Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
1. This Office Action is in response to the preliminary amendment filed on 03/07/2024.
Claims 3, 4, 7-8 and 11-13 have been amended.
Claims 1-13 are pending.
Information Disclosure Statement
2. The information disclosure statement (IDS) filed on 09/19/2024 complies with the provisions of M.P.E.P. 609. The examiner has considered it.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
At Step 1:
Independent claims 1, 12 and 13 are directed to a "method", a “system” and a “program product” and thus directed to a statutory category
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
• " receiving a layer input for the convolutional layer" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 4 and page 17, line 4 where one can mentally visualizing receiving a layer input for the convolutional layer.
• " processing the layer input to generate a layer output for the convolutional layer,
comprising determining a convolution between the layer input and a filter associated with the convolutional layer" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 4 and page 17, lines 5-9 where one can mentally visualizing processing the layer input to generate a layer output.
• " generating a spatial weight mask for the convolutional layer by using a contextual
convolution block in accordance with a set of one or more spatially sensitive mask functions
defined in the contextual convolution block" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 4 and page 17, lines 10-22 where one can mentally visualizing generating a spatial weight mask for the convolutional layer by using a contextual convolution block.
• " determining a weighted layer output for the convolutional layer, comprising determining a product between the spatial weight mask and the layer output of the convolutional layer" as drafted this recites a mentally performable process as an evaluation or judgement. This is also consistent with the specification as in Fig. 4 and page 17, lines 23-25 where one can mentally visualizing determining a weighted layer output for the convolutional layer, comprising determining a product between the spatial weight mask and the layer output of the convolutional layer.
At Step 2A, Prong Two:
• The claim recites no additional elements. At most one might consider that a " A system comprising one or more computers and one or more storage devices … A computer storage medium encoded with instructions " as claimed might be considered to represent a computer-implemented system and method consistent with Fig. 1 even though the claim does not recite any computer. At most this would be a high-level recitation of a generic computer components and represents mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
• Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
• The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
• Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Dependent Claims 2-14
Claim 2 recites, “the convolutional layer comprises a 2D convolutional layer; and the set of one or more spatially sensitive mask functions are defined with respect to a horizontal axis and a vertical axis.” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 3 recites, “wherein the set of one or more spatially sensitive mask functions comprise one or more of a linear function, a sinusoidal function, comprising a 2D sinusoidal function, or a Gaussian function, comprising a 2D Gaussian function.” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 4 recites, “wherein each spatially sensitive mask function in the set of one or more spatially sensitive mask functions includes one or more mask coefficients, and wherein current values of the one or more mask coefficients are dependent on trained values of block parameters of the contextual convolution block.” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 5 recites, “wherein using the contextual convolution block in accordance with the set of one or more spatially sensitive mask functions defined in the contextual convolution block comprises computing the set of one or more spatially sensitive mask functions in accordance with the current values of the one or more mask coefficients.” which further describes the concept is mere gathered data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
Claim 6 recites, “wherein the spatially sensitive mask function comprises a non-zero constant” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claim 7 recites, “wherein generating the spatial weight mask for the convolutional layer comprises using the contextual convolution block to process data derived from the layer output for the convolutional layer in accordance with the set of one or more spatially sensitive mask functions” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claim 8 recites, “wherein the input comprises vision data, and the neural network is configured to perform a perception task on the vision data to generate the output” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claim 9 recites, “wherein the vision data comprises an image, and the perception task comprises one or more of an object detection task, an image classification task, or a semantic segmentation task” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claim 10 recites, “wherein the vision data comprises a video, and the perception task comprises one or more of a video processing task or a motion analysis task” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claim 11 recites, “training the neural network to determine trained values of network parameters of the neural network and the trained values of the block parameters of the contextual convolution block” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Examiner’s Note
5. What are the layers of a neural network? (According to Google): “Neural networks are organized into three core layer types: the input layer that receives raw data, hidden layers that transform and extract features using weights, biases, and non-linear activation functions, and the output layer that produces the final prediction. (1) Input Layer: The initial layer where raw data (like image pixels or tabular variables) enters the network. It performs no computations; it simply passes the data features forward. (2) Hidden Layers: The intermediate layers between the input and output where all the computational "heavy lifting" happens. These layers learn complex patterns by calculating the weighted sum of inputs and passing them through activation functions. (3) Output Layer: The final layer that produces the model's prediction or classification (e.g., probability of an image being a cat, or a forecasted numerical value).”.
