Systems and methods of using wireless location, context, and/or one or more communication networks for monitoring for, preempting, and/or mitigating pre-identified behavior

• US 10,477,342 B2
• Filed: 12/13/2017
• Issued: 11/12/2019
• Est. Priority Date: 12/15/2016
• Status: Active Grant

• Current Assignee: Conquer Your Addiction LLC
• Original Assignee: David H. Williams
• Inventors: Williams, David H.
• Primary Examiner(s): Phuong, Dai
• Application Number:
  US15/840,762
• Publication Number:
  US 20180176727A1
• Time in Patent Office: 699 Days
• Field of Search: 4554122, 4554141, 455418-419
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/4842 Monitoring progression or stage of a disease
• CPC Class Codes: A61B 5/6802 Sensor mounted on worn items
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/747 in case of emergency, i.e. alerting emergency services
• CPC Class Codes: A63F 9/12 Three-dimensional jig-saw puzzles
• CPC Class Codes: G06F 21/36 by graphic or iconic representation
• CPC Class Codes: G06F 2221/2111 Location-sensitive, e.g. geographical location, GPS
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 20/3224 Transactions dependent on location of M-devices
• CPC Class Codes: G06Q 20/401 Transaction verification
• CPC Class Codes: G06Q 20/4014 Identity check for transactions
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G07F 17/3237 about the players, e.g. profiling, responsible gaming, strategy/behavior of players, location of players
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: H04L 63/107 wherein the security policies are location-dependent, e.g. entities privileges depend on current location or allowing specific operations only from locally connected terminals
• CPC Class Codes: H04L 63/20 for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 12/06 Authentication
• CPC Class Codes: H04W 12/08 : Access security
• CPC Class Codes: H04W 4/021 : Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Exemplary embodiments are disclosed of systems and methods of using location, context, and/or one or more communication networks for monitoring for, preempting, and/or mitigating pre-identified behavior. For example, exemplary embodiments disclosed herein may include involuntarily, automatically, and/or wirelessly monitoring/mitigating undesirable behavior (e.g., addiction related undesirable behavior, etc.) of a person (e.g., an addict, a parolee, a user of a system, etc.). In an exemplary embodiment, a system generally includes a plurality of devices and/or sensors configured to determine, through one or more communications networks, a location of a person and/or a context of the person at the location; predict and evaluate a risk of a pre-identified behavior by the person in relation to the location and/or the context; and facilitate one or more actions and/or activities to mitigate the risk of the pre-identified behavior, if any, and/or react to the pre-identified behavior, if any, by the person.

Claims

1. A system for monitoring for and preempting pre-identified behavior(s) of one or more pre-identified persons, the system comprising a plurality of different devices, sensors, sensor arrays, and/or communications networks, the system configured to:

determine, through a plurality of measurements/readings taken by the plurality of different devices, sensors, sensor arrays, and/or communications networks, behavior(s) of at least one of the one or more pre-identified persons and context(s) associated with the behavior(s) of the at least one of the one or more pre-identified persons;

assess, evaluate, and predict a risk of a future occurrence(s) of a pre-identified addiction-related behavior(s) and associated context(s) by the at least one of the one or more pre-identified persons; and

facilitate one or more pre-identified actions and/or activities to preempt and/or lower the risk of a future occurrence(s) of the pre-identified addiction-related behavior(s) by the at least one of the one or more pre-identified persons;

whereby the system is configured such that the plurality of measurements/readings are taken by the plurality of different devices, sensors, sensor arrays, and/or communications networks without requiring the at least one of the one or more pre-identified persons to take the plurality of measurements/readings; and

