REAL-TIME PROCESSING OF SPATIOTEMPORAL DATA

Number: 20190327579
Published: 2019-10-24
Filed: 2018-04-18
Assignee: Disney Enterprises, Inc.
Inventors: RANKIN, V; John Robert et al.
CPC: H04W4/80; G01C21/14; G06Q30/0201; H04W4/021; H04L67/12; H04W4/029
Verdict: Set aside real-time spatiotemporal data processing, generic location tracking
Source: Google Patents · FreePatentsOnline

Abstract

Systems and methods for detecting and processing spatiotemporal data are disclosed. Signals are received from user devices, where each of the signals includes an identifier of a respective user device that transmitted the signal. A first subset of the signals is determined, where each signal in the first subset was received from a first user device. A first signal in the first subset is identified to classify as a begin signal based on a configuration associated with a first physical area, and a second signal in the first subset is identified to classify as an end signal based on the configuration associated with the first physical area. An amount of time that elapsed between receiving the begin signal and receiving the end signal is determined, and an estimated spatiotemporal measure is generated based at least in part on the determined first amount of time.

Background

BACKGROUNDField of the Invention

The present disclosure generally relates to processing measures of time, and more specifically, to real-time detecting and processing of spatiotemporal data obtained from user devices.Description of the Related Art

Determining real-time temporal data is important when groups of people gather in any location. For example, administrators and employees need to know the current wait time for a queue in order to determine whether additional queues should be opened, the queue should be closed, and the like. Additionally, it is frequently important to accurately anticipate crowds and waits in order to better serve customers, such as by opening additional queues before the wait time gets too long. Current systems to process wait times, however, are inefficient and prone to inaccuracy.

One existing approach to determine the wait time of a queue is to simply visually determine how long the queue is. This approach is insufficient for several reasons, including that it requires an employee or other administrator to visually observe the queue, and does not take into account various factors that influence how quickly the queue will move. Other approaches similarly require at least one employee to manually track users in the queue, either using identifying cards, writing down times, or the like. Accordingly, there is a need for a solution that provides automated real-time processing of various spatiotemporal measures, such as wait time and

