DETAILED ACTION
This action is in response to the original filing on 07/03/2024. Claims 1-19 are pending for examination.
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 .
Claim Objections
Claims 1 and 18 are objected to because of the following informalities: Missing “:” after “comprising”. Appropriate correction is required.
Claims 1 and 18-19 are objected to because of the following informalities: the phrase “the intermediate layers” should be “the plurality of intermediate layers”. Appropriate correction is required.
Claims 2-5 and 13-17 are objected to because each claim recites the following informality: “the function parameters”, which should be “the plurality of function parameters”. Appropriate correction to each claim is required.
Claim 8 is objected to because of the following informalities: “respect to same input”, which should read “respect to the same input”. Appropriate correction is required.
Claim 10 is objected to because of the following informalities: the phrase “time-series data” should be “a time-series data”. Appropriate correction is required.
Claims 11-12 are objected to because each claim recites the following informality: “image information”, which should be “an image information”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 and 8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites: “the processing unit learns a correspondence between an output from the intermediate layers in the neural network and a combination of a plurality of the function parameters by reinforcement learning.” It is unknown if “a plurality of the function parameters” is intended to refer to the same or different plurality of function parameters of Claim 1. For the purposes of examination, “plurality of function parameters” will refer to the same plurality of function parameters mentioned in Claim 1.
Claim 8 recites: “the processing unit performs feedback related to a plurality of the function parameters a plurality of times with respect to same input information input to the neural network.”. It is unknown if “a plurality of the function parameters” is intended to refer to the same or different plurality of function parameters of Claim 1. For the purposes of examination, “plurality of function parameters” will refer to the same plurality of function parameters mentioned in Claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because Claim 19 is directed to software per se.
Claim 19 recites: “A program for causing a computer to function as an information processing device including a processing unit that executes processing using a neural network, wherein the processing unit performs feedback related to a plurality of function parameters respectively used in a plurality of intermediate layers in the neural network on a basis of an output from the intermediate layers” without reciting any associated hardware structure is software per se. Accordingly, software per se is directed to a non-statutory subject matter. Therefore, Claim 19 is rejected under 35 USC 101 for being directed to a non-statutory subject matter.
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.
Claims 1, 5 and 16-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sun et al. (US 20210397966 A1, hereinafter Sun).
Regarding Claim 1, Sun teaches: An information processing device comprising a processing unit that executes processing using a neural network, (Paragraph [0015] The neural network system 100 (e.g., the encoder network 102 and/or the decoder network 104) may be implemented using one or more processors, one or more storage devices, and/or other suitable accessory devices) wherein
the processing unit performs feedback related to a plurality of function parameters respectively used in a plurality of intermediate layers in the neural network on a basis of an output from the intermediate layers. (Paragraph [0020] The reconstruction may be performed through the layers of the decoder network 204 and a series of up-sampling and transposed convolution (e.g., deconvolution) operations, e.g., similar to those described in association with the decoder network 104. Paragraph [0021] The reconstructed segmentation mask 210 may be compared to the annotated input mask 206 (e.g., as a ground truth) to determine a reconstruction loss 212. The reconstruction loss 212 may be calculated based on various mathematical formulas including, for example, mean squared error (MSE), L1/L2 norm, etc. In response to determining the reconstruction loss 212, the encoder network 202 and the decoder network 204 may adjust their respective operating parameters (e.g., weights), for example, based on a gradient descent associated with the reconstruction loss to reduce the reconstruction loss 212. The adjustment of the parameters may be performed, for example, by backpropagating the reconstruction error through the encoder and decoder networks. (Parameters are fed back (backpropagated) into intermediate layers of a model on the basis of an output (ex. loss) of these layers)).
Regarding Claims 18-19, Claims 18 and 19 are a method and a program claim which correspond to the information processing device claim of Claim 1. As such, they are rejected for the same reasons as Claim 1 above.
Regarding Claim 5, Sun teaches all the limitations as claimed in Claim 1, including: in a case where an end condition is not satisfied in learning by a gradient method, the processing unit performs further learning with each of the function parameters obtained by a search algorithm set as a correct answer value. (Paragraph [0021] The reconstructed segmentation mask 210 may be compared to the annotated input mask 206 (e.g., as a ground truth) to determine a reconstruction loss 212. The training process 200 may then repeat until one or more training termination criteria are satisfied (e.g., after completing a pre-determined number of training iterations, after the reconstruction loss falls below a predetermined threshold, etc.). (Should training end condition not be met, further learning occurs with certain parameters labeled as ground truth (correct answer value)))
Regarding Claim 16, Sun teaches all the limitations as claimed in Claim 1, including: the function parameters include a parameter used in a kernel function. (Paragraph [0029] Each of the layers may correspond to a plurality of filters (e.g., kernels) and each filter may be designed to detect (e.g., learn) a set of key points that collectively represent a respective feature or pattern. The filters may be associated with respective weights that, when applied to an input, produce an output indicating whether certain visual features or patterns have been detected. (The parameters are passed through filters in layers, i.e. kernels)).
