the characteristics can be determined by computing time-of-arrival-dependent rendering functions based on transmitted power levels for rays traced through the environment based on RF-RF outputs. In some implementations, as discussed below in reference to FIGS. 9-10, the one or more characteristics include key performance indicators (KPI) that characterize a quality of the wireless transmission. KPIs can be determined, for example, based on PDPs determined based on the model outputs, and/or based on the model outputs without intermediate computation of PDPs.” ¶ [0132]: “RF-RF model-based processes according to this disclosure can predict channel characteristics for signal scenarios not previously recorded (e.g., for different Prx and/or ptx, for a different frequency, for different modulation, for different time/weather, for different beam pattern, etc.).”).
Regarding claim 15 (depends on claim 1), CORGAN discloses:
wherein the reference radio characteristics are measurements taken at different locations in the 3D scene (¶ [0099]: “One or more characteristics (sometimes referred to as “channel estimates” or as “channel state information” (CSI)) of the wireless channel between the transmitter 604 and the receiver 602 are determined based on characteristics of the received signal 606 (614), for example, based on the sounding signal, reference tone, and/or other predetermined signal/sequence in the received signal 606 (e.g., estimated based on a PUSCH, DMRS, or PBCH, a data structure corresponding to those protocols/signal types, etc.). The characteristics of the wireless channel can include, for example, a channel tensor H, a PDP, an impulse response, and/or another estimate of how RF signals propagate from the transmitter 604 to the receiver 602. These estimates are “measured” estimates based on measured/collected RF data, as opposed to outputs of the RF-RF model which are predictive estimates. For example, the determined characteristics of the wireless channel can represent the effects of one or more of scattering, fading, or power decay with distance, e.g., the characteristics of the wireless channel can include a PDP or other CSI. The characteristics of the wireless channel can be determined, for example, in the time domain and/or in the frequency domain.” ¶ [0113]: “RF-RF model training can be performed based only on RF captures,” ¶ [0114]: “Capture of RF data for training/retraining can be performed in an operations-integrated manner (e.g., in which network devices/systems such as UE and base stations capture training data during standard network operation) and/or in special-purpose operations. As an example of the latter, a reference signal receiver can be driven through an environment to capture accurate location and impulse response (PDP) data (from one or multiple emitters), obtaining the received signals and corresponding signals used for training. Other data can be collected and/or determined in conjunction with reception of reference signals, to be used as the “other information” shown in FIG. 6 to train RF-RF models that receive additional inputs for more-contextually-specific channel estimation. For example, telemetry about the location of the receiver can be collected (e.g. GPS coordinates that provide prx for training data, velocity, heading, timing, precision), and/or other relevant data such as environmental factors, e.g., weather, temperature, humidity, etc. The ground-truth channel estimates can include, for example, received channel state information (CSI) such as frequency-domain channel estimators on OFDM or other basis functions, time-domain channel impulse response estimates, PDP, delay-Doppler spectrum estimates, and/or other representations of the channel response for signals received at the receiver.”).
Regarding claim 16 (depends on claim 1), CORGAN discloses:
wherein at least one of the steps of computing or updating is performed on a server or in a data center (¶ [0177]: “FIG. 15 is a diagram illustrating an example of a computing system that may be used to implement one or more components of a system that utilizes RF-RF models for RF system operations. The computer system illustrated in FIG. 15 can be, or can include, one or more of the network devices and modules described herein, e.g., UE, DU, RU, CU, and cloud computing system, for example in any of systems 100, 600, and/or 700. The computer system illustrated in FIG. 15, and/or a component or portion thereof, can be used to perform any of the processes described herein, such as processes 200, 610, 800, 900, 1000, 1100, 1200, and/or 1300.” ¶ [0178]: “The computing system includes computing device 1500 and a mobile computing device 1550 that can be used to implement the techniques described herein. For example, either or both of the computing device 1500 and the mobile computing device 1550 can execute an RF-RF model for RF communication control and/or other purposes.” ¶ [0179]: “The computing device 1500 is intended to represent various forms of digital computers and network components, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, cloud computing systems, base stations, mainframes, back-end network equipment, and other appropriate computers.”)