/or whereby the system is configured to predict the risk of a future occurrence of the pre-identified addiction-related behavior(s) and associated context(s) by the at least one of the one or more pre-identified persons without requiring the at least one of the one or more pre-identified persons to actively participate in predicting the risk of future relapse;

wherein the system is configured to determine whether any of one or more addiction triggers predetermined in the system are active or present by comparing one or more settings for the at least one of the one or more pre-identified persons with data collected from one or more of the plurality of different devices, sensors, sensor arrays, and/or communications networks without requiring the at least one of the one or more pre-identified persons to actively participate in the data collection process by the one or more of the plurality of different devices, sensors, sensor arrays, and/or communications networks; and

wherein the one or more settings for the at least one of the one or more pre-identified persons include one or more of blood pressure, heart rate, skin temperature, body temperature, respiratory rate, perspiration, weight, exercise schedule, external temperature, noise levels/loudness, and/or noise types/frequency(ies); and

/or wherein the plurality of different devices, sensors, sensor arrays, and/or communications networks comprise one or more biometric, environmental, and/or behavioral sensors that provide biometric, environmental, and/or behavioral data for the at least one of the one or more pre-identified persons usable by the system in determining whether any of the one or more addiction triggers predetermined in the system are active or present before a relapse by the at least one of the one or more pre-identified persons in partaking and/or exposure to the addiction and/or without requiring the at least one of the one or more pre-identified persons to actively participate in the collection process of the biometric, environmental, and/or behavioral data by the one or more biometric, environmental, and/or behavioral sensors; and

wherein the system is configured to receive and process feedback and to adjust the plurality of different devices, sensors, sensor arrays, and/or communications networks including modifying one or more of the settings and increasing or decreasing a frequency of data collection in response to the feedback including actions, contexts, and behaviors of the at least one of the one or more pre-identified persons associated with the data.

2. The system of claim 1, wherein:

the pre-identified behavior(s) includes pre-identified addiction-related undesirable and desirable behavior(s);

the system is configured to be operable for automatically monitoring for and preempting the pre-identified addiction-related undesirable behavior(s) before a relapse by the at least one of the one or more pre-identified persons in partaking and/or exposure to the addiction; and

the system is configured to be operable for automatically monitoring for, and reinforcing the pre-identified addiction-related desirable behavior(s).

3. The system of claim 1, wherein the system is configured to:

determine, through the plurality of different devices, sensors, sensor arrays, and/or communications networks, a location of an addict and a context of the addict at the location;

assess, evaluate, and predict a risk of future relapse by the addict in relation to the location and the context before a relapse by the addict in partaking and/or exposure to the addiction and/or without the addict to actively participate in predicting the risk of future relapse; and

facilitate one or more pre-identified actions and/or activities to preempt and/or risk of future relapse.

4. The system of claim 1, wherein the system is configured to:

determine, through the plurality of different devices, sensors, sensor arrays, and/or communications networks, a location of the at least one of the one or more pre-identified persons and a context of the at least one of the one or more pre-identified persons at the location; and

determine whether any of the one or more addiction triggers, which are indicative of the risk of a future occurrence(s) of the pre-identified addiction-related behavior(s) by the at least one of the one or more pre-identified persons and predetermined in the system, are active or present based on the location, the context, and biometric, environmental, activity, and/or behavioral data for the at least one of the one or more pre-identified persons.

5. The system of claim 1, wherein the system is configured to determine whether any of the one or more addiction triggers indicative of the risk of a future relapse by an addict are active or present based on the behavior(s) of the addict and the context(s) associated with the behavior(s) of the addict as automatically determined through the plurality of different devices, sensors, sensor arrays, and/or communications networks without requiring the addict'"'"'s active participation and/or before a relapse by the addict in partaking and/or exposure to the addiction.

6. The system of claim 1, wherein the plurality of different devices, sensors, sensor arrays, and/or communications networks comprise one or more Internet of Things network(s).

7. The system of claim 1, wherein the system is configured to determine a location of the at least one of the one or more pre-identified persons via one or more of an Internet of Things network(s), cell tower identification (CID), cell tower triangulation, AFLT, TDOA, a beacon(s), a Bluetooth network, a Bluetooth low energy (BLE) network, radio frequency fingerprinting, real-time location systems (RLTS), Wi-Fi based location systems, radio frequency identification-based (RFID) location systems, a drone(s), crowdsourcing, cloud-based positioning, peer-to-peer, Zigbee, and/or simultaneous localization and mapping.

