# US 8,625,885 B2 · Methods and systems for data analysis and feature recognition > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,625,885 B2 - Document ID: US8625885B2 - Status: Granted - Kind: B2 - Application number: 13/036,982 - Filed: 2011-02-28 - Published: 2012-06-07 - Granted: 2014-01-07 - Earliest claimed: 2006-03-23 - Assignees: Intelliscience Corporation - Applicants: Robert M. Brinson, Jr.; Nicholas Levi Middleton; Bryan Glenn Donaldson - Inventors: Robert M. Brinson, Jr.; Nicholas Levi Middleton; Bryan Glenn Donaldson - Web page: https://patentgrep.com/patent/US8625885B2 ## CPC - G06V 20/13: Satellite images - G06F 18/24765: {Rule-based classification} - G06F 18/41: {Interactive pattern learning with a human teacher} - G06V 10/765: {using rules for classification or partitioning the feature space} - G06V 10/7788: {the supervisor being a human, e.g. interactive learning with a human teacher} - G06V 10/95: {structured as a network, e.g. client-server architectures} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## 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 ### Claim 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. ### Claim 2 (depends on 1) The system of claim 1, wherein: the stored programming instructions causing the computer processor to train for the presence of the feature further cause the computer processor to: (i) calculate a third value for the first target data element within the first digital data set using a third algorithm from the series of algorithms; (ii) repeat the step of calculating the third value for the plurality of additional target data elements within the first digital data set; and (iii) define the plurality of synaptic paths by the calculated first values, calculated second values, and calculated third values for each of the target data elements; and further wherein the stored programming instructions causing the computer processor to identify the feature in the second digital data set cause the processor to: (i) calculate a third value for the first target data element within the second digital data set using the third algorithm from the series of algorithms; (ii) repeat the step of calculating the third value for the plurality of additional target data elements within the first digital data set; and (iii) determine whether the feature is present in the second digital data set by comparing the calculated first values, calculated second values, and calculated third values from the second digital data set with the defined plurality of synaptic paths associated with the feature. ### Claim 3 (depends on 1) The system of claim 1, wherein the first digital data set is a multispectral data set of a first type and the second digital data set is a multispectral data set of the first type. ### Claim 4 (depends on 3) The system of claim 3, wherein the first target data element, the second target data element, and the plurality of additional target data elements from the first digital data set are each contained within a common band within the first digital data set. ### Claim 5 (depends on 3) The system of claim 3, wherein the defined cluster extends across multiple bands within the multispectral data set. ### Claim 6 (depends on 1) The system of claim 1, wherein the stored programming instructions further cause the processor to create a known feature data store, the known feature data store containing unique synaptic paths associated with the feature. ### Claim 7 (depends on 6) The system of claim 6, wherein the known feature data store further comprises a count value for each of a plurality of unique synaptic paths, the count value indicating a number of instances in which the feature has been found to be associated with each one of the unique synaptic paths. ### Claim 8 (depends on 1) The system of claim 1, wherein the synaptic paths comprise a plurality of nodes, each of the nodes representing a calculated value from a different one of the series of algorithms. ### Claim 9 (depends on 1) The system of claim 1, wherein the stored programming instructions further cause the processor to perform an action comprising one of sounding an alarm or displaying an indicator when the processor determines that the feature is present in the second digital data set. ### Claim 10 (depends on 1) The system of claim 1, wherein the first digital data set is a multispectral data set comprising a plurality of subsets of sensor data, the plurality of subsets of sensor data comprising contiguous bands within a range of a frequency spectrum. ### Claim 11 (depends on 10) The system of claim 10, wherein the range of the frequency spectrum comprises the visible and the infrared portions of the frequency spectrum, and further wherein the plurality of subsets of sensor data lie within a plurality of different bands within the visible/infrared portion of the frequency spectrum. ### Claim 12 (depends on 10) The system of claim 10, wherein each of the synaptic paths comprises a plurality of nodes, the plurality of nodes representing calculated values corresponding to target data elements from a plurality of subsets of sensor data. ### Claim 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. ### Claim 14 (depends on 13) The system of claim 13 wherein each of the synaptic paths comprises a plurality of nodes, the plurality of nodes representing calculated values corresponding to target data elements from a plurality of subsets of sensor data. ### Claim 15 (depends on 13) The system of claim 13, wherein: the stored programming instructions causing the computer processor to train for the presence of the feature further cause the computer processor to: (i) calculate a third value for a third target data element within a third subset from the plurality of subsets within the first digital data set using one of the algorithms from the series of algorithms; (ii) repeat the step of calculating the third value for the plurality of additional target data elements within the first digital data set; and (iii) define the plurality of synaptic paths by the calculated first values, calculated second values, and calculated third values for each of the target data elements; and further wherein the stored programming instructions causing the computer processor to identify the feature in the second digital data set cause the processor to: (i) calculate a third value for a third target data element within a third subset from the plurality of subsets within the second digital data set using one of the algorithms from the series of algorithms; (ii) repeat the step of calculating the third value for the plurality of additional target data elements within the first digital data set; and (iii) determine whether the feature is present in the second digital data set by comparing the calculated first values, calculated second values, and calculated third values from the second digital data set with the defined plurality of synaptic paths associated with the feature. ### Claim 16 (depends on 15) The system of claim 15 wherein the first value, the second value, and the third value are each calculated by using the same algorithm from the series of algorithms. ### Claim 17 (depends on 15) The system of claim 15, wherein the first value, the second value, and the third value are each calculated by using a different algorithm from the series of algorithms. ### Claim 18 (depends on 13) The system of claim 13, wherein the defined cluster extends across multiple bands within the multispectral data set.