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 1):
A system for simplified generation of systems for broad area geospatial object detection comprising:\n an object model creation module comprising a processor, a memory, and a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions:\n receives a plurality labeled positive training orthorectified geospatial images in at least some of which an object of interest has been identified; \n retrieves a plurality of labeled negative training orthorectified geospatial images where objects that are not the object of interest, at least one of which closely resembles the object of interest, have been identified; \n programmatically isolates features found in the objects of interest but not in the irrelevant training objects; and \n creates at least one object of interest classification model using the features unique to the object of interest; \n a machine learning classifier training and verification computer comprising a processor, a memory, and a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions:\n accepts at least one classification model; \n retrieves a plurality of labeled and unlabeled orthorectified geospatial training images each comprising the object of interest; \n trains a plurality of pre-built machine learning classifier elements, each running a pre-programmed machine learning protocol parameterized with the classification model, using the plurality of labeled and unlabeled orthorectified geospatial training images each comprising the object of interest; and \n for each trained machine learning classifier element, verifies performance in classifying the object of interest using a plurality of unlabeled orthorectified geospatial training images comprising the object of interest and a plurality of unlabeled orthorectified geospatial training images that do not contain the object of interest; and \n a model-based object classifier comprising a processor, a memory, and a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions:\n retrieve the plurality of trained machine learning elements for the object of interest; \n analyzes a plurality of resolution scale-corrected, unanalyzed orthorectified geospatial image segments for presence of at least one object of interest; and \n reports the presence and location of any objects of interest found.

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): 0.3616148172344447
- 35 USC 102 Novelty (BERT): 0.6117378473507303
- Combined Prediction Score: 0.3866271202460732
- Mean Citation Score: 425.711134
- Max Citation Score: 524.563
- Similarity Product: 522.1231584230661

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