PATENT CLAIM ANALYSIS

Application Number: 15994702
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2019-09
Patent Classification: ["340", "907000"]

Abstract:
Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

Claim (Index 39):
A method comprising\n using electronic sensors located at roadside equipment in a vicinity of an intersection of a ground transportation network to monitor the intersection and approaches to the intersection, the electronic sensors generating motion data about ground transportation entities moving on the approaches or in the intersection, one or more of the ground transportation entities comprising vulnerable ground transportation entities and not being capable of sending safety messages to other ground transportation entities in the vicinity of the intersection, applying the generated motion data to a machine learning model running in the roadside equipment to predict a trajectory of the one of the vulnerable ground transportation entities, the machine learning model having been provided to the roadside equipment by a remote server through the Internet, the machine learning model having been trained using motion data generated by the electronic sensors located at the roadside equipment, based on the motion data generated by the electronic sensors, broadcasting virtual safety messages from the roadside equipment wirelessly for receipt by any of the ground transportation entities at or near the intersection that is capable of receiving the messages, and incorporating in the virtual safety messages information about one or more of the vulnerable ground transportation entities, the incorporated information in each of the virtual safety messages comprising at least a predicted future trajectory of one of the vulnerable ground transportation entities,\n the predicted future trajectory incorporated in each of the virtual safety messages being for use by any of the ground transportation entities at or near the intersection that is capable of receiving messages to reconcile its own trajectory with the predicted future trajectory of the one of the vulnerable ground transportation entities.

Metadata:
- Claim Count in Document: 2.0
- Percentile: 93.0
- Lexical Diversity: 2.80519
- Patent Class: 340.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15994826', '15682500', '15303876', '15430081', '14954220']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6461742345915096
- 35 USC 102 Novelty (BERT): 0.6014301327872577
- Combined Prediction Score: 0.6416998244110844
- Mean Citation Score: 236.149416
- Max Citation Score: 550.4454
- Similarity Product: 430.2943653781056

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