Dynamic and adaptive systems and methods for rewarding and/or disincentivizing behaviors
The present disclosure generally relates to dynamic and adaptive systems and methods for rewarding and/or disincentivizing behaviors.
1 . A 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 and/or inferred through information from system inputs, behavior(s) of at least one entity and context(s) associated with the behavior(s) of the at least one entity, wherein:
the system is further configured to assess, evaluate, and predict, within a predetermined time frame, a risk or trending risk of a behavior(s) and associated context(s) by the at least one entity;
the system is configured to, without requiring manual human intervention, dynamically and adaptively determine:
a reward for incentivizing behavior for an associated context of the at least one entity based at least in part on a computed decrease that the incentivized behavior would have on the predicted risk or trending risk of the behavior(s) by the at least one entity; and/or
a disincentive for disincentivizing behavior for an associated context of the at least one entity based at least in part on a computed increase that the disincentivized behavior would have on the predicted risk or trending risk of the behavior(s) by the at least one entity;
the dynamic and adaptive determining includes automatically selecting a type, level, and amount of the reward and/or the disincentive responsive to how, where, and by how much the predicted risk or trending risk of the behavior(s) by the at least one entity changes as determined from sensor-detected trigger(s) and contextual features.
2 . The system of claim 1 , wherein the plurality of different devices, sensors, sensor arrays, and/or communications networks are usable to measure individual behavior(s) and associated context(s) of the at least one entity and associated participation and/or performance of the at least one entity in the behavior(s) and associated context(s) as it relates to the participation in and/or the performance of one or more production and/or delivery processes.
3 . The system of claim 2 , wherein the one or more production and/or delivery processes comprises a plurality of parts, steps, and/or sub-processes that can be monitored and/or measured to determine the participation and/or the performance of the at least one entity in the plurality of parts, steps, and/or sub-processes by using data and/or information gathered and/or reported by the plurality of different devices, sensors, sensor arrays, and/or communications networks.
4 . The system of claim 3 , wherein the system is configured to dynamically and adaptively determine a reward for the participation and/or performance by the at least one entity for completion of each corresponding one of the plurality of parts, steps, and/or sub-processes of the one or more production and/or delivery processes.
5 . The system of claim 3 wherein the system is configured to:
dynamically and adaptively determine a reward for incentivizing a specific behavior for associated context(s) of the at least one entity, which said reward is based on the participation and/or performance associated with the at least one entity relative at least one of the plurality of parts, steps, and/or sub-processes of the one or more production and/or delivery processes; and
dynamically and adaptively determine a disincentive for disincentivizing a specific behavior for associated context(s) of the at least one entity, which said disincentive is based on the participation and/or performance associated with the at least one entity relative to at least one of the plurality of parts, steps, and/or sub-processes of the one or more production and/or delivery processes.
6 . The system of claim 3 , wherein:
the system is configured to be operable such that receiving of and/or redemption of the reward and/or disincentive is based on the at least one entity's participation and/or performance in at least one of the plurality of parts, steps, and/or sub-processes relative to participation and/or performance target(s) associated with the plurality of parts, steps, and/or sub-processes;
a reward is associated with meeting or exceeding the participation and/or performance target(s);
a disincentive is associated with not meeting or exceeding the participation and/or performance targets; and
they at least one entity's participation and/or performance target(s) are adjusted based on the context(s) of the behavior occurring during and/or associated with the at least one entity's participation and/or performance of at least one of the plurality of parts, steps, and/or sub-processes of the one or more production and/or delivery processes.
7 . The system of claim 1 , wherein:
the at least one entity comprises one or more of a robot, an artificial intelligence, an animal, a virtual agent, a corporation, a business entity, a nation, a network, a driverless vehicle, a connected vehicle, a drone, and/or a governmental entity; or
the at least one entity comprises a human and/or a machine.
8 . The system of claim 1 , wherein:
the system is configured to dynamically and adaptively determine the reward for incentivizing behavior for an associated context of the at least one entity, and facilitate redemption of the reward including one or more of a material reward, a physical reward, a financial reward, a monetary reward, an electronic reward, a virtual reward, a non-material reward, and a non-financial reward; and
the system is further configured to dynamically and adaptively determine a disincentive for disincentivizing behavior for an associated context of the at least one entity and facilitate redemption of the disincentive by enacting/applying one or more of a material punishment or penalty, a physical punishment or penalty, a financial punishment or penalty, a monetary punishment or penalty, an electronic punishment or penalty, a virtual punishment or penalty, a non-material punishment or penalty, and a non-financial punishment or penalty.
