METHODS AND SYSTEMS FOR DATA ANALYSIS AND FEATURE RECOGNITION

• US 20070244844A1
• Filed: 03/21/2007
• Published: 10/18/2007
• Est. Priority Date: 03/23/2006
• Status: Abandoned Application

• Current Assignee: IntelliScience Corp.
• Original Assignee: IntelliScience Corp.
• Inventors: Donaldson, Bryan Glenn, Middleton, Nicholas Levi, Brinson, Robert M.
• Application Number:
  US11/689,361
• Publication Number:
  US 20070244844A1
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06F 18/24765 Rule-based classification
• CPC Class Codes: G06F 18/41 Interactive pattern learning with a human teacher
• CPC Class Codes: G06V 10/765 using rules for classification or partitioning the feature space
• CPC Class Codes: G06V 10/7788 the supervisor being a human, e.g. interactive learning with a human teacher
• CPC Class Codes: G06V 10/95 structured as a network, e.g. client-server architectures
• CPC Class Codes: G06V 20/13 Satellite images

Abstract

Systems and methods for automated pattern recognition and object detection. The method can be rapidly developed and improved using a minimal number of algorithms for the data content to fully discriminate details in the data, while reducing the need for human analysis. The system includes a data analysis system that recognizes patterns and detects objects in data without requiring adaptation of the system to a particular application, environment, or data content. The system evaluates the data in its native form independent of the form of presentation or the form of the post-processed data.

Claims

1. A method for training one or more data sets for use in data analysis and feature recognition, the method comprising:

processing one or more algorithms on a previously defined region of interest in a first data set for identifying a feature; and

storing the results of processing as the feature.

2. The method of claim 1, wherein the data sets are digital data sets.

3. The method of claim 1, further comprising defining the region of interest in the first data set,

wherein processing the one or more algorithms further comprises training the feature based on the defined region of interest and the one or more algorithms.

4. The method of claim 1, wherein processing includes creating a datastore associated with the one or more algorithms.

5. The method of claim 4, wherein creating comprises generating an algorithm value cache, wherein generating comprises:

a) retrieving a first target data element in the first data set;

b) processing the one or more algorithms on a target data area for the retrieved first target data element;

c) repeating a) and b) for a plurality of target data elements in the first data set; and

d) storing the results of the processed one or more algorithms to generate the algorithm value cache.

6. The method of claim 5, wherein processing further comprises:

defining the region of interest in the first data set;

developing a training path array from at least one of a positive value training set and a negative value training set based on the defined region of interest in the first data set; and

associating the training path array with the trained feature.

7. The method of claim 6, wherein storing further comprises assigning a processing action to the feature.

8. A system training one or more data sets for use in data analysis and feature recognition, the system comprising:

a datastore;

a display; and

a processor in data communication with the display and the datastore, configured to automatically train the feature based on a first series of algorithms executed on a region of interest in a first data set and save the results of the training as the feature in the datastore.

9. The system of claim 8, wherein the data sets are digital data sets.

10. The system of claim 8, further comprising a user interface device configured to allow a user to define the region of interest in the first data set,

wherein the processor trains of the feature based on the defined region of interest and the one or more algorithms.

11. The system of claim 8, wherein the processor creates a datastore associated with the one or more algorithms.

12. The system of claim 11, wherein the datastore includes an algorithm value cache and the processor a) retrieves a first target data element in the first data set, b) processes the one or more algorithms on a target data area for the retrieved first target data element, repeats a) and b) for a plurality of target data elements in the first data set, and stores the results of the processed one or more algorithms to generate the algorithm value cache.

13. The system of claim 12, wherein the processor defines the region of interest in the first data set, develops a training path array from at least one of a positive value training set and a negative value training set based on the defined region of interest in the first data set, and associates the training path array with the trained feature.

14. The system of claim 13, wherein the processor assigns a processing action to the feature.

15. A method for data analysis and feature recognition comprising:

receiving a first data set; and

identifying a feature in the received data set using results of a series of algorithms processed on a second data set, wherein identifying the feature further comprises;

generating an algorithm value cache for the first data set;

selecting a first target data element in a region of interest in the first data set;

comparing the algorithm value cache for the first data set to the processed first series of algorithms on the second data set; and

performing a feature processing action if there is match based on the comparison.

16. The method of claim 15, wherein performing a feature action further comprising identifying the feature.

17. The method of claim 16, wherein identifying the feature comprises generating an output event, the output event includes at least one of emitting one or more system sounds or painting in a chosen color.

18. A system for data analysis and feature recognition comprising:

a datastore configured to contain processed results of a first series of algorithms performed on a first data set;

a display; and

a processor in data communication with the display and the datastore, the processor comprising;

a component configured to identify a feature in a second data set using the datastore, the component comprising;

a first sub-component configured to generate an algorithm value cache for the second data set;

a second sub-component configured to select a first target data element in a region of interest in the second data set;

a third sub-component configured to compare the set of algorithm values to the processed set of algorithms in the datastore; and

a fourth sub-component configured to perform a feature processing action if there is match between the second set of algorithm values and the first set of algorithm values in the datastore.

19. The system of claim 18, wherein the component comprises:

a fifth sub-component configured to identify the feature action.

20. The system of claim 19, wherein the fifth sub-component is further configured to generate an output event, the output event includes at least one of emitting one or more system sounds or painting in a chosen color.