Claims

1. A method comprising: receiving a plurality of signals from a plurality of user devices, wherein each of the signals includes an identifier of a respective user device that transmitted the signal; determining a first subset of the plurality of signals, wherein each signal in the first subset was received from a first user device; identifying a first signal in the first subset to classify as a first begin signal based on a first configuration assigned to a first physical area; identifying a second signal in the first subset to classify as a first end signal based on the first configuration; determining a first amount of time that elapsed between receiving the first begin signal and receiving the first end signal; and generating a first estimated spatiotemporal measure based at least in part on the determined first amount of time. 2. The method of claim 1, the method further comprising: determining a plurality of subsets of the plurality of signals, wherein each respective subset in the plurality of subsets includes signals that were received from a respective user device; for each respective subset of the plurality of subsets: identifying a third signal in the respective subset to classify as a respective begin signal based on the first configuration; identifying a fourth signal in the respective subset to classify as a respective end signal based on the first configuration; determining a respective amount of time that elapsed between receiving the respective begin signal and receiving the respective end signal; and updating the first estimated spatiotemporal measure based at least in part on the determined respective amounts of time. 3. The method of claim 1, wherein the first estimated spatiotemporal measure comprises one of: (i) an estimated wait time; (ii) an estimated amount of time that an average user will stay in the first physical area; and (iii) an estimated travel time. 4. The method of claim 1, the method further comprising: determining a second subset of the plurality of signals, wherein each signal in the second subset was received from a second user device; identifying a third signal in the second subset to classify as a second begin signal based on the first configuration; and determining not to use signals received from the second user device when updating the first estimated spatiotemporal measure, based on an identified signal processing device that received the second begin signal. 5. The method of claim 1, the method further comprising: determining a second subset of the plurality of signals, wherein each signal in the second subset was received from a second user device; identifying a third signal in the second subset to classify as a second begin signal based on the first configuration; and identifying a fourth signal in the second subset to classify as a second end signal based on the first configuration; determining a second amount of time that elapsed between receiving the second begin signal and receiving the second end signal; and determining not to use signals received from the second user device when updating the first estimated spatiotemporal measure, based on determining that a difference between the first amount of time and the second amount of time exceeds a predefined threshold. 6. The method of claim 1, the method further comprising: determining a second subset of the plurality of signals, wherein each signal in the second subset was received from a second user device; identifying a third signal in the second subset to classify as a second begin signal based on the first configuration; identifying a fourth signal in the second subset to classify as a second end signal based on the first configuration; and determining not to use signals received from the second user device when updating the first estimated spatiotemporal measure, based on determining that a sequence of signals received before or after the identified third signal differs from a sequence of signals received before or after the identified fourth signal. 7. The method of claim 1, wherein generating the first estimated spatiotemporal measure based at least in part on the determined first amount of time comprises subtracting a predefined amount of time from the determined first amount of time. 8. The method of claim 1, wherein the first configuration defines the first begin signal as one of: (i) a first received signal of the plurality of signals, (ii) a last received signal of the plurality of signals, or (iii) a strongest signal of the plurality of signals. 9. The method of claim 1, wherein at least one of the plurality of signals is received by a signal processing device configurable to set a distance at which signals from user devices are detected. 10. A system comprising: a plurality of signal processing devices; and a computing device comprising a processor and a computer memory storing a program, which, when executed on the processor, performs an operation comprising: receiving a plurality of signals from a plurality of user devices, wherein each of the signals includes an identifier of a respective user device that transmitted the signal; determining a first subset of the plurality of signals, wherein each signal in the first subset was received from a first user device; identifying a first signal in the first subset to classify as a first begin signal based on a first configuration assigned to a first physical area; identifying a second signal in the first subset to classify as a first end signal based on the first configuration; determining a first amount of time that elapsed between receiving the first begin signal and receiving the first end signal; and generating a first estimated spatiotemporal measure based at least in part on the determined first amount of time. 11. The system of claim 10, the operation further comprising: determining a plurality of subsets of the plurality of signals, wherein each respective subset in the plurality of subsets includes signals that were received from a respective user device; for each respective subset of the plurality of subsets: identifying a third signal in the respective subset to classify as a respective begin signal based on the first configuration; identifying a fourth signal in the respective subset to classify as a respective end signal based on the first configuration; determining a respective amount of time that elapsed between receiving the respective begin signal and receiving the respective end signal; and updating the first estimated spatiotemporal measure based at least in part on the determined respective amounts of time. 12. The system of claim 10, wherein the first estimated spatiotemporal measure comprises one of: (i) an estimated wait time; (ii) an estimated amount of time that an average user will stay in the first physical area; and (iii) an estimated travel time. 13. The system of claim 10, the operation further comprising: determining a second subset of the plurality of signals, wherein each signal in the second subset was received from a second user device; identifying a third signal in the second subset to classify as a second begin signal based on the first configuration; and determining not to use signals received from the second user device when updating the first estimated spatiotemporal measure, based on an identified signal processing device of the plurality of signal processing devices that received the first begin signal. 14. The system of claim 10, the operation further comprising: determining a second subset of the plurality of signals, wherein each signal in the second subset was received from a second user device; identifying a third signal in the second subset to classify as a second begin signal based on the first configuration; and identifying a fourth signal in the second subset to classify as a second end signal based on the first configuration; determining a second amount of time that elapsed between receiving the second begin signal and receiving the second end signal; and determining not to use signals received from the second user device when updating the first estimated spatiotemporal measure, based on determining that a difference between the first amount of time and the second amount of time exceeds a predefined threshold. 15. The system of claim 10, the operation further comprising: determining a second subset of the plurality of signals, wherein each signal in the second subset was received from a second user device; identifying a third signal in the second subset to classify as a second begin signal based on the first configuration; identifying a fourth signal in the second subset to classify as a second end signal based on the first configuration; and determining not to use signals received from the second user device when updating the first estimated spatiotemporal measure, based on determining that a sequence of signals received before or after the identified third signal differs from a sequence of signals received before or after the identified fourth signal. 16. The system of claim 10, wherein generating the first estimated spatiotemporal measure based at least in part on the determined first amount of time comprises subtracting a predefined amount of time from the determined first amount of time. 17. The system of claim 10, wherein the first configuration defines the first begin signal as one of: (i) a first received signal of the plurality of signals, (ii) a last received signal of the plurality of signals, or (iii) a strongest signal of the plurality of signals. 18. A non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation comprising: receiving a plurality of signals from a plurality of user devices, wherein each of the signals includes an identifier of a respective user device that transmitted the signal; determining a first subset of the plurality of signals, wherein each signal in the first subset was received from a first user device; identifying a first signal in the first subset to classify as a first begin signal based on a first configuration assigned to a first physical area; identifying a second signal in the first subset to classify as a first end signal based on the first configuration; determining a first amount of time that elapsed between receiving the first begin signal and receiving the first end signal; and generating a first estimated spatiotemporal measure based at least in part on the determined first amount of time. 19. The non-transitory computer-readable storage medium of claim 18, the operation further comprising: determining a plurality of subsets of the plurality of signals, wherein each respective subset in the plurality of subsets includes signals that were received from a respective user device; for each respective subset of the plurality of subsets: identifying a third signal in the respective subset to classify as a respective begin signal based on the first configuration; identifying a fourth signal in the respective subset to classify as a respective end signal based on the first configuration; determining a respective amount of time that elapsed between receiving the respective begin signal and receiving the respective end signal; and updating the first estimated spatiotemporal measure based at least in part on the determined respective amounts of time. 20. The non-transitory computer-readable storage medium of claim 18, wherein the first estimated spatiotemporal measure comprises one of: (i) an estimated wait time; (ii) an estimated amount of time that an average user will stay in the first physical area; and (iii) an estimated travel time.