PATENT CLAIM ANALYSIS

Application Number: 15906348
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-07
Patent Classification: ["382", "103000"]

Abstract:
A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.

Claim (Index 2):
A method for simplified generation of systems for broad area geospatial object detection the step comprising:\n (a) retrieving a plurality of color and spectrally optimized geospatial training images comprising an object of interest that is clearly labeled and a second plurality of color and spectrally optimized geospatial training images that do not contain the object of interest to isolate a set of visual features unique to the object of interest using an pre-engineered object model creation module comprising a processor, a memory, and a plurality of programming instructions stored in the memory and operable on the processor; (b) employing the set of visual features unique to the object of interest to parameterize at least one pre-engineered machine learning classifier element running at least one pre-programmed machine learning programming protocol using a machine learning classifier element training and verification module comprising a processor, a memory, and a plurality of programming instructions stored in the memory and operable on the processor; (c) training pre-engineered machine learning classifier elements to identify the object of interest using a plurality of training geospatial images with the object of interest labeled in one subset and not labeled in a second subset within the machine learning classifier element training and verification module; (d) refining and confirming the fine specificity of trained machine learning classifier elements for the object of interest to the exclusion of other objects using geospatial training images not containing the object of interest, some of which comprise other irrelevant objects; and (e) analyzing previously unanalyzed, scale corrected geospatial images for presence of the object of interest using trained machine learning classified element and reporting the results of the study in a format pre-determined by the study author.

Metadata:
- Claim Count in Document: 3.0
- Percentile: 88.0
- Lexical Diversity: 1.65152
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15608894', '15709252', '15194541', '15452076', '14835736']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3618518155821877
- 35 USC 102 Novelty (BERT): 0.611139117721947
- Combined Prediction Score: 0.3867805457961637
- Mean Citation Score: 425.711134
- Max Citation Score: 524.563
- Similarity Product: 519.3363849685192

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test