Methods and systems for data analysis and feature recognition
First Claim
Patent Images
1. A system for data analysis and feature recognition comprising:
- a computer processor;
a display in communication with the computer processor;
a data store in communication with the computer processor, the data store containing a series of algorithms; and
a memory in communication with the computer processor and containing stored programming instructions operable by the computer processor, the stored programming instruction, when operated by the computer processor, causing the computer processor to;
train for the presence of the feature within a first digital data set based on the series of algorithms, comprising;
calculating a first value for a first target data element within the first digital data set using a first algorithm from the series of algorithms;
calculating a second value for the first target data element within the first digital data set using a second algorithm from the series of algorithms;
repeating the steps of calculating the first value and calculating the second value for a plurality of additional target data elements within the first digital data set using the first algorithm and the second algorithm from the series of algorithms;
defining a plurality of synaptic paths, each one of the plurality of synaptic paths being defined by the calculated first values and calculated second values for each of the target data elements; and
associating the feature with one or more of the synaptic paths, based upon a cluster value, the cluster value representing a number of times the feature must occur within a defined cluster area for the feature to be associated with the target data element; and
identify the feature in a second digital data set based on the series of algorithms, comprising;
calculating a first value for a first target data element within the second digital data set using the first algorithm from the series of algorithms;
calculating a second value for the first target data element within the second digital data set using the second algorithm from the series of algorithms;
repeating the steps of calculating the first value and calculating the second value for a plurality of additional target data elements within the second digital data set using the first algorithm and the second algorithm from the series of algorithms; and
determining whether the feature is present in the second digital data set by comparing the calculated first values and second values from the second digital data set with the defined plurality of synaptic paths associated with the feature.
1 Assignment
0 Petitions
Accused Products
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.
37 Citations
18 Claims
-
1. A system for data analysis and feature recognition comprising:
-
a computer processor; a display in communication with the computer processor; a data store in communication with the computer processor, the data store containing a series of algorithms; and a memory in communication with the computer processor and containing stored programming instructions operable by the computer processor, the stored programming instruction, when operated by the computer processor, causing the computer processor to; train for the presence of the feature within a first digital data set based on the series of algorithms, comprising; calculating a first value for a first target data element within the first digital data set using a first algorithm from the series of algorithms; calculating a second value for the first target data element within the first digital data set using a second algorithm from the series of algorithms; repeating the steps of calculating the first value and calculating the second value for a plurality of additional target data elements within the first digital data set using the first algorithm and the second algorithm from the series of algorithms; defining a plurality of synaptic paths, each one of the plurality of synaptic paths being defined by the calculated first values and calculated second values for each of the target data elements; and associating the feature with one or more of the synaptic paths, based upon a cluster value, the cluster value representing a number of times the feature must occur within a defined cluster area for the feature to be associated with the target data element; and identify the feature in a second digital data set based on the series of algorithms, comprising; calculating a first value for a first target data element within the second digital data set using the first algorithm from the series of algorithms; calculating a second value for the first target data element within the second digital data set using the second algorithm from the series of algorithms; repeating the steps of calculating the first value and calculating the second value for a plurality of additional target data elements within the second digital data set using the first algorithm and the second algorithm from the series of algorithms; and determining whether the feature is present in the second digital data set by comparing the calculated first values and second values from the second digital data set with the defined plurality of synaptic paths associated with the feature. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
-
-
13. A system for data analysis and feature recognition comprising:
-
a computer processor; a display in communication with the computer processor; a data store in communication with the computer processor, the data store containing a series of algorithms; a first digital data set in communication with the processor, the first digital data set comprising a plurality of subsets, each of the plurality of subsets being drawn from a different band of a frequency spectrum; a second digital data set in communication with the processor, the second digital data set comprising a plurality of subsets, each of the plurality of subsets being drawn from a different band of a frequency spectrum; and a memory in communication with the computer processor and containing stored programming instructions operable by the computer processor, the stored programming instruction, when operated by the computer processor, causing the computer processor to; train for the presence of the feature within the first digital data set based on the series of algorithms, comprising; calculating a first value for a first target data element within a first subset from the plurality of subsets within the first digital data set using one of the algorithms from the series of algorithms; calculating a second value for a second target data element within a second subset from the plurality of subsets within the first digital data set using one of the algorithms from the series of algorithms; repeating the steps of calculating the first value and calculating the second value for a plurality of additional target data elements within first subset and the second subset from the first digital data set; defining a plurality of synaptic paths, each one of the plurality of synaptic paths being defined by the calculated first values and calculated second values for each of the target data elements; and associating the feature with one or more of the synaptic paths, based upon a cluster value, the cluster value representing a number of times the feature must occur within a defined cluster area for the feature to be associated with the target data element; and identify the feature in the second digital data set based on the series of algorithms, comprising; calculating a first value for a first target data element within a first subset from the plurality of subsets within the second digital data set using one of the algorithms from the series of algorithms; calculating a second value for a second target data element within a second subset from the plurality of subsets within the second digital data set using one of the algorithms from the series of algorithms; repeating the steps of calculating the first value and calculating the second value for a plurality of additional target data elements within the second digital data set using the first algorithm and the second algorithm from the series of algorithms; and determining whether the feature is present in the second digital data set by comparing the calculated first values and second values from the second digital data set with the defined plurality of synaptic paths associated with the feature. - View Dependent Claims (14, 15, 16, 17, 18)
-
Specification