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US 10,650,558 B2Granted
Techniques for displaying stack graphs
AssigneePalantir Technologies Inc.
InventorsJulie Noelle Tibshirani, Ryan Amelia Beiermeister, Daniel Patrick Cervelli, Timothy James Slatcher, Gregory DeJuan Martin, Antoine Alexandre Adrien Llorca, Timothy James Wilson
Life of this patent
Estimating the term…
Abstract
A method and system for drawing a stack graph that includes a timeline and one or more stack lines based on a set of event data. A stack line may be associated with an event target and may include one or more event overlays that represent event objects. In one implementation, event overlays may include a visual characteristic that identifies an event source associated with the event object of the event overlay.
Claims24
A method comprising:
- receiving event data, wherein the event data comprises:
- healthcare beneficiary data;
- healthcare provider data;
- healthcare transaction data, wherein the healthcare transaction data comprises:
- a first identifier that identifies particular healthcare beneficiary data that corresponds to particular healthcare transaction data;
- a second identifier that identifies particular healthcare provider data that corresponds to the particular healthcare transaction data;
- and
- event time data;
- displaying a stack graph for the event data, wherein the stack graph comprises:
- a timeline;
- a group of stack lines, wherein each particular stack line of the group of stack lines comprises one or more event overlays and corresponds to a particular subset of the healthcare beneficiary data;
- determining a cardinality of each particular subset of the healthcare beneficiary data corresponding to a stack line of the group of stack lines by:
- identifying one or more particular subsets of the healthcare transaction data that correspond to the particular subset of the healthcare beneficiary data;
- identifying one or more particular subsets of the healthcare provider data that correspond to the one or more particular subsets of the healthcare transaction data;
- counting a number of unique particular subsets of the healthcare provider data that correspond to the one or more particular subsets of the healthcare transaction data;
- using the number of unique particular subsets of the healthcare provider data as the cardinality;
- generating a cluster hierarchy of the healthcare beneficiary data by:
- calculating, for each particular subset of the healthcare beneficiary data, a cohesiveness score based on a similarity of each particular subset of the healthcare beneficiary data to an aggregate similarity of the healthcare beneficiary data;
- placing each particular subset of the healthcare beneficiary data into the cluster hierarchy based on the cohesiveness scores, wherein placing each particular subset of the healthcare beneficiary data into the cluster hierarchy comprises placing a subset of the healthcare beneficiary data having a higher cluster score higher in the cluster hierarchy;
- excluding, from the stack graph, stack lines of the group of stack lines corresponding to a first group of particular subsets of the healthcare beneficiary data having a particular cardinality below a cardinality threshold;
- and
- modifying a displayed position of the remaining stack lines corresponding to a second particular group of particular subsets of the healthcare beneficiary data having a particular cardinality at or above the cardinality threshold, on the stack graph, based, at least in part, on the cluster hierarchy;
- wherein the method is executed using one or more processors.
20 more elements
A system, comprising:
- storage media;
- one or more processors;
- and one or more programs stored in the storage media and configured for execution by the one or more processors, the one or more programs comprising instructions for:
- receiving event data, wherein the event data comprises:
- healthcare beneficiary data;
- healthcare provider data;
- healthcare transaction data, wherein the healthcare transaction data comprises:
- a first identifier that identifies particular healthcare beneficiary data that corresponds to particular healthcare transaction data;
- a second identifier that identifies particular healthcare provider data that corresponds to the particular healthcare transaction data;
- and
- event time data;
- displaying a stack graph for the event data, wherein the stack graph comprises:
- a timeline;
- a group of stack lines, wherein each particular stack line of the group of stack lines comprises one or more event overlays and corresponds to a particular subset of the healthcare beneficiary data;
- determining a cardinality of each particular subset of the healthcare beneficiary data corresponding to a stack line of the group of stack lines by:
- identifying one or more particular subsets of the healthcare transaction data that correspond to the particular subset of the healthcare beneficiary data;
- identifying one or more particular subsets of the healthcare provider data that correspond to the one or more particular subsets of the healthcare transaction data;
- counting a number of unique particular subsets of the healthcare provider data that correspond to the one or more particular subsets of the healthcare transaction data;
- using the number of unique particular subsets of the healthcare provider data as the cardinality;
- generating a cluster hierarchy of the healthcare beneficiary data by:
- calculating, for each particular subset of the healthcare beneficiary data, a cohesiveness score based on a similarity of each particular subset of the healthcare beneficiary data to an aggregate similarity of the healthcare beneficiary data;
- placing each particular subset of the healthcare beneficiary data into the cluster hierarchy based on the cohesiveness scores, wherein placing each particular subset of the healthcare beneficiary data into the cluster hierarchy comprises placing a subset of the healthcare beneficiary data having a higher cluster score higher in the cluster hierarchy;
- excluding, from the stack graph, stack lines of the group of stack lines corresponding to a first group of particular subsets of the healthcare beneficiary data having a particular cardinality below a cardinality threshold;
- and
- modifying a displayed position of the remaining stack lines corresponding to a second particular group of particular subsets of the healthcare beneficiary data having a particular cardinality at or above the cardinality threshold, on the stack graph, based, at least in part, on the cluster hierarchy.
22 more elements
Dependencies are inferred from the claim text.