What is a convolutional layer in a neural network? (According to Google): “A convolutional layer is the core building block of a Convolutional Neural Network (CNN). It applies sliding learnable filters (kernels) over an input to detect local patterns like edges, textures, or shapes, outputting the results into a new grid called a feature map.”
What is a a layer input for a convolutional layer in a neural network? (According to Google): “A layer input for a convolutional layer in a neural network is typically a 3D volume of numbers (often called a tensor) organized as Height × Width × Depth. It represents either the raw pixel data of an image or the extracted feature maps from a previous layer.”
What is a spatial weight mask for a convolutional layer in a neural network? (According to Google): “A spatial weight mask in a convolutional neural network (CNN) refers to the filter (or kernel) applied during the convolution operation. It acts as a sliding "window" of learnable weights that scans across an input image to extract spatial features like edges, corners, and textures.”
A Contextual Convolution Block (According to Google): “A Contextual Convolution Block is an advanced neural network module that dynamically alters its feature-detecting filters based on surrounding data or global priors. Unlike standard convolutions that apply the same weights uniformly across an entire image, these blocks selectively adapt their sampling locations and kernel weights to focus on relevant context.”
Farber et al, US 11,527,092, [Faber: Abstract (“Images of a hand may be used to identify users. Quality, detail, and so forth of these images may vary. An image is processed to determine a first spatial mask. A first neural network comprising many layers uses the first spatial mask at a first layer and a second spatial mask at a second layer to process images and produce an embedding vector representative of features in the image. The first spatial mask provides information about particular portions of the input image, and is determined by processing the image with an algorithm such as an orientation certainty level (OCL) algorithm. The second spatial mask is determined using unsupervised training and represents weights of particular portions of the input image as represented at the second layer. The use of the masks allows the first neural network to learn to use or disregard particular portions of the image, improving overall accuracy”)] [Faber: Column 2, lines 16-25 (“trains and utilizes a neural network using a plurality of spatial masks to process an input image and determine embedding vector data that is representative of features presented in the input image. For example, the neural network may comprise a convolutional neural network including a plurality of convolution layers, a plurality of pooling layers, and so forth. As a result of the training, the embedding vector data is particularly suited to represent features that distinguish one user from another.”)].
Anssari Moin et al, US 20200320685, [Moin: Paragraph 9 (“the computer providing the voxel representation to the input of a first 3D deep neural network, the 3D deep neural network being trained to classify voxels of the voxel representation into one or more tooth classes, preferably into at least 32 tooth classes of a dentition; the first deep neural network comprising a plurality of first 3D convolutional layers defining a first convolution path and a plurality of second 3D convolutional layers defining a second convolutional path parallel to the first convolutional path, the first convolutional path configured to receive at its input a first block of voxels of the voxel representation and the second convolutional path being configured to receive at its input a second block of voxels of the voxel representation, the first and second block of voxels having the same or substantially the same center point in the image volume and the second block of voxels representing a volume in real-world dimensions that is larger than the volume in real-world dimensions of the first block of voxels, the second convolutional path determining contextual information for voxels of the first block of voxels; the output of the first and second convolutional path being connected to at least one fully connected layer for classifying voxels of the first block of voxels into one or more tooth classes; and, the computer receiving classified voxels of the voxel representation of the dento-maxillofacial structure from the output of the first 3D deep neural network”)] [Moin: Paragraph 108 (“FIG. 5 depicts an example of a 3D deep neural network architecture for classification of individual teeth for use in the methods and systems for automated taxonomy of 3D image data as described in this application. The network may be implemented using 3D convolutional layers (3D CNNs). The convolutions may use an activation function as known in the field. A plurality of 3D convolutional layers, 504-508, may be used wherein minor variations in the number of layers and their defining parameters, e.g. differing activation functions, kernel amounts, use of subsampling and sizes, and additional functional layers such as dropout layers and batch normalization may be used in the implementation without losing the essence of the design of the deep neural network”)] [Moin: Paragraph 109 (“In order to reduce the dimensionality of the internal representation of the data within the deep neural network, a 3D max pooling layer 510 may be employed. At this point in the network, the internal representation may be passed to a densely-connected layer 512 aimed at being an intermediate for translating the representation in the 3D space to activations of potential labels, in particular tooth-type labels. The final or output layer 514 may have the same dimensionality as the desired number of encoded labels and may be used to determine an activation value (analogous to a prediction) per potential label 518”)].
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404]. The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on [571-272-4080]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Hung Le
07/16/2026
/HUNG D LE/Primary Examiner, Art Unit 2161
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