# US 8,752,073 B2 · Generating event definitions based on spatial and relational relationships

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- Number: US 8,752,073 B2
- Document ID: US8752073B2
- Status: Granted
- Kind: B2
- Application number: 13/471,302
- Filed: 2012-05-14
- Published: 2012-11-15
- Granted: 2014-06-10
- Earliest claimed: 2011-05-13
- Assignees: Orions Digital Systems, Inc.
- Applicants: Nils B. Lahr
- Inventors: Nils B. Lahr
- Web page: https://patentgrep.com/patent/US8752073B2

## CPC

- G07F 17/3241: {Security aspects of a gaming system, e.g. detecting cheating, device integrity, surveillance (computer security G06F21/00)}
- A61B 5/0002: {Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network (endoradiosondes A61B5/07)}
- G07F: COIN-FREED OR LIKE APPARATUS (coin sorting G07D3/00; coin testing G07D5/00; {handling coins or paper currencies apart from payment activated apparatus G07D; payment architectures, schemes or protocols G06Q20/00})
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)

## Abstract

Data from one or more sensors are used to detect an event corresponding to an activity by a person. The detection of the event is then used to loosely model the event to produce an initial event definition. The initial event definition is used in searching historical sensor data to detect other prospective occurrences of the event. Based on the detection of these additional occurrences, the event definition is refined to produce a more accurate event definition. The resulting refined event definition can then be used with current sensor data to accurately detect when the activity is being carried out. For example, the activity may be the use of a video imaging device for cheating, and a casino can use the refined event definition with video imaging to detect when people are using the video imaging device for such purposes.

## Claims

### Claim 1

A method for generating event definitions to identify an event corresponding to an activity, comprising:
(a) defining a dynamic event detection architecture;
(b) generally specifying an event that may correspond to the activity;
(c) processing historical sensor data to detect at least one instance of the event that was generally specified;
(d) using modeling language derived from the historical sensor data for the at least one instance to create an initial event definition for the event;
(e) using the initial event definition to search the historical sensor data for additional occurrences of the event;
(f) based on the additional occurrences of the event that were identified in the search of the historical sensor data, creating a refined event definition; and
(g) using the refined event definition to detect an activity corresponding to the event.