9 . The system of claim 1 , wherein the system is configured to capture transactions in a system of record for tracking, managing, and redeeming reward(s) and disincentive(s), the system of record including one or more of a ledger, distributed ledger, or blockchain system, and wherein each transaction records participation and/or performance at a step, part, and/or sub-process together with a corresponding assignment or adjustment of an electronic or physical store of value that is redeemable, transferable, and/or combinable.
10 . The system of claim 9 , wherein the context(s) associated with the behavior(s) of the at least one entity includes at least one of:
a physical location of the at least one entity and a context of the at least one entity at the physical location; and
a virtual location of the at least one entity and a context of the at least one entity at the virtual location.
11 . The system of claim 1 , wherein the system is configured to:
facilitate redemption of the reward to thereby incentivize and encourage the behavior for the associated context of the at least one entity by automatically assigning, rewarding, and/or allocating one or more of a material reward, a physical reward, a financial reward, a monetary reward, an electronic reward, a virtual reward, a non-material reward, and a non-financial reward; and/or
facilitate redemption of the disincentive to thereby disincentivize and discourage the behavior for the associated context of the at least one entity by automatically enacting/applying one or more of a material punishment or penalty, a physical punishment or penalty, a financial punishment or penalty, a monetary punishment or penalty, an electronic punishment or penalty, a virtual punishment or penalty, a non-material punishment or penalty, and a non-financial punishment or penalty;
wherein the assignment/rewarding/allocation of the reward or enactment/application of the disincentive is parameterized responsive to how, where, and by how much the predicted risk or trending risk of the behavior(s) by the at least one entity changes.
12 . The system of claim 1 , wherein the system is configured to assess, evaluate, and predict a risk or trending risk of a future occurrence(s) of an undesirable behavior(s) within a predetermined time frame and associated context(s) by the at least one entity.
13 . The system of claim 12 , wherein the system is configured to:
dynamically and adaptively determine a reward for incentivizing behavior for an associated context that decreases the predicted risk or trending risk of a future occurrence(s) of the undesirable behavior(s) by the at least one entity before the behavior(s) occurs; and
dynamically and adaptively determine a disincentive for disincentivizing behavior for an associated context that increases the predicted risk or trending risk of a future occurrence(s) of the undesirable behavior(s) by the at least one entity before the behavior(s) occurs.
14 . The system of claim 13 , wherein the system is configured to:
dynamically monitor for and detect the incentivized behavior for the associated context that decreases the predicted risk or trending risk of a future occurrence(s) of the behavior(s) by the at least one entity before the behavior(s) occurs, and to parameterize the reward's type, level, and amount based on how, where, and by how much the predicted risk or trending risk of a future occurrence(s) of the undesirable behavior(s) by the at least one entity changes; and
dynamically monitor for and detect the disincentivized behavior for the associated context that increases the predicted risk or trending risk of a future occurrence(s) of the behavior(s) by the at least one entity before the behavior(s) occurs, and to parameterize the disincentive's type, level, and amount based on how, where, and by how much the predicted risk or trending risk of a future occurrence(s) of the undesirable behavior(s) by the at least one entity changes.
15 . The system of claim 13 , wherein the system is configured to:
dynamically and adaptively determine the reward type, level, and amount based upon how, where, and by how much the predicted risk or trending risk of a future occurrence(s) of the undesirable behavior(s) by the at least one entity was decreased by the incentivized behavior for the associated context, which is computed without requiring manual human intervention; and
dynamically and adaptively determine the disincentive type, level, and amount based upon how, where, and by how much the predicted risk or trending risk of a future occurrence(s) of the undesirable behavior(s) by the at least one entity was increased by the disincentivized behavior for the associated context, which is computed without requiring manual human intervention.
16 . The system of claim 12 , wherein the system is configured to:
dynamically and adaptively determine a reward for incentivizing behavior for an associated context that increases a likelihood of a future occurrence(s) of the undesirable behavior(s) by the at least one entity before the behavior(s) occurs; and
dynamically and adaptively determine a disincentive for disincentivizing behavior for an associated context that decreases the likelihood of a future occurrence(s) of the undesirable behavior(s) by the at least one entity before the behavior(s) occurs.
