Systems and methods for analyzing distinct datasets with a common index
Inventors
HARDWICK, Andrew Craig • RUSSELL, Kyle Jordan
Assignees
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Publication Number
US-12430309-B2
Publication Date
2025-09-30
Expiration Date
Abstract
A system for analyzing distinct datasets with a common index is configurable to (i) receive input data that includes a first set of data and a second set of data that share a common index; (ii) perform a clustering operation on the first set of data to generate a set of clustered data comprising groups representing related datapoints; (iii) identify a set of occurrences within the second set of data (where each occurrence is associated with a respective set of coordinates in the common index), (iv) for each of the set of occurrences: (a) localize search space(s) in the common index using the respective set of coordinates for the occurrence, and (b) facilitate analysis of group(s) of the set of clustered data that are located within the search space(s) to determine whether a relationship exists between the group(s) and the occurrence.
Core Innovation
The invention relates to a computer-implemented system for analyzing distinct datasets with a common index. The system receives input data comprising a first set of data and a second set of data that share the common index, performs a clustering operation on the first set of data to generate a set of clustered data comprising groups representing related datapoints, and each group includes a respective first set of coordinates in the common index.
The system identifies a set of occurrences within the second set of data, where each occurrence is associated with a respective second set of coordinates in the common index. For each particular occurrence, the system localizes one or more search spaces in the common index using the relative second set of coordinates for that occurrence and determines whether the respective first set of coordinates of one or more groups are located within the localized one or more search spaces.
Based on whether the respective first sets of coordinates are located within the localized search spaces, the system assigns one or more labels to at least one group. The system further generates and presents a representation on a user interface, where the representation indicates whether the first set of coordinates of one or more groups are located within the localized search spaces.
Claims Coverage
The partial content includes three independent claims (clm-00001, clm-00013, clm-00020). Each independent claim covers a computer-implemented system that links clustered groups from a first dataset to occurrences from a second dataset by localizing search spaces on a common index and determining whether group coordinates fall within those search spaces.
Coordinated clustering and occurrence-based localization on a common index
receive input data comprising a first set of data and a second set of data sharing a common index; perform a clustering operation on the first set of data to generate a set of clustered data comprising groups representing related datapoints, each group comprising a respective first set of coordinates in the common index; identify a set of occurrences within the second set of data, each occurrence associated with a respective second set of coordinates in the common index; for each particular occurrence, localize one or more search spaces in the common index using the relative second set of coordinates for the particular occurrence; determine whether the respective first set of coordinates in the common index of one or more groups are located within the one or more search spaces in the common index; assign one or more labels to at least one group based on whether the respective first set of coordinates are located within the one or more search spaces.
Representation of clustered groups and localized search spaces for a user interface
receive input data comprising a first set of data and a second set of data sharing a common index; perform a clustering operation on the first set of data to generate a set of clustered data comprising groups representing related datapoints, each group comprising a respective first set of coordinates in the common index; identify a set of occurrences within the second set of data, each occurrence associated with a respective second set of coordinates in the common index; for each particular occurrence, localize one or more search spaces in the common index using relative to the respective second set of coordinates in the common index; generate a representation of (i) each particular occurrence, (ii) each group, and (iii) the localized search spaces, wherein the representation indicates whether one or more respective first sets of coordinates of one or more groups are located within the one or more search spaces; present the representation on a user interface.
User input occurrences and localization configurations for coordinated visualization
receive input data comprising a first set of data and a second set of data sharing a common index; perform a clustering operation on the first set of data to generate a set of clustered data comprising groups representing related datapoints, each group comprising respective first set of coordinates in the common index; receive user input identifying a set of occurrences within the second set of data, each occurrence associated with a respective second set of coordinates in the common index; receive user input defining one or more localization configurations; for each particular occurrence, localize one or more search spaces in the common index relative to the respective second set of coordinates in the common index in accordance with the one or more localization configurations; generate a representation of (i) each particular occurrence, (ii) each group, and (iii) the localized search spaces, wherein the representation indicates whether one or more respective first sets of coordinates of one or more groups are located within the one or more search spaces; present the representation on a user interface.
Across clm-00001, clm-00013, and clm-00020, the core claim coverage is the same analytical linkage: cluster a first dataset into coordinate-bearing groups on a common index, identify coordinate-bearing occurrences in a second dataset on the same common index, localize search spaces around each occurrence, and determine whether group coordinates fall within those search spaces. The independent claims differ in whether they emphasize assigning labels or generating and presenting a representation on a user interface, including the use of user input for occurrence selection and localization configurations.
Stated Advantages
Not explicitly described in patent.
Documented Applications
Not explicitly described in patent.
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