and the simulated radio characteristics (e.g., ¶ [0111]: “the trained RF-RF model”) or an image generated from the simulated radio characteristics is streamed to a user device (e.g., ¶ [0111]: “deployed (620), e.g., onto any one or more devices/systems of a radio network, such as in UE”) (¶ [0111]: “Referring again to the process 610 of FIG. 6, the trained RF-RF model can be deployed (620), e.g., onto any one or more devices/systems of a radio network, such as in UE, in a base station (e.g., a gNB), in a DU, in a CU, in a RIC application, in an MEC system, in a cloud computing system for the radio network, etc. For example, the weights and other parameters that represent the trained RF-RF model can be stored in one or more of these devices/systems. Once deployed, the trained RF-RF model can be executed (by the device/system on which the trained RF-RF model is deployed) for channel estimation (e.g., as described with respect to FIG. 2) and used for various purposes, e.g., for RF transmission/reception control, RF network design, localization, etc. Examples of utilization of deployed RF-RF models are described below with respect to FIGS. 8-13.” ¶ [0131]:”The process 800 includes executing an RF-RF model characterizing an environment (802). For example, the RF-RF model can be a neural RF-RF model including one or more MPLs, a Gaussian splat-based radiance field model, an “instant” RF-RF model incorporating a learned multiresolution hash encoder, etc. The RF-RF model can be executed as described in reference to FIGS. 2-3, e.g., by providing, as inputs to the RF-RF model, positions in the environment, and obtaining, as outputs of the RF-RF model, corresponding parameters such as absorption, transmission, reflection, etc. For example, executing the RF-RF model can include providing, as input to the RF-RF model, positions along a ray in the environment and, in some implementations, additional information such as RF signal frequency, weather information, time, etc., and obtaining, as output of the RF-RF model, absorptions and reflections corresponding to the positions or the ray.” ¶ [0132]: “The process 800 further includes determining, based on outputs of the RF-RF model, one or more characteristics of a wireless channel between a first position and a second position in the environment (804). For example, the first position and the second position can be positions between which rays are traced, and positions on the rays can be provided as input to the RF-RF model in operation 802. Determining the one or more characteristics of the wireless channel can be performed as described in reference to FIG. 2, e.g., operations 210 and 212 of FIG. 2. For example, the one or more characteristics can include a PDP, impulse response, transmitted energy for the wireless channel, and/or other CSI for transmission between the first position and the second position, and the characteristics can be determined by computing time-of-arrival-dependent rendering functions based on transmitted power levels for rays traced through the environment based on RF-RF outputs. In some implementations, as discussed below in reference to FIGS. 9-10, the one or more characteristics include key performance indicators (KPI) that characterize a quality of the wireless transmission. KPIs can be determined, for example, based on PDPs determined based on the model outputs, and/or based on the model outputs without intermediate computation of PDPs. Whereas visual NeRF predicts an image of a scene from a previously-uncaptured perspective, RF-RF model-based processes according to this disclosure can predict channel characteristics for signal scenarios not previously recorded (e.g., for different P.sub.rx and/or p.sub.tx, for a different frequency, for different modulation, for different time/weather, for different beam pattern, etc.).” ¶ [0133]: “The process 800 further includes controlling, based on the one or more characteristics of the wireless channel, RF transmission between the first position and the second position (806). Controlling the transmission can include controlling scheduling, beamforming, modulation, pairing, resource allocation, etc., as discussed below in reference to specific examples. For example, optimized parameters can be selected from a set of candidate parameters based on the channel characteristics, and one or more RF devices (e.g., UE, network infrastructure systems, etc.) can be controlled to conform to the optimized parameters, e.g., as discussed below in reference to FIGS. 9-10.”).
Regarding claim 17 (depends on claim 1), CORGAN discloses:
wherein at least one of the steps of computing or updating is performed within a cloud computing environment (¶ [0055]: “The UE performs network communication using a RAN architecture that includes a radio unit (RU) 102; a RAN Intelligent Controller (RIC) 110 that can, in some implementations, host one or more xAPPs, dApps, rAPPs, and/or zAPPs; a distributed unit (DU) 112; a central unit (CU) 120; and a cloud service 114. “ ¶ [0059]: “The cloud service 114 represents back-end distributed computing functions. For example, the cloud service 114 can implement core network functions, IP Multimedia Subsystem (IMS) functions, and/or other network-related functions. The cloud service 114 can include, for example, an edge-cloud, a regional-cloud, and/or a national-cloud.” ¶ [0100]: “For example, the receiver 602 can send the signal or features thereof to a DU, a CU, a RIC App, or a cloud computing system, which performs the channel estimation.” ¶ [0102]: “As shown in FIG. 6, in some implementations, the determined channel characteristics are sent to another system and/or stored for later retrieval (618). For example, a channel tensor H and/or a PDP determined at the receiver 602 can be sent from the receiver 602 to a remote storage or system, such as the RIC 110, the cloud service 114, etc. In some implementations, other information to be used for training (e.g., the ptx and prx corresponding to the received signal) can also be sent. The stored data can later be retrieved and used for training.” ¶ [0108]: “Model training (operation 616) can be performed using one or more computing devices and/or systems. In some implementations, model training is performed at a base station, e.g., at the transmitter 604. In some implementations, model training is performed at a distributed unit (DU) of a RAN. In some implementations, training data such as aggregated ptx and prx, and PDP are sent (e.g., from receiver 602 such as UE, and/or from transmitter 604 such as a base station) to another service/layer such as a base station, a RAN intelligent controller (RIC) xApp, rApp, or other application, a multi-access edge computing (MEC) system, or a cloud computing system, which performs the training. In some implementations, the RIC, MEC system, and/or cloud computing system receives data (e.g., and stores the data) in operation 618 and uses the received data for training. In some implementations, model training is performed at UE.”).
