DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application 18/006,730, filed on 01/25/2023, is being examined under the first inventor to file provisions of the AIA . A preliminary amendment is filed on 01/25/2023. Claim 7 has been canceled. Claims 1, 4-6, 8-12 and 14-15 have been amended. Claims 1-6 and 8-15 are pending.
Priority
2. Acknowledgment is made of the applicant’s claim priority under a PCT application PCT/EP2021/070817, filing date 07/26/2021 and a Foreign application EP20382676.3, filing date 07/27/2020.
Specification
3. The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 04/11/2023 is being considered by the examiner.
Claim Objections
5. Claims 2-6 and 7-12 objected to because of the following informalities: “A method” in claims 2-6 and 7-12 should be amended to -The method- since they are depended on claim 1. Appropriate correction is required.
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.
6. Claims 1-6 and 8-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
In claims 1-6 and 8-12 are rejected under 35 U.S.C 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more. Claims 1-12 are directed to the abstract idea of deploying neural network for processing structure data, as explained in detail below. The claims do not include elements that are sufficient to amount to significantly more than the judicial exception because the elements can be concepts performed in the human mind which do not add meaningful limits to practicing the abstract idea.
Claim 1 recites an implemented method comprises:
a) deploying a neural network (NN) comprising at least one input stage (S.sub.in) and one output stage (S.sub.out) wherein each stage (S.sub.in, S.sub.i, S.sub.out) of the neural network (NN) comprises at least a neural unit (NU) (e.g., designing or deploying a neural network model using at least one input stage and output stage can be can be performed in human mind using pen and paper);
the set of stages of the neural network (NN) are stacked and consecutively connected, the at least one neural unit (NU) comprises: a receptive field (RF) comprising a plurality of input ports (p.sub.i), and one output port (out) (e.g., designing or deploying a neural model including a lot of input ports and output ports can be performed in human mind using pen and paper);
b) receiving structured data Ii representing an image into the input stage wherein datum locations x is indexed at least with one index i (e.g., e.g., observing and evaluating image data with a specific position can be performed in human mind);
c) processing the inputted structured data in the neural network (NN) (e.g., observing and evaluating image data can be performed in human mind);
d) outputting the data outputted in the output stage (e.g., outputting result can be performed in human mind including evaluation, judgement and opinion);
characterized in that
e) the at least one neural unit (NU) provides an output value INRF on the output port (out) depending on the values inputted in the input ports (p.sub.i) when processing data in a predetermined neighborhood N(x) of location x of the structured data provided to the stage (S.sub.in, S.sub.i, S.sub.out) of the neural unit (NU), where x∈N(x), the output value being provided according to the following expression for the receptive field:
INRF(x)=.∑.yi∈N(x)miu(yi)-λ∑.yi∈N(x)ωiσ(u(yi)-∑yj∈Nk(x)g(yj-x)u(yj)) wherein yi∈N(x) denotes the set of locations in the neighborhood N(x), yj∈Nk(x) denotes the set of locations in the neighborhood Nk(x), u(yi) denotes the values inputted in the input ports pi, mi denotes m(x,yi) in abbreviated form, the predetermined weights of a first kernel m(.) defined on the neighborhood N(x), ωi denotes ω(x,yi) in abbreviated form, the predetermined weights of a second kernel ω(.) defined on the neighborhood N(x), g(x,yj) denotes the predetermined weights of third kernel g(.) defined on a predetermined second neighborhood Nk(x), λ is a non-zero predetermined real value, and σ(.) denotes a predetermined non-linear real function (e.g., observing and evaluating output values calculated from a mathematical equation with respect to input values can be performed in human mind using pen and paper).