17 . The system of claim 16 , wherein the system is configured to:
dynamically monitor for and detect the incentivized behavior for the associated context that increases the likelihood of a future occurrence(s) of the undesirable behavior(s) by the at least one entity before the behavior(s) occurs; and
dynamically monitor for and detect the disincentivized behavior for the associated context that decreases the likelihood of a future occurrence(s) of the undesirable behavior(s) by the at least one entity before the behavior(s) occurs.
18 . The system of claim 16 , wherein the system is configured to:
dynamically and adaptively determine the reward type, level, and amount based upon how, where, and by how much the likelihood of a future occurrence(s) of the undesirable behavior(s) by the at least one entity was decreased by the incentivized behavior for the associated context; and
dynamically and adaptively determine the disincentive type, level, and amount based upon how, where, and by how much the likelihood of a future occurrence(s) of the undesirable behavior(s) by the at least one entity was increased by the disincentivized behavior for the associated context.
19 . The system of claim 1 , wherein the system is configured to:
determine whether at least one trigger indicative of a risk of a future occurrence(s) of the behavior(s) by the at least one entity is active or present based on the behavior(s) of the at least one entity and the context(s) associated with the behavior(s) of the at least one entity, as determined through the plurality of different devices, sensors, sensor arrays, and/or communications networks and/or inferred through information from system inputs; and
determine a reward for incentivizing behavior for an associated context that eliminates or reduces the at least one trigger.
20 . The system of claim 1 , wherein the system is configured to:
determine whether at least one trigger is active or present based on the behavior(s) of the at least one entity and the context(s) associated with the behavior(s) of the at least one entity, as determined through the plurality of different devices, sensors, sensor arrays, and/or communications networks and/or inferred through information from system inputs; and
determine a reward for incentivizing behavior for an associated context that caused or created the at least one trigger to be active or present.
21 . The system of claim 20 , wherein the at least one trigger comprises at least one positive trigger including one or more of an aspiration trigger, a goal trigger, a happiness trigger, or other positive trigger associated with behavior to be incentivized and encouraged by the system.
22 . The system of claim 1 , wherein the system is configured to:
determine whether at least one or more of an Anger trigger, an Anxiety trigger, a Boredom trigger, a Depression trigger, a Fear trigger, and a Frustration trigger that are indicative of a risk of a future occurrence(s) of the behavior(s) by the at least one entity is active or present based on the behavior(s) of the at least one entity and the context(s) associated with the behavior(s) of the at least one entity, as determined through the plurality of different devices, sensors, sensor arrays, and/or communications networks and/or inferred through information from system inputs; and
determine a reward for incentivizing behavior for an associated context that eliminates or reduces the at least one or more of an Anger trigger, an Anxiety trigger, a Boredom trigger, a Depression trigger, a Fear trigger, and a Frustration trigger.
23 . The system of claim 1 , wherein the system is configured to dynamically and adaptively determine the reward and/or the disincentive by using one or more of machine learning, a neural network, a quantum network, and/or an artificial intelligence that operate to compute:
predicted risk or trending risk of the behavior(s) by the at least one entity; and
change in the predicted risk or trending risk of the behavior(s) by the at least one entity.
24 . The system of claim 1 , wherein the system is configured to dynamically and adaptively determine the reward and/or the disincentive without requiring manual human intervention to create, define, manage, and administer the reward and/or the disincentive by using one or more of machine learning, a neural network, a quantum network, and an artificial intelligence to:
compute predicted risk or trending risk of the behavior(s) by the at least one entity;
compute change in the predicted risk or trending risk of the behavior(s) by the at least one entity;
determine type, level, and amount of the reward and/or the disincentive; and
initiate assigning, rewarding, and/or allocation of the reward and/or enactment of the disincentive.
25 . The system of claim 1 , wherein the system is configured to:
use a blockchain system or other distributed ledger technology as the system of record;
use artificial intelligence to monitor and analyze a blockchain trail to make predictions regarding behaviors and/or behavior patterns; and
control redemption, transfer, and/or combination of stores of value via smart contracts.
26 . The system of claim 1 , wherein the system is configured to:
determine a blockchain verifiable and/or cybercurrency based reward for incentivizing behavior for an associated context of the at least one entity; and
facilitate redemption of the blockchain verifiable and/or cybercurrency based reward.
27 . The system of claim 1 , wherein the context(s) associated with the behavior(s) of the at least one entity include four or more 5W1H (who, what, when, where, why, how) attributes.