8. The system of claim 1, wherein the system is configured to assess, evaluate, and predict a risk of a future occurrence(s) of the pre-identified behavior(s) and associated context(s) by the at least one of the one or more pre-identified persons by using one or more of:

historical visitation patterns of the at least one of the one or more pre-identified persons; and

/or

monitoring social media.

9. The system of claim 1, wherein the system is configured to detect and track behavior of the at least one of the one or more pre-identified persons via the plurality of different devices, sensors, sensor arrays, and/or communications networks to determine applicability and value of behavior and to provide a corresponding incentive or disincentive for the at least one of the one or more pre-identified persons.

10. The system of claim 1, wherein the system is configured to facilitate avoidance of one or more predetermined locations by de-augmenting the one or more predetermined locations from one or more augmented reality and/or virtual reality applications.

11. The system of claim 1, wherein the system is configured to establish one or more geo-fences for one or more predetermined locations and to provide one or more alerts when the at least one of the one or more pre-identified persons crosses a geo-fence to enter or exit the predetermined location corresponding to the geo-fence and violates parameters associated with the geo-fence.

12. The system of claim 1, wherein the system is configured to use the plurality of different devices, sensors, sensor arrays, and/or communications networks to assess a likelihood that the at least one of the one or more pre-identified persons is an alcoholic and/or drug addict.

13. The system of claim 1, wherein:

the plurality of different devices, sensors, sensor arrays, and/or communications networks include one or more of a local network, a public network, a private network, the internet, the Internet of Things, a wireless network, a terrestrial network, a cloud network, a Bluetooth network, a beacon network, a cloud network, a peer-to-peer network, a drone network, a Zigbee network, a satellite network, and/or wireline network; and

the context(s) include a situation, an environment, and/or a state of mind of the at least one of the one or more pre-identified persons based on one or more of biometric, environmental, activity, and/or behavioral data of the at least one of the one or more pre-identified persons.

14. The system of claim 1, wherein the plurality of different devices, sensors, sensor arrays, and/or communications networks includes:

a plurality of sensors configured to monitor a location and/or the context(s) of the at least one of the one or more pre-identified persons at the location, one or more of the plurality of sensors being located in, on, and/or near the at least one of the one or more pre-identified persons; and

a plurality of interface devices configured to engage in interaction with the at least one of the one or more pre-identified persons, with one or more support persons for the at least one of the one or more pre-identified persons, and/or with one or more third parties in the event the system determines a relationship between the location and/or the context(s) and one or more triggers predetermined in the system that indicates a risk of a future occurrence(s) of the pre-identified behavior(s) by the at least one of the one or more pre-identified persons;

whereby the system is configured to select the interaction based on the one or more triggers and the location and/or the context(s) of the at least one of the one or more pre-identified persons at the location.

15. The system of claim 1, wherein the system is configured to develop and/or update a profile of the at least one of the one or more pre-identified persons including one or more predetermined actions to implement for the at least one of the one or more pre-identified persons depending on the prediction and evaluation of the risk of an occurrence(s) of the pre-identified behavior(s) by the at least one of the one or more pre-identified persons.

16. The system of claim 1, wherein

the system is configured to be usable by another one or more persons to automatically and/or involuntarily monitor a location of the at least one of the one or more pre-identified persons and the context(s) of the at least one of the one or more pre-identified persons at the location without requiring active participation and/or action(s) by the at least one of the one or more pre-identified persons during the automatic and/or involuntary monitoring.

17. The system of claim 1, wherein the one or more pre-identified actions and/or activities facilitated by the system include one or more of:

requesting the at least one of the one or more pre-identified persons to attend a nearby addiction support meeting, visit another one or more persons in a support network, and/or travel to a predetermined location for a certain activity; and

/or

automatically changing operation of a vehicle of the at least one of the one or more pre-identified persons to driverless; and

/or

automatically playing, in real time or a recording of, a voice, video, or holographic projection of a family member or a friend; and

/or

providing a location-based alternative and/or a location-based advertisement to the at least one of the one or more pre-identified persons via a mobile phone.

