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 25):
The system of  claim 16 , wherein the operations further comprise:\n training the at least one machine-learning network to communicate over a multi-user MIMO communication channel utilized by multiple users, wherein the transmitter comprises one or more encoder machine-learning networks, and the receiver comprises one or more decoder machine-learning networks, wherein using the transmitter to process the first information and generate the plurality of first RF signals comprises: using the one or more encoder machine-learning networks to (i) process at least a first portion of the first information to generate a first subset of the plurality of first RF signals; and (ii) process at least a second portion of the first information and generate a second subset of the plurality of first RF signals, wherein using the receiver to process the plurality of second RF signals and generate second information as a reconstruction of the first information comprises: using the one or more decoder machine-learning networks to (i) process a first subset of the plurality of second RF signals and generate at least a first portion of the second information as a reconstruction of the first portion of the first information; and (ii) process a second subset of the plurality of second RF signals and generate at least a second portion of the second information as a reconstruction of the second portion of the first information, wherein calculating the measure of distance between the second information and the first information comprises: (i) calculating a first measure of distance between the first portion of the second information and the first portion of the first information, and (ii) calculating a second measure of distance between the second portion of the second information and the second portion of the first information, and wherein updating the at least one machine-learning network based on the measure of distance between the second information and the first information comprises:\n based on the first measure of distance and the second measure of distance, updating at least one of (i) the one or more encoder machine-learning networks, or (ii) the one or more decoder machine-learning networks.

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): 0.5328624344507845
- 35 USC 102 Novelty (BERT): 0.5259993303038879
- Combined Prediction Score: 0.5321761240360949
- Mean Citation Score: 281.051018
- Max Citation Score: 343.46152
- Similarity Product: 259.0600038168049

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