Regarding Claim 17, Sun teaches all the limitations as claimed in Claim 1, including: the function parameters include a parameter used in an activation function. (Paragraph [0029] Each of the neural networks described herein may comprise multiple layers including an input layer, one or more convolutional layers, one or more non-linear activation layers (The parameters are used in an activation function (activation layer) in order to accomplish various tasks, such as optimization)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-3 and 6-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sun, as applied in Claim 1 above, in view of Kosiorek et al. (US 20240070972 A1, hereinafter Kosiorek).
Regarding Claim 2, Sun teaches all of the limitations as claimed in Claim 1. Sun does not teach: the processing unit updates a latent variable on a basis of an output from the intermediate layers in the neural network, and updates the function parameters on a basis of the updated latent variable.
In the same field of endeavor, Kosiorek teaches: the processing unit updates a latent variable on a basis of an output from the intermediate layers in the neural network, and (Paragraph [0051] A “latent variable” can generally refer to an embedding in a latent space. Generally, the latent variable 104 can implicitly represent features that are specific to the scene 125, e.g., positions and kinds of objects depicted in the scene 125, colors and lighting in the scene 125, or any other appropriate features of the scene 125. Paragraph [0098] The neural network 400 can then process the embedding circ(d) using a linear neural network layer (e.g., “linear” in FIG. 4), and generate a joint embedding by combining (e.g., concatenating) the output from the linear layer with the updated embedding of circ(x) generated by an update block. Paragraph [0099] The scene representation neural network 400 can use each update block in a sequence of update blocks to update the joint embedding using attention over the set of latent sub-variables, e.g., as described above. After the joint embedding has been updated by the last update block in the sequence of update blocks, the neural network 400 can process the joint embedding using a multi-layer perceptron neural network layer to generate an output that defines the radiance emitted in the viewing direction at that spatial location in the scene (e.g., (r, g, b) in FIG. 4) (A latent variable, referring to an embedding such as a joint embedding, is updated based on output of a layer in a neural network))
updates the function parameters on a basis of the updated latent variable. (Paragraph [0028] conditioning the scene representation neural network on a latent variable representing the other scene; and rendering one or more images that each depict the other scene from the perspective of a camera at a location in the other scene using the scene representation neural network conditioned on the latent variable representing the other scene; and updating current values of the neural network parameters of the scene representation neural network using gradients of an objective function that depends on the images of the other scene rendered using the scene representation neural network conditioned on the latent variable representing the other scene. (Parameter values are updated based on a rendered scene, which is based on an updated latent variable, meaning the parameter values are updated based on this updated latent variable))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined a processing unit updating a latent variable based on output of intermediate layers in a neural network and then updating function parameters based on the updated latent variable as taught by Kosiorek into Sun as both are in the same field of backpropagation of neural networks in order to accurately train these networks through usage of latent variables in a latent space and doing so would be desirable in order to better classify and simulate images, such as scene or location data in order to correctly identify features within (Kosiorek Paragraphs [0005]-[0011]).
Regarding Claim 3, the combination of Sun and Kosiorek teaches all the limitations as claimed in Claim 2, including: the processing unit executes update of the function parameters based on the updated latent variable and update of the latent variable based on the updated function parameters in order from an intermediate layer closer to an input layer side (Kosiorek Paragraph [0013] According to a first aspect, there is provided a method performed by one or more data processing apparatus for rendering a new image. Paragraph [0018] with respect to the current parameters of the current probability distribution over the space of latent variables, of an objective function that depends on: (i) the rendered image that depicts the scene from the perspective of the camera at the target camera location, and (ii) a target image of the scene captured from the camera at the target camera location; and updating the current parameters of the current probability distribution over the space of latent variables using: (i) the gradient of the objective function, and (ii) the embeddings of the observations (A processing apparatus updates a function parameter based on the "embeddings of the observations", which are latent variables as defined in Paragraph [0051]))
update of the latent variable based on the updated function parameters in order from an intermediate layer closer to an input layer side (Kosiorek Paragraph [0018] In some implementations, generating the parameters of the probability distribution over the space of latent variables from the embeddings of the observations comprises: initializing current parameters of a current probability distribution over the space of latent variables; and for each time step in a sequence of time steps: sampling a current latent variable from the space of latent variables in accordance with the current probability distribution over the space of latent variables; Paragraph [0128] In some cases the system can iteratively optimize the parameters of the probability distribution. (Parameters of a probability distribution are optimized, and periodically samples a current latent variable from a latent variable space, comprising an update))
Regarding Claim 6, Sun teaches all the limitations as claimed in Claim 1. Sun does not teach: the processing unit learns a correspondence between an output from the intermediate layers in the neural network and a combination of a plurality of the function parameters by reinforcement learning.