18. The system of claim 1, wherein the system is configured to restrict and condition access to the data of the least one of the one or more pre-identified persons collected by the plurality of different devices, sensors, sensor arrays, and/or communications networks based on selection of location-based data for the at least one of the one or more pre-identified persons from a plurality of options presented by the system for selection, the plurality of options including the location-based data and one or more other options.

19. The system of claim 1, wherein:

the system is configured to use one or more interface devices for interfacing with the at least one of the one or more pre-identified persons and to disseminate information to/from the at least one of the one or more pre-identified persons and/or one or more support persons for the at least one of the one or more pre-identified persons; and

the one or more interface devices comprise one or more of tangible and/or tactile interfaces including one or more of a display, illumination, sound, vibration, heat, and/or smell interface(s).

20. The system of claim 1, wherein the system is configured to:

detect one or more relationship(s) between a location and the context(s) and one or more predetermined triggers indicative of the risk of a future occurrence(s) of the pre-identified addiction-related behavior(s) by the at least one of the one or more pre-identified persons without requiring active participation and/or action(s) by the at least one of the one or more pre-identified persons when detecting the one or more relationship(s) and/or before a relapse by the at least one of the one more pre-identified persons in partaking and/or exposure to the addiction; and

based on the detected relationship(s), use one or more interface devices to interact with the at least one of the one or more pre-identified persons, with one or more support persons for the at least one of the one or more pre-identified persons, and/or with a third party.

21. The system of claim 1, wherein:

the pre-identified behavior(s) includes one or more pre-identified triggers indicative of the risk of a future occurrence(s) of the pre-identified addiction-related behavior(s) by the at least one of the one or more pre-identified persons; and

the system is configured for automatically monitoring for and preempting the one or more pre-identified triggers without requiring active participation and/or action(s) by the at least one of the one or more pre-identified persons during the automatic monitoring and/or before a relapse by the at least one of the one or more pre-identified persons in partaking and/or exposure to the addiction.

22. The system of claim 1, wherein the context(s) associated with the behavior(s) of the at least one of the one or more pre-identified persons comprises at least one or more interrelated conditions including situations, circumstances, events, environment, activities, and/or actions being done by, associated with, and/or around the one or more pre-identified persons, time, and/or location(s) of the one or more pre-identified persons as determined through the plurality of different devices, sensors, sensor arrays, and/or communications networks.

23. The system of claim 1, wherein:

the plurality of different devices, sensors, sensor arrays, and/or communications networks are configured to collect and/or report different types of data for the one or more pre-identified persons including biometric, environmental, activity, and/or behavioral data without requiring the at least one of the one or more pre-identified person to actively participate in the collection process of each of the different types of data by the plurality of different devices, sensors, sensor arrays, and/or communications networks; and

the system is configured to use the data collected and/or reported by the plurality of different devices, sensors, sensor arrays, and/or communications networks that meets one or more pre-identified criteria.

24. The system of claim 1, wherein the system is configured to determine whether one or more triggers indicative of the risk of a future occurrence(s) of the pre-identified addiction-related behavior(s) by the at least one of the one or more pre-identified persons and predetermined in the system are active or present based on the behavior(s) of at least one of the one or more pre-identified persons and context(s) associated with the behavior(s) of the at least one of the one or more pre-identified persons, as determined through the plurality of different devices, sensors, sensor arrays, and/or communications networks without requiring the at least one of the one or more pre-identified persons to actively participate in making the determination of whether one or more triggers are active or present and/or before a relapse by the at least one of the one or more pre-identified persons in partaking and/or exposure to the addiction.

