PATENT CLAIM ANALYSIS

Application Number: 15971904
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2019-11
Patent Classification: ["708", "514000"]

Abstract:
A system for block floating point computation in a neural network receives a block floating point number comprising a mantissa portion. A bit-width of the block floating point number is reduced by decomposing the block floating point number into a plurality of numbers each having a mantissa portion with a bit-width that is smaller than a bit-width of the mantissa portion of the block floating point number. One or more dot product operations are performed separately on each of the plurality of numbers to obtain individual results, which are summed to generate a final dot product value. The final dot product value is used to implement the neural network. The reduced bit width computations allow higher precision mathematical operations to be performed on lower-precision processors with improved accuracy.

Claim (Index 15):
One or more computer storage media having computer-executable instructions for block floating point computation that, upon execution by a processor, cause the processor to at least:\n receive a block floating point number comprising a mantissa portion and an exponent portion; reduce a bit-width of the block floating point number by decomposing the block floating point number into a plurality of numbers each having a mantissa portion with a bit-width that is smaller than a bit-width of the mantissa portion of the block floating point number; scale the reduced bit-width block floating point number, wherein the scaling includes scaling the exponent portion of the reduced bit-width block floating point number based on the mantissa portion within the reduced bit-width block floating point number; perform one or more dot product operations separately on each of the plurality of numbers to obtain individual results; sum the individual results to generate a final dot product value; and use the final dot product value to implement the neural network.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 93.0
- Lexical Diversity: 1.97059
- Patent Class: 708.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15592021', '10786332', '14106442', '12127898', '14295818']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4320542702920323
- 35 USC 102 Novelty (BERT): 0.5012993371268814
- Combined Prediction Score: 0.4389787769755172
- Mean Citation Score: 203.921026
- Max Citation Score: 222.03918
- Similarity Product: 149.36322589454412

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