PATENT CLAIM ANALYSIS

Application Number: 16012691
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-12
Patent Classification: ["375", "267000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over multi-input-multi-output (MIMO) channels. One of the methods includes: determining a transmitter and a receiver, at least one of which implements a machine-learning network; determining a MIMO channel model; determining first information; using the transmitter to process the first information and generate first RF signals representing inputs to the MIMO channel model; determining second RF signals representing outputs of the MIMO channel model, each second RF signal representing aggregated reception of the first RF signals altered by transmission through the MIMO channel model; using the receiver to process the second RF signals and generate second information as a reconstruction of the first information; calculating a measure of distance between the second and first information; and updating the machine-learning network based on the measure of distance between the second and first information.

Claim (Index 8):
The method of  claim 7 , wherein the at least one channel-modeling layer represents at least one of (i) additive Gaussian thermal noise in the MIMO communication channel, (ii) delay spread caused by time-varying effects of the MIMO communication channel, (iii) phase noise or other distortions caused by transmission and reception over the MIMO communication channel or hardware, or (iv) offsets in phase, frequency, rate, or timing caused by transmission and reception over the MIMO communication channel.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 94.0
- Lexical Diversity: 2.31944
- Patent Class: 375.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15970324', '15961454', '15961465', '13953355', '15978920']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5326420899136108
- 35 USC 102 Novelty (BERT): 0.5241599965155284
- Combined Prediction Score: 0.5317938805738026
- Mean Citation Score: 281.051018
- Max Citation Score: 343.46152
- Similarity Product: 287.7135013022041

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

Dataset: test