PATENT CLAIM ANALYSIS

Application Number: 16158697
Application Type: Utility
Filing Date: 2018-10
Publication Date: 2019-04
Patent Classification: ["705", "332000"]

Abstract:
In some embodiments, the present disclosure provides a network of multi-functional sensors; where, based on a quality insurance, each multi-functional sensor is positioned in, on, or in a vicinity of: a transported cargo and/or a cargo container, containing the transported cargo; where each multi-functional sensor is configured to measure particular transport-related condition, particular cargo-related condition, or both, to form cargo transport sensor data and wirelessly transmit it to a server that is configured to dynamically predict, based on the cargo transport sensor data, a predicted quality loss of the transported cargo, determine a current loss value of the transported cargo and cause one or more remedial actions that include instantaneously instructing to pay a payout amount to an owner of the transported cargo to compensate for the current loss value and/or transmitting a remedial instruction with an adjustment to the operation of one or more of a cargo transport, the cargo container, and a cargo storage.

Claim (Index 23):
The method of  claim 13 , wherein the at least one server having cargo quality shortfall administration software comprises at least one machine learning algorithm configured to dynamically predict the at least one current quality metric of the transported cargo.

Metadata:
- Claim Count in Document: 68.0
- Percentile: 97.0
- Lexical Diversity: 2.23077
- Patent Class: 705.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15173891', '15173953', '15907715', '15573125', '15175091']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1284911943848299
- 35 USC 102 Novelty (BERT): 0.5175280639492943
- Combined Prediction Score: 0.1673948813412763
- Mean Citation Score: 138.27004
- Max Citation Score: 141.84523000000004
- Similarity Product: 82.86292627836706

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