Regarding claim 18 (depends on claim 1), CORGAN discloses:
wherein at least one of the steps of computing or updating is performed for training, testing, or certifying a neural network (e.g., ¶ [0120]: “updates to the RF-RF model (e.g., model weights and/or weight update gradients)”) employed in a machine (e.g., ¶ [0120]: “transmitted to one or more other devices/systems, such as another UE 702,”), robot, or autonomous vehicle (e.g., ¶ [0172]: “an autonomous vehicle, such as a drone or self-driving car.”) (¶ [0120]: “One or more resulting updates to the RF-RF model (e.g., model weights and/or weight update gradients) can then be transmitted to one or more other devices/systems, such as another UE 702, an infrastructure system 704, and/or a cloud computing system 706, so that the receiving devices/systems can update their local RF-RF model(s) based on the provided updates.” ¶ [0172]: “Another example of utilization of an RF-RF model is simulation and measurement of performance of a device within an environment for which the RF-RF model has been trained. For example, given a trained RF-RF model and parameters for an area within a city, a specific drive path, or a route through the environment may be considered. For example, the route can be along a walking path, a road, a train, a UAV flight path, etc. It may be desirable to ensure that devices will maintain a sufficiently strong SINR along the path, and/or to understand which bands or resources, or antenna or beam configurations, or power levels, or modulation and coding (MCS) levels, should be chosen to maximize performance along the path. In some implementations, the path can be extracted from the planned route of an autonomous vehicle, such as a drone or self-driving car.”).
Regarding claim 22, claim 22 is directed to a computer-readable media storing the computer instructions executed by the processor in the system of claim 20 and, as such, is likewise rejected for the same reasons applied above in the rejection of claim 20.
Regarding claim 23 (depends on claim 22) CORGAN discloses:
wherein the differentiable ray tracer computes paths of electromagnetic waves (¶ [0050]: “ray tracing has been employed to attempt to simulate radio wave propagation and reflection throughout an environment.” ¶ [0062]: “used with differentiable ray-tracing methods to estimate RF channel parameters and, in this manner, control RF transmission/reception.” ¶ [0065]: “process 200 is a non-limiting example of the types of ray-tracing-based processes, within the scope of this disclosure, that can be performed using RF-RF models” ¶ [0069]: “A ray (e.g., an i-th ray) is traced between the transmission point ptx and the reception point prx through the selected point pi (204).”).
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art;
Ascertaining the differences between the prior art and the claims at issue;
Resolving the level of ordinary skill in the pertinent art; and
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over CORGAN et al. (US 2024/0323719 hereinafter “CORGAN”) in view of LEBLANC et al. (US 2018/0093183, hereinafter “LEBLANC”).
Regarding claim 10 (depends on claim 1), CORGAN discloses:
wherein the at least one trainable parameter comprises an antenna pattern (e.g., ¶ [0088]: “an antenna pattern that can be learned and encoded in the RF-RF model,”) that is modeled as a mixture of spherical [harmonic] ( ) (¶ [0088]: “For example, each UE, base station, etc., can have an antenna pattern that can be learned and encoded in the RF-RF model, to predict propagation from ptx to prx of signals emitted by and/or received by a particular UE or base station, or type/model of UE or base station.” ¶ [0088]: “In some implementations, to configure the RF-RF model in this manner, training (in operation 616 discussed below) can start with an initial set of coefficients representing an emitter's or receiver's antenna pattern (e.g., start by assuming spherically-uniform transmission/reception or assuming a standard pattern for a type of an antenna, such as a 120-degree base station sector antenna), and the coefficients can be modified during the training (based on back-propagation) to reduce loss and produce an estimate for the emitter's and/or receiver's antenna pattern. For example, the antenna patterns can be represented as spherical harmonics. In some implementations, spherical harmonics can be particularly effective for use in RF-RF modeling, because spherical harmonics provide a compact way to represent a two- or three-dimensional antenna gain pattern as a concise basis function, such that spherical harmonics are a useful way to estimate an RF emission surface using the methods described herein. Moreover, in some implementations, the spherical harmonic representation can be particularly storage-efficient and/or computationally-efficient when processed.” ¶ [0089]: “In some implementations, Ai can be the antenna pattern modeled as a spherical harmonic basis function, and Ai can be learned during model training or set as a known antenna pattern.”).