28 . The system of claim 1 , wherein the context(s) associated with the behavior(s) of the at least one entity include four or more of:
where is the location(s) of the at least one entity;
why the at least one entity is at the location(s);
who or what is virtually and/or actually with the at least one entity at the location(s) or nearby the location(s) within audible, visual, and/or electronic detection range of the devices, sensors, and/or communication network(s);
what the at least one entity is doing at the location(s);
when the at least one entity is at the location(s);
how the at least one entity arrived at the location(s) and/or how will the at least one entity leave the location(s); and
an environmental condition at the location(s).
29 . The system of claim 1 , wherein the system is configured to determine whether a trigger and at least one related trigger for the at least one entity is active, in danger of becoming active, or imminent, including two or more of an Anger trigger, an Anxiety trigger, a Boredom trigger, a Depression trigger, a Fear trigger, a Frustration trigger, a Job trigger, a Personal Relationship trigger, a Stress trigger, a Despair trigger, a Self-Loathing trigger, a Resentment trigger, an Information Overload/Snowball Effect trigger, and/or a Yelling trigger.
30 . The system of claim 1 , wherein the system is configured to:
determine whether (a) the context(s) associated with the behavior(s) of the at least one entity corresponds to a high-risk context(s) or (b) a location and the context(s) associated with the behavior(s) of the at least one entity at the location correspond to a high-risk location and context(s); and
enhance or increase the type, level, and/or amount of the reward and/or the disincentive when (a) the context(s) associated with the behavior(s) of the at least one entity is determined to correspond to a high-risk context(s) or (b) the location and the context(s) associated with the behavior(s) of the at least one entity at the location are determined to correspond to a high-risk location and context(s).
31 . The system of claim 1 , wherein:
the system is configured to:
capture contextual information associated with a media file, the contextual information including at least one of: a time of capture, a physical and/or virtual location, and one or more unique identifiers, which may include unique or descriptive attributes, of persons, objects, or features detected or associated with the capture environment; and
associate the contextual information with a file or frame reference of the media file; and
the system includes a blockchain-based ledger configured to:
store, optionally in hashed or encrypted form, the contextual information and the file or frame reference;
record placement data or an algorithm defining the location of one or more unique identifiers embedded within the media file; and
enable subsequent verification of authenticity by comparing the contextual information stored in the blockchain with the corresponding media frame or segment; wherein the placement of the unique identifier within the media file is determined according to a randomization or algorithmic scheme, and the scheme or placement data is recorded in the blockchain.
32 . The system of claim 1 , wherein the system is configured to:
assess, using data captured through a plurality of measurements/readings taken by the plurality of different devices, sensors, sensor arrays, and/or communications networks, a risk(s) of a trigger-related episode(s) and behavior(s) of at least one person and
(a) context(s) associated with the trigger-related episode(s) and behavior(s) of the at least one person; or
(b) location(s) and the context(s) associated with the trigger-related episode(s) and behavior(s) of the at least one person;
analyze the measurements/readings, the trigger-related episode(s) and behavior(s) of the at least one person, and (a) the context(s) associated with the trigger-related episode(s) and behavior(s) of the at least one person or (b) the location(s) and the context(s) associated with the trigger-related episode(s) and behavior(s) of the at least one person, to thereby determine a risk(s) of an trigger-related episode(s) and behavior(s) of the at least one person relative to a trigger threshold(s); and
facilitate one or more actions to lower the risk(s) of the trigger-related episode(s) and behavior(s) of the at least one person associated with a trigger(s) from reaching or exceeding the trigger threshold(s) associated with the trigger-related episode(s) and behavior(s) of the at least one person before the trigger-related episode(s) and behavior(s) of the at least one person occurs.
33 . The system of claim 32 , wherein the trigger is a Stress trigger, and wherein the system is configured to:
assess, using data captured through a plurality of measurements/readings taken by the plurality of different devices, sensors, sensor arrays, and/or communications networks, a risk(s) of a stress-related episode(s) and behavior(s) of at least one person and
(a) context(s) associated with the stress-related episode(s) and behavior(s) of the at least one person; or
(b) location(s) and the context(s) associated with the stress-related episode(s) and behavior(s) of the at least one person;
analyze the measurements/readings, the stress-related episode(s) and behavior(s) of the at least one person, and (a) the context(s) associated with the stress-related episode(s) and behavior(s) of the at least one person or (b) the location(s) and the context(s) associated with the stress-related episode(s) and behavior(s) of the at least one person, to thereby determine a risk(s) of an stress-related episode(s) and behavior(s) of the at least one person relative to a trigger threshold(s); and
facilitate one or more actions to lower the risk(s) of the stress-related episode(s) and behavior(s) of the at least one person associated with a trigger(s) from reaching or exceeding the trigger threshold(s) associated with the trigger-related episode(s) and behavior(s) of the at least one person before the trigger-related episode(s) and behavior(s) of the at least one person occurs.
