IP Library Granted Patent US 10,776,708
Granted Patent B2
US 10,776,708 · App. 15/399,147 · Granted Sep 15, 2020

Analyzing behavior in light of social time

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Quick Facts
Patent No.
US 10,776,708
App. No.
15/399,147
Granted
Sep 15, 2020
Kind
B2
Abstract

A relational event history is determined based on a data set, the relational event history including a set of relational events that occurred in time among a set of actors. Data is populated in a probability model based on the relational event history, where the probability model is formulated as a series of conditional probabilities that correspond to a set of sequential decisions by an actor for each relational event, where the probability model includes one or more statistical parameters and corresponding statistics. A baseline communications behavior for the relational event history is determined based on the populated probability model, and departures within the relational event history from the baseline communications behavior are determined.

Claims (78)

1. A method comprising

determining, based on a data set, a relational event history, the relational event history comprising a set of relational events that occurred in time among a set of actors;

populating, based on the relational event history, data in a probability model, wherein the probability model is formulated as a series of conditional probabilities that correspond to a set of sequential decisions by an actor for each relational event, and wherein the probability model includes one or more statistical parameters and one or more corresponding statistics;

determining, by one or more processing devices and based on the populated probability model, a baseline communications behavior for the relational event history, wherein the baseline comprises a first set of values for the one or more statistical parameters; and

determining departures from the baseline communications behavior within the relational event history; and wherein:

determining departures from the baseline communications behavior within the relational event history comprises:

determining, based on one or more subsets of the relational events included in the relational event history, a second set of values for the statistical parameters; and

comparing the second set of values for the statistical parameters to the first set of values: and

comparing the second set of values for the statistical parameters to the first set of values comprises:

determining a hypothesis regarding communications behavior within the relational event history; and

testing the hypothesis using the second set of values.

2. The method of claim 1 , wherein testing the hypothesis using the second set of values comprises:

computing, based on the second set of values, a value of a test statistic; and

using the test statistic to determine the departures from the baseline communications behavior.

3. The method of claim 1 , wherein determining the hypothesis regarding communications behavior within the relational event history comprises selecting a set of predictions regarding differences in communications behavior with respect to one or more of the statistical parameters over a period of time.

4. A method comprising

determining, based on a data set, a relational event history, the relational event history comprising a set of relational events that occurred in time among a set of actors;

populating, based on the relational event history, data in a probability model, wherein the probability model is formulated as a series of conditional probabilities that correspond to a set of sequential decisions by an actor for each relational event, and wherein the probability model includes one or more statistical parameters and one or more corresponding statistics;

determining, by one or more processing devices and based on the populated probability model, a baseline communications behavior for the relational event history, wherein the baseline comprises a first set of values for the one or more statistical parameters: and

determining departures from the baseline communications behavior within the relational event history; and wherein:

determining the relational event history comprises: extracting data from the data set; transforming the extracted data; loading the transformed data; and enriching the loaded data.

5. The method of claim 4 , wherein the one or more corresponding statistics relate to one or more of senders of relational events, modes of relational events, topics of relational events, or recipients of relational events.

6. A method comprising

determining, based on a data set, a relational event history, the relational event history comprising a set of relational events that occurred in time among a set of actors;

populating, based on the relational event history, data in a probability model, wherein the probability model is formulated as a series of conditional probabilities that correspond to a set of sequential decisions by an actor for each relational event, and

wherein the probability model includes one or more statistical parameters and one or more corresponding statistics;

determining, by one or more processing devices and based on the populated probability model, a baseline communications behavior for the relational event history, wherein the baseline comprises a first set of values for the one or more statistical parameters; and

determining departures from the baseline communications behavior within the relational event history; and

determining a subset of actors included in the set of actors, wherein at least one decision included in the set of sequential decisions comprises selecting a recipient of the relational event from the subset of actors.

7. The method of claim 6 , further comprising:

receiving one or more user inputs;

outputting, based on the one or more user inputs, a graphical analysis of the baseline communications behavior; and

outputting, based on the one or more user inputs, one or more graphical analyses of the departures from the baseline communications behavior.

8. A system comprising:

one or more processing devices; and

one or more non-transitory computer-readable media coupled to the one or more processing devices having instructions stored thereon which, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising:

determining, based on a data set, a relational event history, the relational event history comprising a set of relational events that occurred in time among a set of actors;

populating, based on the relational event history, data in a probability model, wherein the probability model is formulated as a series of conditional probabilities that correspond to a set of sequential decisions by an actor for each relational event, and wherein the probability model includes one or more statistical parameters and one or more corresponding statistics;

determining, by one or more processing devices and based on the populated probability model, a baseline communications behavior for the relational event history, wherein the baseline comprises a first set of values for the one or more statistical parameters; and

determining departures from the baseline communications behavior within the relational event history.

9. A system comprising:

one or more processing devices; and

one or more non-transitory computer-readable media coupled to the one or more processing devices having instructions stored thereon which, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising:

determining, based on a data set, a relational event history, the relational event history comprising a set of relational events that occurred in time among a set of actors;

populating, based on the relational event history, data in a probability model wherein the probability model is formulated as a series of conditional probabilities that correspond to a set of sequential decisions by an actor for each relational event, and wherein the probability model includes one or more statistical parameters and one or more corresponding statistics;

determining, by one or more processing devices and based on the populated probability model, a baseline communications behavior for the relational event history, wherein the baseline comprises a first set of values for the one or more statistical parameters; and

determining departures from the baseline communications behavior within the relational event history; and wherein

determining departures from the baseline communications behavior within the relational event history comprises:

determining, based on one or more subsets of the relational events included in the relational event history, a second set of values for the statistical parameters; and

comparing the second set of values for the statistical parameters to the first set of values; and

comparing the second set of values for the statistical parameters to the first set of values comprises:

determining a hypothesis regarding communications behavior within the relational event history; and

testing the hypothesis using the second set of values.