In the same field of endeavor, Kosiorek teaches: the processing unit learns a correspondence between an output from the intermediate layers in the neural network and a combination of a plurality of the function parameters by reinforcement learning. (Paragraph [0076] In order to render the new image, the system can render the color of each pixel in the new image 215 by accumulating the radiances emitted in the direction of the ray 210 and the volume densities σ at multiple spatial locations 230 on the ray 210. The system can accumulate the radiances and volume densities along a ray corresponding to a pixel using any appropriate accumulation technique. For example, the system can accumulate the radiances and volume densities by scaling each radiance value along the ray by corresponding volume density, and then summing the scaled radiance values. (A correspondence between radiance value and volume density along a ray corresponding to a pixel is learned in order to then render a new image))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined a processing unit learning a correspondence between intermediate layer output in the network and a combination of function parameters via reinforcement learning as taught by Kosiorek into Sun as both are in the same field of backpropagation of neural networks in order to accurately train these networks through usage of latent variables in a latent space and doing so would be desirable in order to better classify and simulate images, such as scene or location data in order to correctly identify features within (Kosiorek Paragraphs [0005]-[0011]).
Regarding Claim 7, Sun teaches all the limitations as claimed in Claim 1. Sun does not teach: the processing unit performs feedback related to a plurality of the function parameters on a basis of a plurality of intermediate feature amounts.
In the same field of endeavor, Kosiorek teaches: the processing unit performs feedback related to a plurality of the function parameters on a basis of a plurality of intermediate feature amounts. (Paragraph [0028] In some implementations, the scene representation neural network has a plurality of neural network parameters, wherein before being used to render the new image of the scene, the scene representation neural network is trained to determine trained values of the neural network parameters from initial values of the neural network parameters, wherein training the scene representation neural network comprises, for each of a plurality of other scenes: conditioning the scene representation neural network on a latent variable representing the other scene; and rendering one or more images that each depict the other scene from the perspective of a camera at a location in the other scene using the scene representation neural network conditioned on the latent variable representing the other scene; and updating current values of the neural network parameters. Paragraph [0107] More specifically, the system can initialize current parameters of the probability distribution to default (e.g., predefined or random) values. (The initial parameters of the network are updated to become optimal parameters, meaning that the requisite backpropagation (feedback) process of the neural network uses these initial values (intermediate feature amounts) as intermediate values due to their transitory nature. The initial parameters are also random in some embodiments, further adding to their transitory and thus intermediate nature))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined a processing unit performing feedback related to function parameters on a basis of intermediate feature amounts as taught by Kosiorek into Sun as both are in the same field of backpropagation of neural networks in order to accurately train these networks through usage of latent variables in a latent space and doing so would be desirable in order to better classify and simulate images, such as scene or location data in order to correctly identify features within (Kosiorek Paragraphs [0005]-[0011]).
Regarding Claim 8, Sun teaches all the limitations as claimed in Claim 1. Sun does not teach: the processing unit performs feedback related to a plurality of the function parameters a plurality of times with respect to same input information input to the neural network.
In the same field of endeavor, Kosiorek teaches: the processing unit performs feedback related to a plurality of the function parameters a plurality of times with respect to same input information input to the neural network. (Paragraph [0119] As described above with reference to FIG. 1, the image rendering system can include a training engine that can train the scene representation neural network and the encoding neural network on a set of training data over multiple training iterations. In some implementations, the training engine can jointly train the scene representation neural network and the encoding neural network, e.g., by backpropagating gradients of an objective function through the scene representation neural network and into the encoding neural network. Paragraph [0120] The training data can include a collection of training examples, where each training example includes a set of observations of a particular scene (Feedback (backpropagation) relating to a parameter gradient of the same training data over multiple iterations))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined a processing unit performing feedback related to function parameters a number of times with respect to the same input information input to the network as taught by Kosiorek into Sun as both are in the same field of backpropagation of neural networks in order to accurately train these networks through usage of latent variables in a latent space and doing so would be desirable in order to better classify and simulate images, such as scene or location data in order to correctly identify features within (Kosiorek Paragraphs [0005]-[0011]).
