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 17):
The one or more computer storage media of  claim 16 , wherein the higher bit portion and the lower bit portion represent higher order and lower order mantissa bits, respectively, and the one or more computer storage media have further computer-executable instructions that, upon execution by a processor, cause the processor to at least scale the higher and lower bit portions before performing the one or more dot product operations.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 93.0
- Lexical Diversity: 1.97059
- Patent Class: 708.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15592021', '10786332', '14106442', '12127898', '14295818']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4623285252357506
- 35 USC 102 Novelty (BERT): 0.4998293204831648
- Combined Prediction Score: 0.466078604760492
- Mean Citation Score: 203.921026
- Max Citation Score: 222.03918
- Similarity Product: 156.54852347707632

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