10. The system of claim 9 , wherein testing the hypothesis using the second set of values comprises:

computing, based on the second set of values, a value of a test statistic; and

using the test statistic to determine the departures from the baseline communications behavior.

11. The system of claim 9 , wherein determining the hypothesis regarding communications behavior within the relational event history comprises selecting a set of predictions regarding differences in communications behavior with respect to one or more of the statistical parameters over a period of time.

12. A system comprising:

one or more processing devices; and

one or more non-transitory computer-readable media coupled to the one or more processing devices having instructions stored thereon which, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising:

determining, based on a data set, a relational event history, the relational event history comprising a set of relational events that occurred in time among a set of actors;

populating, based on the relational event history, data in a probability model, wherein the probability model is formulated as a series of conditional probabilities that correspond to a set of sequential decisions by an actor for each relational event, and wherein the probability model includes one or more statistical parameters and one or more corresponding statistics;

determining, by one or more processing devices and based on the populated probability model, a baseline communications behavior for the relational event history, wherein the baseline comprises a first set of values for the one or more statistical parameters; and

determining departures from the baseline communications behavior within the relational event history; and wherein

determining the relational event history comprises: extracting data from the data set; transforming the extracted data; loading the transformed data; and enriching the loaded data.

13. The system of claim 12 , wherein the one or more corresponding statistics relate to one or more of senders of relational events, modes of relational events, topics of relational events, or recipients of relational events.

14. A system comprising:

one or more processing devices: and

one or more non-transitory computer-readable media coupled to the one or more processing devices having instructions stored thereon which, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising:

determining, based on a data set, a relational event history, the relational event history comprising a set of relational events that occurred in time among a set of actors;

populating, based on the relational event history, data in a probability model, wherein the probability model is formulated as a series of conditional probabilities that correspond to a set of sequential decisions by an actor for each relational event, and wherein the probability model includes one or more statistical parameters and one or more corresponding statistics;

determining, by one or more processing devices and based on the populated probability model, a baseline communications behavior for the relational event history, wherein the baseline comprises a first set of values for the one or more statistical parameters; and

determining departures from the baseline communications behavior within the relational event history; and

determining a subset of actors included in the set of actors, wherein at least one decision included in the set of sequential decisions comprises selecting a recipient of the relational event from the subset of actors.

15. The system of claim 14 , the operations further comprising:

receiving one or more user inputs;

outputting, based on the one or more user inputs, a graphical analysis of the baseline communications behavior; and

outputting, based on the one or more user inputs, one or more graphical analyses of the departures from the baseline communications behavior.

Assignments (11)
RELEASE OF SECURITY INTEREST Recorded Apr 2, 2025
From: UBS AG, STAMFORD BRANCH
To: FORCEPOINT, LLC; BITGLASS, LLC
Reel/Frame 070706/0263 →
CHANGE OF NAME Recorded Mar 21, 2025
From: FORCEPOINT FEDERAL HOLDINGS LLC
To: EVERFOX HOLDINGS LLC
Reel/Frame 070585/0524 →
PARTIAL PATENT RELEASE AND REASSIGNMENT AT REEL/FRAME 055052/0302 Recorded Oct 3, 2023
From: CREDIT SUISSE, AG, CAYMAN ISLANDS BRANCH, AS COLLATERAL AGENT
To: FORCEPOINT FEDERAL HOLDINGS LLC (F/K/A FORCEPOINT LLC)
Reel/Frame 065103/0147 →
SECURITY INTEREST Recorded Sep 29, 2023
From: FORCEPOINT FEDERAL HOLDINGS LLC
To: APOLLO ADMINISTRATIVE AGENCY LLC, AS COLLATERAL AGENT
Reel/Frame 065086/0822 →
CHANGE OF NAME Recorded May 12, 2021
From: FORCEPOINT LLC
To: FORCEPOINT FEDERAL HOLDINGS LLC
Reel/Frame 056216/0309 →
PATENT SECURITY AGREEMENT Recorded Jan 20, 2021
From: REDOWL ANALYTICS, INC.; FORCEPOINT LLC
To: CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH, AS COLLATERAL AGENT
Reel/Frame 055052/0302 →
RELEASE OF SECURITY INTEREST IN PATENTS Recorded Jan 8, 2021
From: RAYTHEON COMPANY
To: FORCEPOINT LLC
Reel/Frame 055452/0207 →
RELEASE OF SECURITY INTEREST IN PATENTS Recorded Jan 8, 2021
From: RAYTHEON COMPANY
To: REDOWL ANALYTICS, INC.
Reel/Frame 055492/0241 →
PATENT SECURITY AGREEMENT SUPPLEMENT Recorded Feb 27, 2020
From: FORCEPOINT LLC
To: RAYTHEON COMPANY
Reel/Frame 052045/0482 →
PATENT SECURITY AGREEMENT Recorded Feb 12, 2018
From: REDOWL ANALYTICS, INC.
To: RAYTHEON COMPANY
Reel/Frame 045307/0578 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 5, 2017
From: LOSPINOSO, JOSH; FILIPPELLI, GUY LOUIS
To: REDOWL ANALYTICS, INC.
Reel/Frame 041268/0387 →