Regarding Claim 9, Sun teaches all the limitations as claimed in Claim 1. Sun does not teach: the processing unit further performs acquisition of input information input to the neural network or feedback related to processing parameters used for processing the input information
In the same field of endeavor, Kosiorek teaches: the processing unit further performs acquisition of input information input to the neural network or feedback related to processing parameters used for processing the input information (Paragraph [0008] The system receives a plurality of observations characterizing the scene, each observation comprising an image of the scene, and data identifying a location of a camera that captured the image of the scene, and generates a latent variable representing the scene from the observations. Paragraph [0053] Generally, “conditioning” the scene representation neural network 120 on the latent variable 104 representing the scene 125 can refer to providing the latent variable 104 as an input to the scene representation neural network 120, e.g., to be processed along with other inputs to the scene representation neural network. (A processing unit (paragraph 0013) generates a latent variable included in an input, thereby acquiring input to the neural network))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined a processing unit performing acquisition of input information as taught by Kosiorek into Sun as both are in the same field of backpropagation of neural networks in order to accurately train these networks through usage of latent variables in a latent space and doing so would be desirable in order to better classify and simulate images, such as scene or location data in order to correctly identify features within (Kosiorek Paragraphs [0005]-[0011]).
Regarding Claim 10, the combination of Sun and Kosiorek teaches all the limitations as claimed in Claim 9, including: the function parameters or the processing parameters are used to determine contents of processing using the function parameters or the processing parameters or presence or absence of processing. (Kosiorek Paragraph [0018] initializing current parameters of a current probability distribution over the space of latent variables; and for each time step in a sequence of time steps: sampling a current latent variable from the space of latent variables in accordance with the current probability distribution over the space of latent variables; conditioning the scene representation neural network on the current latent variable; rendering an image that depicts the scene from a perspective of a camera at a target camera location using the scene representation neural network conditioned on the current latent variable; determining a gradient, with respect to the current parameters of the current probability distribution over the space of latent variables, of an objective function that depends on: (i) the rendered image that depicts the scene from the perspective of the camera at the target camera location, and (ii) a target image of the scene captured from the camera at the target camera location; and updating the current parameters of the current probability distribution over the space of latent variables (The parameters which represent the contents of the image (e.g. shape, textures etc.) are used for determination of further processing, which comprises sampling a latent variable from a latent space based on a gradient distribution of parameters))
Regarding Claim 11, the combination of Sun and Kosiorek teaches all the limitations as claimed in Claim 9, including: the input information input to the neural network is image information, and (Kosiorek Paragraph [0008] The system receives a plurality of observations characterizing the scene, each observation comprising an image of the scene (Input information is images of a scene))
the processing parameters are used by an imaging sensor that acquires the image information. (Kosiorek Paragraph [0010] The system renders the new image that depicts the scene from the perspective of the camera at the new camera location by projecting radiance values from the model of the scene as a 3D radiance field onto an image plane of the camera at the new camera location. (The system renders a new image by using its parameters to dictate a new camera location and acquire an image))
Regarding Claim 12, the combination of Sun and Kosiorek teaches all the limitations as claimed in Claim 9, including: the input information input to the neural network is image information, and (Kosiorek Paragraph [0008] The system receives a plurality of observations characterizing the scene, each observation comprising an image of the scene, and data identifying a location of a camera that captured the image of the scene, and generates a latent variable representing the scene from the observations. (Input of image information))
the processing parameters are used by an image processing processor that processes the image information. (Kosiorek Paragraph [0009] The system conditions a (trained) scene representation neural network on the latent variable representing the scene. The scene representation neural network defines a model of the scene as a three-dimensional (3D) radiance field. The scene representation neural network is configured to receive a representation of a spatial location in the scene and a representation of a viewing direction, and to process the representation of the spatial location in the scene, the representation of the viewing direction, and the latent variable representing the scene to generate an output that defines a radiance emitted in the viewing direction at the spatial location in the scene. (Image data is processed))
Regarding Claim 13, the combination of Sun and Kosiorek teaches all the limitations as claimed in Claim 9, including: the processing unit controls feedback related to both or any of the function parameters and (Kosiorek Paragraph [0125] The system determines gradients of an objective function that depends on the predicted images of the scene rendered using the scene representation neural network, e.g., using backpropagation techniques (608). (The processor controlled unit controls feedback (backpropagation) of parameters))