34 . The system of claim 1 , wherein the system is configured to automatically select the type, level, and amount of the reward and/or the disincentive responsive to how, where, by how much, and when the predicted risk or trending risk changes for the associated context as determined from sensor-detected trigger(s) and contextual features.
35 . The system of claim 1 , wherein the context(s) associated with trigger(s) of the at least one entity include four or more 5W1H (who, what, when, where, why, how) attributes.
36 . The system of claim 1 , wherein the context(s) associated with trigger(s) of the at least one entity include four or more of:
where is the location(s) of the at least one entity;
why the at least one entity is at the location(s);
who or what is virtually and/or actually with the at least one entity at the location(s) or nearby the location(s) within audible, visual, and/or electronic detection range of the devices, sensors, and/or communication network(s);
what the at least one entity is doing at the location(s);
when the at least one entity is at the location(s);
how the at least one entity arrived at the location(s) and/or how will the at least one entity leave the location(s); and
an environmental condition at the location(s).
37 . The system of claim 1 , wherein the system is configured to:
monitor effectiveness of the sensors and algorithmic use of the sensor data;
learn from the monitored effectiveness of the sensors and algorithmic use of the sensor data; and
based at least in part on the learning, modify which and how sensors are used such as by changing variables, variable weight, and variable combinations/heuristics.
38 . The system of claim 1 , wherein the system is configured to:
monitor effectiveness of the rewards and/or disincentives;
learn from the monitored effectiveness of the rewards and/or disincentives; and
based at least in part on the learning, dynamically and adaptively modify types, levels, and amounts of the reward and/or the disincentive to help increase effectiveness of the reward in incentivizing behavior for the associated context of the at least one entity and/or the disincentive in disincentivizing behavior for the associated context of the at least one entity.
39 . The system of claim 1 , wherein the system is configured to:
segment a schedule into a plurality of steps, parts, and/or sub-processes; and
dynamically and adaptively determine a reward and/or disincentive each time the at least one entity completes each corresponding part, step, and/or sub-process of the plurality of parts, steps, and/or sub-processes of the schedule.
40 . The system of claim 39 , wherein the system is configured to:
monitor effectiveness of the rewards and/or disincentives for the at least one entity's completion of the corresponding parts, steps, and/or sub-processes of the plurality of parts, steps, and/or sub-processes of the schedule;
learn from the monitored effectiveness of the rewards and/or disincentives; and
based at least in part on the learning, dynamically and adaptively modify types, levels, and amounts of the reward and/or the disincentive to help increase effectiveness of the reward in incentivizing behavior of the at least one entity while completing parts, steps, and/or sub-processes of the schedule and/or the disincentive in disincentivizing behavior of the at least one entity while completing parts, steps, and/or sub-processes of the schedule.
41 . The system of claim 1 , wherein the system is configured to determine a reward for the at least one entity withstanding a contextual risk that increases a risk or likelihood that at least one trigger for the at least one entity becomes active, in danger of becoming active, or imminent.
42 . The system of claim 1 , wherein the system is configured to determine a reward for the at least one entity withstanding a contextual risk that increases a risk or likelihood of activation of at least one trigger for the at least one entity.
43 . The system of claim 1 , wherein the system is configured to determine a reward for the at least one entity withstanding a contextual risk that increases a risk or likelihood of activation of at least one trigger indicative of a future occurrence(s) of the behavior(s) by the at least one entity.
44 . The system of claim 1 , wherein the system is configured to:
determine whether a contextual risk of at least one trigger for the at least one entity is active, in danger of becoming active, or imminent, as determined through the plurality of different devices, sensors, sensor arrays, and/or communications networks and/or inferred through information from system inputs; and
determine a reward for the at least one entity withstanding the contextual risk that would otherwise increase a risk or likelihood of activation of the at least one trigger.
