Generating Predilection Cohorts
First Claim
1. A computer implemented method of generating a predilection cohort, the computer implemented method comprising:
- receiving digital sensor data associated with a predilection cohort from a set of multimodal sensors, wherein the predilection cohort comprises an identified member of the predilection cohort, and wherein the digital sensor data comprises metadata describing attributes of the identified member;
processing and parsing the digital sensor data using a set of data models to identify a set of events associated with the predilection cohort;
analyzing, by an inference engine, the set of events and description data for the identified member, to generate a predilection score, wherein the inference engine analyzes the set of events and the description data using a rule set, and wherein the predilection score indicates a probability of a future occurrence of the potential action being performed by the identified cohort member; and
responsive to a determination that the predilection score exceeds a threshold, identifying the potential action as an action that is likely to occur.
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Accused Products
Abstract
A computer implemented method, apparatus, and computer program product for generating risk scores for specific risk cohorts. Digital sensor data associated with a specific risk cohort is received from a set of multimodal sensors. The specific risk cohort includes a set of identified cohort members. The digital sensor data includes metadata describing attributes associated with at least one cohort member in the set of identified cohort members. Description data for each cohort member in the set of identified cohort members is retrieved to form a set of cohort description data. The description data for each cohort member comprises data describing a previous history of the cohort member or a current status of the cohort member. The cohort member is a person, animal, plant, thing, or location. A specific risk score is generated for the specific risk cohort based on selected risk factors, the attributes associated with the at least one identified member, and the set of cohort description data. A response action is initiated in response to a determination that the specific risk score exceeds a risk threshold.
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Citations
20 Claims
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1. A computer implemented method of generating a predilection cohort, the computer implemented method comprising:
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receiving digital sensor data associated with a predilection cohort from a set of multimodal sensors, wherein the predilection cohort comprises an identified member of the predilection cohort, and wherein the digital sensor data comprises metadata describing attributes of the identified member; processing and parsing the digital sensor data using a set of data models to identify a set of events associated with the predilection cohort; analyzing, by an inference engine, the set of events and description data for the identified member, to generate a predilection score, wherein the inference engine analyzes the set of events and the description data using a rule set, and wherein the predilection score indicates a probability of a future occurrence of the potential action being performed by the identified cohort member; and responsive to a determination that the predilection score exceeds a threshold, identifying the potential action as an action that is likely to occur. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A computer program product for generating a predilection cohort, the computer program product comprising:
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a computer usable medium having computer usable program code embodied therewith, the computer usable program code comprising; computer usable program code configured to receive digital sensor data associated with a predilection cohort from a set of multimodal sensors, wherein the predilection cohort comprises an identified member of the predilection cohort, and wherein the digital sensor data comprises metadata describing attributes of the identified member; computer usable program code configured to process and parse the digital sensor data using a set of data models to identify a set of events associated with the predilection cohort; computer usable program code configured to analyze, by an inference engine, the set of events and description data for the identified member, to generate a predilection score, wherein the inference engine analyzes the set of events and the description data using a rule set, and wherein the predilection score indicates a probability of a future occurrence of the potential action being performed by the identified cohort member; and computer usable program code configured to identify the potential action as an action that is likely to occur in response to determination that the predilection score exceeds a threshold. - View Dependent Claims (14, 15, 16)
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17. An apparatus comprising:
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a bus system; a communications system coupled to the bus system; a memory connected to the bus system, wherein the memory includes computer usable program code; and a processing unit coupled to the bus system, wherein the processing unit executes the computer usable program code to receive digital sensor data associated with a predilection cohort from a set of multimodal sensors, wherein the predilection cohort comprises an identified member of the predilection cohort, and wherein the digital sensor data comprises metadata describing attributes of the identified member, process and parse the digital sensor data using a set of data models to identify a set of events associated with the predilection cohort, analyze, by an inference engine, the set of events and description data for the identified member, to generate a predilection score, wherein the inference engine analyzes the set of events and the description data using a rule set, and wherein the predilection score indicates a probability of a future occurrence of the potential action being performed by the identified cohort member, and identify the potential action as an action that is likely to occur in response to determination that the predilection score exceeds a threshold. - View Dependent Claims (18, 19)
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20. A data processing system comprising:
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a sensor analysis engine, wherein the sensor analysis engine receives digital sensor data associated with a predilection cohort from a set of multimodal sensors, wherein the predilection cohort comprises an identified member of the predilection cohort, and wherein the digital sensor data comprises metadata describing attributes of the identified member;
processes and parses the digital sensor data using a set of data models to identify a set of events associated with the predilection cohort; andan inference engine, wherein the inference engine analyzes the set of events and description data for the identified member, to generate a predilection score, wherein the inference engine analyzes the set of events and the description data using a rule set, and wherein the predilection score indicates a probability of a future occurrence of the potential action being performed by the identified cohort member, and wherein the inference engine identifies the potential action as an action that is likely to occur in response to determination that the predilection score exceeds a threshold.
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Specification