Systems and methods for dynamic report generation based on automatic modeling of complex data structures

Patent metadata

Field	Value
Publication number	US-10445152-B1
Application number	US-201514975440-A
Country	US
Kind code	B1
Filing date	Dec 18, 2015
Priority date	Dec 19, 2014
Publication date	Oct 15, 2019
Grant date	Oct 15, 2019

Abstract

Various systems and methods are disclosed for accessing and traversing disparate, complex, and multi-dimensional data structures to dynamically and interactively generate reports based on automated modeling of complex and non-uniformly formatted data. Automated analysis of probabilistic functions and temporal-based data records enable non-technical users to quickly and dynamically act on time-sensitive information. In response to various user inputs, the system automatically accesses and traverses complex data structures (including, for example, frequency distribution models) calculates complex data based on the traversals, displays the calculated complex data to the user, and enters the calculated complex data into the reports.

First claim

What is claimed is:

1. A computing system operable to access one or more electronic data sources in response to periodic automated inquiries in order to automatically calculate data for inclusion into a report, the computing system comprising:
a non-transitory storage device configured to store a plurality of event records associated with respective users, each of the event records indicating an event associated with a respective user; and
a physical processor that is in communication with the non-transitory storage device and that is configured to:
access the plurality of event records associated with respective users; and
for each individual event record of at least a subset of the event records, assign a category to the individual event record, the category selected from a plurality of predetermined categories;
generate a user profile for a particular user, wherein the user profile comprises categorized event records associated with the particular user during a set time period;
generate a user event frequency distribution model based on at least some of the categorized event records in the user profile of the particular user of a particular category, wherein the user event frequency distribution model predicts a likelihood that the particular user will engage in a future event in the particular category within a specified period of time;
access the generated user profile for the particular user and the user event frequency distribution model;
determine a gap for the particular user, the gap indicating a time period since a most recent event associated with the particular category by the particular user occurred;
determine a gap limit associated with the particular user, the gap limit indicating a period of time by which the particular user is expected to engage in the future event with the predicted likelihood based on the user event frequency distribution model;
compare the determined gap to the gap limit;
in response to determining that the gap is greater than the gap limit, trigger generation of an event change alert; and
transmit, to a client system, the generated event change alert indicating that the particular user has changed event behavior in the particular category.

2. The computing system of claim 1 , wherein the physical processor is further configured to:
determine, in response to determining that the gap is greater than the gap limit, if a filter condition exists; and
in response to determining that a filter condition does not exist, trigger the generation of the event change alert.

3. The computing system of claim 1 , wherein the gap limit is a period of time in which the particular user is expected to make a next event within a ninety-five percent (95%) probability, based on the user event frequency distribution model for the particular user.

4. The computing system of claim 1 , wherein the physical processor is further configured to:
generate a category baseline event frequency distribution model for a particular category, the category baseline event frequency distribution model indicating a likelihood of an event in the particular category by a generic user based on a set of the accessed plurality of event records that are assigned to the particular category; and
update the category baseline event frequency distribution model for the particular category based on a set of the categorized event records of the particular user and associated with the particular category to generate a category-specific user event frequency distribution model.

5. The computing system of claim 4 , wherein the physical processor is further configured to:
periodically access event data sources to determine whether there is an additional event record associated with the particular user and associated with the particular category; and
in response to determining that there is an additional event record associated with the particular user and associated with the particular category, update the category-specific user event frequency distribution model based on the determined additional event record.

6. The computing system of claim 4 , wherein the physical processor is further configured to:
determine a second gap, indicating a time period since the most recent event by the particular user associated with the particular category occurred;
determine a second gap limit indicating a second expected period of time between events associated with the particular user and associated with the particular category;
compare the second gap to the second gap limit;
in response to determining that the second gap is greater than the second gap limit, trigger generation of a category-specific event change alert; and
transmit, to a client system, the category-specific event change alert.

7. The computing system of claim 1 , wherein the physical processor is further configured to:
generate an event frequency distribution model for the additional event record associated with the particular user; and
calculate a weighted sum of the event distribution for the additional event record and the user event frequency distribution model to generate the updated user event frequency distribution model.

8. The computing system of claim 7 , wherein the user event frequency distribution model for the additional event record associated with the particular user comprises a distribution having a one hundred percent (100%) probability of occurring within a time period between the most recent event by the particular user and a time of an event associated with the additional event record associated with the particular user.

9. The computing system of claim 7 , wherein the user event frequency distribution model for the additional event record associated with the particular user comprises a distribution centered on a time period between the most recent event by the particular user and a time of an event associated with the additional event record.

10. The computing system of claim 1 , wherein the physical processor is further configured to generate the event change alert comprising an identification of an event category associated with the event change alert, a number of days since the most recent event by the particular user occurred, and a number of events the particular user has made within a preceding two months.

11. The computing system of claim 1 , wherein the physical processor is further configured to:
in response to determining that a filter condition exists, determine whether the filter condition is met; and
in response to determining that a filter condition is not met, generate an event change alert indicating that the gap is greater than the gap limit; and
transmit, to a client system, the event change alert, the event change alert including an identification of an event category associated with the event change alert, a number of days since a last event by the particular user occurred, and a number of events the particular user has made within a preceding two months.

12. The computing system of claim 1 , further comprising a card reader in communication with the physical processor, the card reader including:
a payment information detector configured to detect payment information for an event of a user;
a targeted content generator configured to:
receive event data during the event of the user; and
identify content stored by the card reader using a comparison between a content selection rule and the event data, said content for presentation via the card reader; and

a display configured to present the content to the user.

13. A method of automatically generating a transaction frequency change alert, the method comprising:
accessing, from a transaction

Assignees

• Experian Inf Solutions Inc

Inventors

• Zhang Lingyun
• Carpenter Mason L
• Bonin Gregor
• Xiong Shanji
• Dichiara Christer
• Zaleta David
• Tao Yaqi

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F40/30

  Semantic analysis · CPC title

• G06F40/216 Primary

  using statistical methods · CPC title

• G06N20/00

  Machine learning · CPC title

• G06Q20/4016

  involving fraud or risk level assessment in transaction processing · CPC title