25. The system of claim 24, wherein the system is configured to determine whether any of the one or more triggers predetermined in the system are active or present by a comparison of data from the plurality of different devices, sensors, sensor arrays, and/or communications networks with one or more settings predetermined in the system for the at least one of the one or more pre-identified persons, without requiring the at least one of the one or more pre-identified persons to actively participate in the data collection process by the plurality of different devices, sensors, sensor arrays, and/or communications networks.

26. The system of claim 1, wherein the system is configured to determine behavior(s) of at least one of the one or more pre-identified persons and context(s) associated with the behavior(s) of the at least one of the one or more pre-identified persons by a comparison of data from the plurality of different devices, sensors, sensor arrays, and/or communications networks with one or more settings for the at least one of the one or more pre-identified persons, without requiring the at least one of the one or more pre-identified persons to actively participate in the data collection process by the plurality of different devices, sensors, sensor arrays, and/or communications networks.

27. The system of claim 1, wherein the system is configured to allow a user to modify the one or more pre-identified persons, the pre-identified behavior(s) and the associated context(s), the pre-identified actions and/or activities, and/or the plurality of different devices, sensors, sensor arrays, and/or communications networks.

28. The system of claim 1, wherein:

the plurality of different devices, sensors, sensor arrays, and/or communications networks include one or more social networks; and

the plurality of different devices, sensors, sensor arrays, and/or communications networks include one or more devices, sensors, and/or sensor arrays remote from the one or more pre-identified persons.

29. The system of claim 1, wherein the plurality of different devices, sensors, sensor arrays, and/or communications networks comprises at least one sensor array including a plurality of different types of sensors operable for taking a plurality of different types of measurements/readings without requiring the at least one of the one or more pre-identified persons to take the plurality of different types of measurements/readings and usable by the system to determine the behavior(s) of the at least one of the one or more pre-identified persons and the context(s) associated with the behavior(s) of the at least one of the one or more pre-identified persons.

30. The system of claim 1, wherein the system is configured to use, in combination, the plurality of measurements/readings taken by the plurality of different devices, sensors, sensor arrays, and/or communications networks to determine when an addiction-related relapse-trigger is being activated that is indicative of a future relapse by the at least one of the one or more pre-identified persons through a pre-identification process and an iterative machine learning/artificial intelligence process incorporating human input, of high risk chance-of-relapse situations, to thereby enable the system to proactively and preemptively detect high-risk addiction relapse situations for the at least one of the one or more pre-identified persons.

31. The system of claim 1, wherein the pre-identified addiction-related behavior(s) includes a pre-addiction behavior by a person having considered high-risk for the addiction.

32. The system of claim 1, wherein the pre-identified addiction-related behavior(s) includes one or more of a substance use behavior, a substance abuse behavior, a substance addiction behavior, an activity behavior, an activity abuse behavior, and/or an activity addiction behavior.

33. The system of claim 32, wherein the pre-identified addiction-related behavior(s) includes one or more of an activity behavior, an activity abuse behavior, and/or an activity addiction behavior, wherein the activity behavior, activity abuse behavior, and/or activity addiction behavior includes one or more of a gambling behavior, a sex behavior, an eating behavior, a shopping behavior, a cigarette or nicotine behavior, a food avoidance or lack of food behavior, a work behavior, a sports viewing behavior, a beauty enhancement or plastic surgery behavior, a videogame behavior, an Internet surfing behavior, and/or a smartphone behavior.