45 . The system of claim 1 , wherein the system is configured to:
determine whether a contextual risk of a trigger and at least one related trigger for the at least one entity is active, in danger of becoming active, or imminent, as determined through a plurality of measurements/readings taken by the plurality of different devices, sensors, sensor arrays, and/or communications networks and/or inferred through information from system inputs, the trigger and the at least one related trigger including two or more of an Anger trigger, an Anxiety trigger, a Boredom trigger, a Depression trigger, a Fear trigger, a Frustration trigger, a Job trigger, a Personal Relationship trigger, a Stress trigger, a Despair trigger, a Self-Loathing trigger, a Resentment trigger, an Information Overload/Snowball Effect trigger, and/or a Yelling trigger; and
determine a reward for the at least one entity withstanding the contextual risk that would otherwise increase a risk or likelihood of activation of the trigger and the at least one related trigger.
46 . A 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 and/or inferred through information from system inputs, behavior(s) of at least one entity and context(s) associated with the behavior(s) of the at least one entity, wherein the system is configured to be operable such that:
the behavior and context are part of a production and/or delivery process;
the production and/or delivery process is explicitly segmented into a plurality of steps, parts, and/or sub-processes;
the at least one entity's participation and/or performance in at least one of the plurality of steps, parts, and/or sub-processes, and the behavior and associated context(s) of said at least one of the plurality of steps, parts, and/or sub-processes is captured as at least one or more transactions in a system of record comprising a ledger, a distributed ledger, and/or a blockchain;
at least one of the one or more transactions, depending on the at least one entity's participation and/or performance in said at least one of the plurality of steps, parts, and/or sub-processes, is assigned, awarded, and/or allocated an electronic or physical store of value;
the electronic or physical store of value is assigned, awarded, and/or allocated based on the at least one entity's behavior and associated contextual participation and/or performance in said at least one of the plurality of steps, parts, and/or sub-processes, where such participation and/or performance is measured against one or more participation and/or performance targets, once said at least one entity's participation and/or performance has been adjusted to incorporate contextual situations, issues, and/or other factors resulting in a context outside expected parameters that may have impacted the at least one entity's participation and/or performance;
monitoring, detection, and/or measurement of participation and/or performance of the at least one entity's behavior and associated context(s) in said at least one of the plurality of steps, parts, and/or sub-processes is performed using the system comprising the plurality of different devices, sensors, sensor arrays, and/or communications networks; and
the individual and/or accumulated store of value(s) associated with the at least one entity can subsequently be redeemed by system of record entity(s), either alone or in combination with other stores of value assigned, awarded, and/or allocated in association of the production and/or delivery process; and
wherein the system is configured to monitor post assignment, reward, and/or allocation outcomes and to modify profiles, thresholds, and device settings responsive to measured effectiveness.
47 . The system of claim 46 , wherein the system of record is a ledger, distributed ledger or blockchain system that records, for each step, part, and/or sub-process, (i) the participation and/or performance; (ii) the contextual adjustments; and (iii) the assignment or adjustment of a store of value; and exposes the recorded data for redemption, transfer, and/or combination under smart-contract control.
48 . The system of claim 46 , wherein the electronic or physical store of value comprises one or more of money, a ticket, and cryptocurrency.
49 . The system of claim 46 , wherein the electronic or physical store of value is transferrable from one entity to another entity and combinable across the plurality of steps, parts, and/or sub-processes, and across one or more smart contracts.
50 . The system of claim 46 , wherein the production and/or delivery process is part of or associated with a smart contract.
51 . The system of claim 46 , wherein the electronic or physical store of value is redeemable for cybercurrency and/or transferrable to an electronic wallet under smart-contract control.
52 . The system of claim 46 , wherein the at least one entity's participation and/or performance is measured against one or more participation and/or performance targets for the production and/or delivery process as a whole or for a combination of less than all of the steps, parts, and/or sub-processes of the production and/or delivery process.
53 . The system of claim 46 , wherein the electronic or physical store of value is combinable with a process or across one or more additional processes, with additional users, within a smart contract, or across one or more additional smart contracts, and wherein the combining is triggered by satisfaction of context-specific conditions recorded in the ledger, distributed ledger, and/or blockchain.
54 . The system of claim 46 , wherein:
the at least one entity comprises one or more of a robot, an artificial intelligence, an animal, a virtual agent, a corporation, a business entity, a nation, a network, a driverless vehicle, a connected vehicle, a drone, and/or a governmental entity; or
the at least one entity comprises a human and/or a machine.