Claim 1 falls within two of the groupings of abstract ideas [e.g., Mental Processes and Mathematical Concepts] enumerated in the 2019 PEG. The recited concept can be performed in human mind including an observation, evaluation, judgement, opinion using mathematical formulas or equations. The limitation of deploying a neural network model, inputting structural data into the neural network model and outputting data values calculated based on formulas, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind using mathematical concepts. Nothing in the claim element precludes the step from practically being performed in the mind. For example, “deploying”, “receiving”, “processing” or “outputting” in the context of this claim encompasses the user designs a model to process data and obtains output values using mathematical formulas. Under the broadest reasonable interpretation, claim 1 covers performance of the limitation in the mind and falls within the “Mental Processes” and “Mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The additional feature merely uses a computer as a tool to output data results after a series of data gathering steps. The output of results and data gathering steps are insignificant extra-solution activity. A computer is used to obtain data, analyze, select and then provide the result. Courts treat collecting information, as well as analyzing information by steps people go through in their minds, as essentially mental processes within the abstract-idea category. FairWarning IP, LLC v. Iatric Systems, Inc., 839 F.3d 1089, 1093 (Fed. Cir. 2016). Therefore, there is no additional element integrates the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, claims 1 is not patent eligible.
Claims 2-6 and 8-12 are similar to claim 1, fall within two of the groupings of abstract ideas [e.g., mental processes and Mathematical concepts] enumerated in the 2019 PEG Accordingly, the claims 2-6 and 8-12 recite an abstract idea. There is no additional element integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not have additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, claims 2-6 and 8-12 are ineligible subject under 35 USC 101.
Claims 13-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 13-15 recite a deployed neural network (NN), a computer program product and a computer system with no structural components do not fall in one of the four categories of statutory subject matter. Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations. See MPEP 2106.03.
Conclusion
The closest prior art Rawat et al. “Deep Convolutional Neural networks for Image Classification” discloses a neural network architecture for an image classification task. An image is input directly to the network, and this is followed by several stages of convolution and pooling. The convolutional layers serve as feature extractors, and thus they learn the feature representations of their input images. The neurons in the convolutional layers are arranged into feature maps. Each neuron in a feature map has a receptive field, which is connected to a neighborhood of neurons in the previous layer via a set of trainable weights, sometimes referred to as a filter bank. Inputs are convolved with the learned weights in order to compute a new feature map, and the convolved results are sent through a nonlinear activation function. All neurons within a feature map have weights that are constrained to be equal; however, different feature maps within the same convolutional layer have different weights so that several features can be extracted at each location. More formally, the kth output feature map Yk can be computed as Yk = f(Wk ∗ x) (2.1) where the input image is denoted by x; the convolutional filter related to the kth feature map is denoted by Wk; the multiplication sign in this context refers to the 2D convolutional operator, which is used to calculate the inner product of the filter model at each location of the input image; and f(·) represents the nonlinear activation function. Nonlinear activation functions allow for the extraction of nonlinear features. The purpose of the pooling layers is to reduce the spatial resolution of the feature maps and thus achieve spatial invariance to input distortions and translations. Initially, it was common practice to use average pooling aggregation layers to propagate the average of all the input values, of a small neighborhood of an image to the next layer. However, in more recent models, max pooling aggregation layers propagate the maximum value within a receptive field to the next layer. Formally, max pooling selects the largest element within each receptive field such that Yki j = max (p,q)∈i j xkpq, (2.2) where the output of the pooling operation, associated with the kth feature map, is denoted byYki j, xkpq denotes the element at location (p, q) contained by the pooling region i j, which embodies a receptive field around the position (i, j). The Rawat et al. reference discloses deploying a neural network (NN) comprising at least one input stage and one output stage, and each stage of the neural network comprises at least a neural unit. The Rawat et al. discloses receiving an image and providing the image into the input state of the neural network (NN). However, the Rawat et al. reference does not explicitly disclose an output value INRF on the output port as recited in claim 1.
Another closest reference Zoumpourlis et al. discloses a computer implemented for processing structured data, specifically an image, bi-dimensional image comprising pixels indexed with two indexes or a three-dimensional image comprising voxels indexed with three indexes. The Zoumpourlis et al. reference discloses displaying a neural network (NN) comprising at least one input stage and one output stage. Each stage of the neural network comprises at least a neural unit. The Zoumpourlis et al. reference discloses receiving an image and providing the image into the input state of the neural network (NN). However, the Zoumpourlis et al. reference does not explicitly disclose an output value INRF on the output port as recited in claim 1.
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/YUK TING CHOI/Primary Examiner, Art Unit 2164