34. A method for monitoring for and preempting pre-identified behavior(s) of one or more pre-identified persons, the method comprising:

determining behavior(s) of at least one of the one or more pre-identified persons and context(s) associated with the behavior(s) of the at least one of the one or more pre-identified persons via a plurality of measurements/readings automatically taken by a plurality of different devices, sensors, sensor arrays, and/or communications networks without requiring the at least one of the one or more pre-identified persons to take the plurality of measurements/readings;

assessing, evaluating, and predicting a risk of a future occurrence(s) of a pre-identified addiction-related behavior(s) and associated context(s) by the at least one of the one or more pre-identified persons;

facilitating one or more pre-identified actions and/or activities to preempt and/or lower the risk of a future occurrence(s) of the pre-identified addiction-related behavior(s) by the at least one of the one or more pre-identified persons;

wherein the method includes determining whether any of one or more addiction triggers are active or present by comparing one or more settings for the at least one of the one or more pre-identified persons with data collected from one or more of the plurality of different devices, sensors, sensor arrays, and/or communications networks without requiring the at least one of the one or more pre-identified persons to actively participate in the data collection process by the one or more of the plurality of different devices, sensors, sensor arrays, and/or communications networks; and

wherein the one or more settings for the at least one of the one or more pre-identified persons include one or more of blood pressure, heart rate, skin temperature, body temperature, respiratory rate, perspiration, weight, exercise schedule, external temperature, noise levels/loudness, and/or noise types/frequency(ies); and

/or wherein the plurality of different devices, sensors, sensor arrays, and/or communications networks comprise one or more biometric, environmental, and/or behavioral sensors that provide biometric, environmental, and/or behavioral data for the at least one of the one or more pre-identified persons usable in determining whether any of the one or more addiction triggers are active or present before a relapse by the at least one of the one or more pre-identified persons in partaking and/or exposure to the addiction and/or without requiring the at least one of the one or more pre-identified persons to actively participate in the collection process of the biometric, environmental, and/or behavioral data by the one or more biometric, environmental, and/or behavioral sensors; and

wherein the method includes receiving and processing feedback and adjusting the plurality of different devices, sensors, sensor arrays, and/or communications networks including modifying one or more of the settings and increasing or decreasing a frequency of data collection in response to the feedback including actions, contexts, and behaviors of the at least one of the one or more pre-identified persons associated with the data.

35. The method of claim 34, wherein the method includes:

detecting a relationship between a location and the context(s) and one or more predetermined triggers indicative of the risk of a future occurrence(s) of the pre-identified addiction-related behavior(s) by the at least one of the one or more pre-identified person without requiring the at least one of the one or more pre-identified persons to actively participate in detecting the relationship and/or before a relapse by the at least one of the one more pre-identified persons in partaking and/or exposure to the addiction; and

based on the detected relationship, using one or more interface devices to interact with the at least one of the one or more pre-identified persons, with one or more support persons for the at least one of the one or more pre-identified persons, and/or with a third party.

36. The method of claim 34, wherein the method includes determining whether a location and the context(s) correspond to a trending risk of the pre-identified behavior(s), then identifying one or more potential actions to preempt and/or lower the risk of a future occurrence(s) of the pre-identified behavior(s), selecting one or more actions and one or more interfaces for the at least one of the one or more pre-identified persons, and implementing the selected action(s) and interface(s) for the at least one of the one or more pre-identified persons.

37. The method of claim 34, wherein the method includes predicting a current or future context of the at least one of the one or more pre-identified persons at a location by analyzing and linking real-time data and historical data for the at least one of the one or more pre-identified persons, the real-time and historical data including the location of the at least one of the one or more pre-identified persons, historical context of the at least one of the one or more pre-identified persons at the location, behavior patterns, travel patterns, health data, and risk calculations.

38. The method of claim 34, wherein facilitating one or more pre-identified actions and/or activities comprises:

determining which of the plurality of different devices, sensors, sensor arrays, and/or communications networks are in use;

determining available interfaces on the plurality of different devices, sensors, sensor arrays, and/or communications networks that are determined to be in use;

determining an inventory of potential interfaces desired by selected actions and that satisfy a privacy requirement and/or live 2-way communication requirement; and

selecting and implementing one or more interfaces from the inventory of potential interfaces.

