PATENT CLAIM ANALYSIS

Application Number: 16273031
Application Type: Utility
Filing Date: 2019-02
Publication Date: 2019-08
Patent Classification: ["706", "025000"]

Abstract:
The present disclosure provides an integrated circuit chip device and related product thereof. The integrated circuit chip device includes an external interface and a processing circuit. The processing circuit is configured to quantize the first layer input data and the first layer weight group data to obtain a first layer quantized input data and a first layer quantized weight group data; query a first layer output data corresponding to the first layer quantized input data and the first layer quantized weight group data from a preset output result table, determine the first layer output data as a second layer input data, and input the second layer input data into n-1 layers to execute forward operations to obtain nth layer output data; the n th  layer output data gradients is determined according to the n th  layer output data and the n th  layer back operations is obtained according to the training instructions.

Claim (Index 8):
A neural network training method for executing neural network training, the neural network comprising n layers with n being an integer greater than 1, wherein the neural network training method comprises:\n receiving training instructions; determining a first layer input data and a first layer weight group data; quantizing the first layer input data and the first layer weight group data to obtain the first layer quantized input data and the first layer quantized weight group data; querying a first layer output data corresponding to the first layer quantized input data and the first layer quantized weight group data from the preset output result table, determining the first layer output data as the second layer input data and inputting the second layer input data into n-1 layers to execute forward operations to obtain the nth layer output data; determining nth layer output data gradients of the nth layer output data, obtaining the nth layer back operations among back operations of n layers of the training instructions, quantizing the n th  layer output data gradients to obtain n th  layer quantized output data gradients; querying n th  layer input data gradients corresponding to the n th  layer quantized output data gradients and a n th  layer quantized input data from the preset output result table, querying n th  layer weight group gradients corresponding to the n th  layer quantized output data gradients and a n th  layer quantized weight group data from the preset output result table, and updating the weight group data of n layers of the n th  layer weight group gradients; determining the n th  input data gradients as the (n-1) th  output data gradients, inputting the (n-1) th  output data gradients into n-1 layers to execute back operations to obtain the n-1 weight group data gradients, updating the n-1 weight group data corresponding to the n-1 weight group data gradients of the n-1 weight group data gradients, wherein the weight group data of each layer comprises at least two weights.

Metadata:
- Claim Count in Document: 32.0
- Percentile: 99.0
- Lexical Diversity: 2.65
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['16075540', '16038083', '10571602', '15655203', '16228357']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3483933596317539
- 35 USC 102 Novelty (BERT): 0.4744445747058059
- Combined Prediction Score: 0.3609984811391591
- Mean Citation Score: 157.77427
- Max Citation Score: 163.24976
- Similarity Product: 114.6154831663132

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

Dataset: test