the processing parameters further on a basis of environment information sensed by an environment sensor. (Kosiorek Paragraph [0066] In one example, the image rendering system 100 can be used as part of a software application (e.g., referred to for convenience as a “street view” application) that provide users with access to interactive panoramas showing physical environments, e.g., environments in the vicinity of streets. In response to a user request to view a physical environment from a perspective of a camera at a new camera location (An example embodiment is usage as a street viewer for a streets physical environment))
Regarding Claim 14, the combination of Sun and Kosiorek teaches all the limitations as claimed in Claim 13, including: the processing unit determines whether or not to execute feedback related to both or any of the function parameters and the processing parameters on a basis of the environment information. (Kosiorek Paragraph [0125] The system determines gradients of an objective function that depends on the predicted images of the scene rendered using the scene representation neural network, e.g., using backpropagation techniques (608). The objective function can be any appropriate objective function, e.g., a variational objective function (e.g., an evidence lower bound function). In some implementations, p.sub.θ(I|z,c) defines a product of a likelihood of the value of each pixel in the target image under a probability distribution generated for the pixel using the scene representation neural network. In other implementations, p.sub.θ(I|z,c) can denote an error, e.g., an L.sub.2 error, between the predicted image and the target image. (Backpropagation (feedback) which occurs to minimize a loss between two images, including their parameters on the basis of the environment in the image, such as lighting or positioning))
Regarding Claim 15, the combination of Sun and Kosiorek teaches all the limitations as claimed in Claim 13, including: the processing unit updates both or any of the function parameters and the processing parameters on a basis of the environment information. (Kosiorek Paragraph [0128] In some cases the system can iteratively optimize the parameters of the probability distribution. More specifically, the system can initialize current parameters of the probability distribution to default (e.g., predefined or random) values. After initializing the current parameters, for each time step in a sequence of time steps, the system can sample a current latent variable from the space of latent variables in accordance with the current probability distribution over the space of latent variables. The system can then condition the scene representation neural network on the current latent variable, and render an image that depicts the scene from a perspective of a camera at a target camera location (as specified by the target observation) using the scene representation neural network conditioned on the current latent variable. (Random parameters are optimized (updated) based on latent variables (environmental information) of an image))
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Sun, as applied in Claim 1 above, in view of Kanevsky et al. (US 20230342108 A1, hereinafter Kanevsky).
Regarding Claim 4, Sun teaches all the limitations as claimed in Claim 1. Sun does not teach: input information input to the neural network is time-series data, and the processing unit sets initial values of the function parameters at a start of learning on a basis of the function parameters updated on a basis of inputs of second and subsequent frames of the time-series data in past learning using the time-series data.
In the same field of endeavor, Kanevsky teaches: input information input to the neural network is time-series data, and (Paragraph [0080] In some examples, sequential input data can include time-series data (e.g., sensor data versus time or imagery captured at different times). Paragraph [0103] In some implementations, as described above, the input data can be sequential in nature. In some instances, the sequential input data can be generated by sampling or otherwise segmenting a stream of input data. As one example, frames can be extracted from a video. (Input data comprising time-series data))
the processing unit sets initial values of the function parameters at a start of learning on a basis of the function parameters updated on a basis of inputs of second and subsequent frames of the time-series data in past learning using the time-series data. (Paragraph [0130] In some implementations, backward propagation of errors can be used in conjunction with an optimization technique (e.g., gradient based techniques) to train machine-learned model 600 (e.g., when machine-learned model is a multi-layer model such as an artificial neural network). For example, an iterative cycle of propagation and model parameter (e.g., weights) update can be performed to train machine-learned model 600. Example backpropagation techniques include truncated backpropagation through time, Levenberg-Marquardt backpropagation, etc. (Input frames which are initally entered into a model and subsequently updated through a feedback (backpropagation) optimization technique in order to minimize errors through recurrency))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined the concepts of time-series data acting as input information and the processing unit setting initial values at the start of learning on the basis of function parameters updated on inputs of sequential time-series data as taught by Kanevsky into Sun as both are in the same field of backpropagation in order to learn training data in machine learning models through usage of reinforcement learning and doing so would be desirable in order to better classify and label data, such as audio data, for those hard of hearing (Kanevsky Paragraphs [0002]-[0005])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sato et al. (US 20240320495 A1) teaches training of a machine learning model through backpropagation (feedback) techniques
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN A CARDOSO whose telephone number is (571)272-8512. The examiner can normally be reached M-F 7:30 - 5:00, alternate Friday's off.
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/JUSTIN CARDOSO/
Patent Examiner, Art Unit 2143
/JENNIFER N WELCH/ Supervisory Patent Examiner, Art Unit 2143
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