PATENT CLAIM ANALYSIS

Application Number: 15955426
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2019-10
Patent Classification: ["708", "200000"]

Abstract:
A neural network engine comprises a plurality of floating point multipliers, each having an input connected to an input map value and an input connected to a corresponding kernel value. Pairs of multipliers provide outputs to a tree of nodes, each node of the tree being configured to provide a floating point output corresponding to either: a larger of the inputs of the node; or a sum of the inputs, one output node of the tree providing a first input of an output module, and one of the multipliers providing an output to a second input of the output module. The engine is configured to process either a convolution layer of a neural network, an average pooling layer or a max pooling layer according to the kernel values and whether the nodes and output module are configured to output a larger or a sum of their inputs.

Claim (Index 1):
A neural network engine configured to receive at least one M\u00d7N window of floating point number values corresponding to a pixel of an input map and a corresponding at least one set of M\u00d7N floating point number kernel values for a neural network layer, the neural network engine comprising:\n a plurality of at least M\u00d7N floating point multipliers, each multiplier having a first operand input configured to be connected to an input map value and a second operand input configured to be connected to a corresponding kernel value; pairs of multipliers within said M\u00d7N multipliers providing respective floating point number outputs to respective input nodes of a tree of nodes, each node of said tree being configured to provide a floating point number output corresponding to either: a larger of the inputs of said node; or a sum of said inputs, one output node of said tree providing a first input of an output module, and one of said M\u00d7N multipliers providing an output to a second input of said output module; wherein when said engine is configured to process a convolution layer of a neural network, each of said kernel values comprises a trained value for said layer, said nodes are configured to sum their inputs and said output module is configured to sum its first and second inputs, to apply an activation function to said sum and to provide an output of said activation function as an output of said output module; wherein when said engine is configured to process an average pooling layer of a neural network, each of said kernel values comprises a value corresponding to 1/(M\u00d7N), said nodes are configured to sum their inputs and said output module is configured to sum its first and second inputs and to provide said sum as said output of said output module; and wherein when said engine is configured to process a max pooling layer of a neural network, each of said kernel values comprises a value equal to 1, said nodes are configured to output a larger of their inputs and said output module is configured to output a larger of its first and second inputs as said output of said output module.

Metadata:
- Claim Count in Document: 15.0
- Percentile: 91.0
- Lexical Diversity: 2.66102
- Patent Class: 708.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15234851', '15423292', '15719829', '15600807', '15423289']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3808793104049772
- 35 USC 102 Novelty (BERT): 0.5022302380760724
- Combined Prediction Score: 0.3930144031720867
- Mean Citation Score: 197.769296
- Max Citation Score: 207.22314
- Similarity Product: 143.55880313108324

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test