CORGAN fails to disclose a mixture of spherical Gaussian distributions.
However, whereas CORGAN is not explicit at to, LEBLANC teaches the use of spherical Gaussians as an alternative to spherical harmonics (¶ [0054]: “Those skilled in the art will appreciate that alternatives to spherical harmonics may be used, including cube maps, spherical Gaussians or other spherical functions. Also, the SH coefficients can be referred to more generally as irradiance parameters and may be placed in an array stored in the memory 11.” ).
Thus, as a simple design choice of using a well-known alternative to spherical harmonics, it would have been obvious to one of ordinary skill in the art to have modified CORGAN so as to incorporate modelling an antenna pattern as a mixture of spherical Gaussian distributions, as taught by LEBLANC.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over CORGAN et al. (US 2024/0323719, hereinafter “CORGAN”) in view of RAPOSO SUBTIL et al. (US 2020/0372699, hereinafter “SUBTIL”).
Regarding claim 19 (depends on claim 1), CORGAN discloses that at least one of the steps of computing or updating is performed on a computer system (¶ [0177]: “FIG. 15 is a diagram illustrating an example of a computing system that may be used to implement one or more components of a system that utilizes RF-RF models for RF system operations. The computer system illustrated in FIG. 15 can be, or can include, one or more of the network devices and modules described herein, e.g., UE, DU, RU, CU, and cloud computing system, for example in any of systems 100, 600, and/or 700. The computer system illustrated in FIG. 15, and/or a component or portion thereof, can be used to perform any of the processes described herein, such as processes 200, 610, 800, 900, 1000, 1100, 1200, and/or 1300.” ¶ [0192]: “The term “system” as used in this disclosure may encompass all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. A processing system can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.”).
CORGAN fails to disclose that the system comprises a virtual machine comprising a portion of a graphics processing unit.
However, whereas CORGAN may not be explicit as to, SUBTIL teaches:
a virtual machine comprising a portion of a graphics processing unit (¶ [0023]: “The parallel processing can be performed by a single processor or by multiple processors—such as different graphics processing unit (GPU) resources, including different threads of a single GPU, one or more discrete GPUs, one or more virtual GPUs (vGPUs), etc.—and is applicable to both local and remote computing.”.
Thus, in order to provide improved high quality and low latency computation performance techniques (as suggested by SUBTIL, ¶ [0005]-[0006] and ¶ [0020]-[0022]), it would have been obvious to one of ordinary skill in the art to modify CORGAN to incorporate performing one of the steps of computing or updating on a virtual machine comprising a portion of a graphics processing unit, as taught by SUBTIL.
Allowable Subject Matter
Claim 11 and 21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
At present, it is not apparent to the examiner which part of the application could serve as a basis for new and allowable claims. However, should the applicant nevertheless regard some particular matter as patentable, the examiner encourages applicant to appropriately amend the claims to include such matter and to indicate in the REMARKS the difference(s) between the prior art and the claimed invention as well as the significance thereof.
Furthermore, should applicant decide to amend the claims, examiner respectfully requests that the applicant please indicate in the REMARKS from which page(s), line(s) or claim(s) of the originally filed application that any amendments are derived. See MPEP § 2163(II)(A) (There is a strong presumption that an adequate written description of the claimed invention is present in the specification as filed, Wertheim, 541 F.2d at 262, 191 USPQ at 96; however, with respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims.).
A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action. Extensions of time may be available under the provisions of 37 CFR 1.136(a). In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action. Failure to reply within the set or extended period for reply will, by statute, cause the application to become ABANDONED (35 USC § 133).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT PEREN who can be reached by telephone at (571) 270-7781, or via email at vincent.peren@uspto.gov. The examiner can normally be reached on Monday-Friday from 10:00 A.M. to 6:00 P.M.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KING POON, can be reached at telephone number (571)272-7440. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VINCENT PEREN/
Examiner, Art Unit 2617
/KING Y POON/Supervisory Patent Examiner, Art Unit 2617