39. The method of claim 34, wherein the method includes:

determining, through the plurality of different devices, sensors, sensor arrays, and/or communications networks, a location of an addict and/or a context of the addict at the location;

assessing, evaluating, and predicting a risk of future relapse by the addict in relation to the location and/or the context before a relapse by the addict in partaking and/or exposure to the addiction and/or without requiring active participation and/or action(s) by the addict when predicting the risk of future relapse; and

facilitating one or more pre-identified actions and/or activities to preempt and/or lower the risk of future relapse.

40. The method of claim 34, wherein:

the method includes determining whether one or more predetermined addiction triggers indicative of the risk of a future occurrence(s) of the pre-identified addiction-related behavior(s) by the at least one of the one more pre-identified persons are active or present based on a location, the context(s), and biometric, environmental, activity, and/or behavioral data for the at least one of the one or more pre-identified persons without requiring the at least one of the one or more pre-identified persons to actively participate in the collection process of the biometric, environmental, activity, and/or behavioral data and/or before a relapse by the at least one of the one or more pre-identified persons in partaking and/or exposure to the addiction.

41. A non-transitory computer-readable storage media comprising computer-executable instructions for monitoring for and preempting pre-identified behavior(s) of one or more pre-identified persons, which when executed by at least one processor, cause the at least one processor to:

determine behavior(s) of at least one of the one or more pre-identified persons and context(s) associated with the behavior(s) of the at least one of the one or more pre-identified persons via a plurality of measurements/readings taken by a plurality of different devices, sensors, sensor arrays, and/or communications networks without requiring the at least one of the one or more pre-identified persons to take the plurality of measurements/readings;

based on the determination and historical location-based data for the one or more pre-identified persons, assess, evaluate, and predict a risk of a future occurrence(s) of a pre-identified behavior(s) and associated context(s) by the at least one of the one or more pre-identified persons;

facilitate one or more pre-identified actions and/or activities to preempt and/or lower the risk of the future occurrence(s) of the pre-identified behavior(s) by the at least one of the one or more pre-identified persons;

determine whether any of one or more addiction triggers are active or present by comparing one or more settings for the at least one of the one or more pre-identified persons with data collected from one or more of the plurality of different devices, sensors, sensor arrays, and/or communications networks without requiring the at least one of the one or more pre-identified persons to actively participate in the data collection process by the one or more of the plurality of different devices, sensors, sensor arrays, and/or communications networks; and

receive and process feedback and adjust the plurality of different devices, sensors, sensor arrays, and/or communications networks including modifying one or more of the settings and increasing or decreasing a frequency of data collection in response to the feedback including actions, contexts, and behaviors of the at least one of the one or more pre-identified persons associated with the data;

wherein the one or more settings for the at least one of the one or more pre-identified persons include one or more of blood pressure, heart rate, skin temperature, body temperature, respiratory rate, perspiration, weight, exercise schedule, external temperature, noise levels/loudness, and/or noise types/frequency(ies); and

/or wherein the plurality of different devices, sensors, sensor arrays, and/or communications networks comprise one or more biometric, environmental, and/or behavioral sensors that provide biometric, environmental, and/or behavioral data for the at least one of the one or more pre-identified persons usable in determining whether any of the one or more addiction triggers are active or present before a relapse by the at least one of the one or more pre-identified persons in partaking and/or exposure to the addiction and/or without requiring the at least one of the one or more pre-identified persons to actively participate in the collection process of the biometric, environmental, and/or behavioral data by the one or more biometric, environmental, and/or behavioral sensors.

42. The non-transitory computer-readable storage media of claim 41, wherein:

the plurality of measurements/readings taken by a plurality of different devices, sensors, sensor arrays, and/or communications networks include one or more of a local network, a public network, a private network, the internet, the Internet of Things, a wireless network, a terrestrial network, a cloud network, a Bluetooth network, a beacon network, a cloud network, a peer-to-peer network, a drone network, a Zigbee network, a satellite network, and/or wireline network; and

the determination of the context is based on one or more of biometric, environmental, activity, and/or behavioral data of the at least one of the one or more pre-identified persons; and

the pre-identified behavior(s) includes pre-identified addiction-related undesirable and desirable behavior(s).