APPARATUS FOR DEPLOYING TINY EDGE DEVICES AND RELATED SYSTEMS AND

METHODS

CROSS-REFERENCE TO RELATED APPLICATION

[0001] The present application claims priority to United States Provisional Patent Application No. 62/692,133 filed on June 29, 2018 and titled“Apparatus For Deploying Tiny Edge Devices And Related Systems And Methods”, the entire contents of which are herein incorporated by reference.

TECHNICAL FIELD

[0002] The following generally relates to tiny edge devices, and apparatuses, systems and methods for deploying the tiny edge devices.

DESCRIPTION OF THE RELATED ART

[0003] Tiny computing devices are starting to become smaller and smaller. These tiny computing devices have a processor, memory, a power source, and a communication module. In some examples, these tiny computing devices also have sensors. In some instances, the tiny computing devices have dimensions of a few centimeters; in some instances the tiny computing devices have dimensions of a few millimeters; in some instances the tiny computing devices have dimensions of about a millimeter; in some instances the tiny computing devices have dimensions of less than a millimeter; in some instances the tiny computing devices have dimensions of about 0.1 millimeters; and in some instances the tiny computing devices have dimensions of about 10 micrometers.

[0004] These tiny computing devices are sometimes referred to as motes. These tiny computing devices are also considered, in some example embodiments, part of the Internet of Things (loT). These tiny computing devices are herein called tiny edge devices (TEDs).

BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Embodiments will now be described by way of example only with reference to the appended drawings wherein:

[0006] FIG. 1 is a schematic diagram showing an example embodiment of a system of apparatuses for deploying TEDs.

[0007] FIG. 2 is a schematic diagram showing an example embodiment of a system of devices, including an apparatus to obtain feedback data about TEDs. [0008] FIG. 3 is a schematic diagram showing another example embodiment of a system of devices, including an apparatus to obtain feedback data about TEDs.

[0009] FIG. 4 is a schematic diagram showing another example embodiment of a system of devices, including a holding structure to obtain feedback data about TEDs.

[0010] FIG. 5 is a schematic diagram of a vehicle that includes a TED dispenser apparatus, according to an example embodiment.

[0011] FIG. 6 is a schematic diagram of a vehicle that includes multiple TED dispenser apparatuses, according to an example embodiment.

[0012] FIG. 7 is a cross-sectional view of a ruggedized TED, according to an example embodiment.

[0013] FIG. 8 is a cross-sectional view of the ruggedized TED in a thing or a material, according to an example embodiment.

[0014] FIG. 9 is a cross-sectional view of a pill that includes a ruggedized TED, according to an example embodiment.

[0015] FIGs. 10 and 1 1 are schematic diagrams of different example embodiments of containers that hold pills, and the pills include TEDs.

[0016] FIG. 12 is a schematic diagram of a manufacturing system for forming pills with TEDs, according to an example embodiment.

[0017] FIG. 13 is a schematic diagram of a manufacturing device with a build chamber for manufacturing an object that includes one or more TEDs, and a computing element that wirelessly communicates with the one or more TEDs.

[0018] FIG. 14 is a schematic diagram of a 3D printer for fused deposition modelling (FDM) that incorporates one or more TEDs into the manufactured part, according to an example embodiment.

[0019] FIG. 15 is a schematic diagram of a stereolithography 3D printer that incorporates one or more TEDs into the manufactured part, according to an example embodiment.

[0020] FIG. 16 is a schematic diagram of a selective laser sintering 3D printer that incorporates one or more TEDs into the manufactured part, according to an example embodiment. [0021] FIG. 17 is a schematic diagram of a selective laser sintering 3D printer and a TED dispenser head that incorporate one or more TEDs into the manufactured part, according to an example embodiment.

[0022] FIG. 18 is a schematic diagram of a hopper and an injection mold machine that incorporate one or more TEDs into the manufactured part, according to an example embodiment.

[0023] FIG. 19 is a schematic diagram of an injection mold machine and a TED dispenser that incorporate one or more TEDs into the manufactured part, according to an example embodiment.

[0024] FIG. 20 is a schematic diagram of a cement mixer equipped with a TED dispenser, according to an example embodiment.

[0025] FIG. 21 is a schematic diagram of a container or package embedded with a first TED and an object in the container or package that has a second TED, according to example embodiment.

[0026] FIG. 22 shows two parts that have their own TEDs, according to an example embodiment.

[0027] FIG. 23 shows an automated car manufacturing line with robotic arms, including a robotic arm that dispenses TEDs onto a car part, according to an example embodiment.

[0028] FIG. 24 shows TEDs embedded or affixed onto different parts of a vehicle, according to an example embodiment.

[0029] FIG. 25 is a block diagram showing example components of a TED.

[0030] FIG. 26 is a block diagram showing example components of a computing element on a dispenser, or on a holding structure, or some other TED-interactive device.

[0031] FIG. 27 is system diagram of TEDs in data communication with a remote computer system and other computing devices.

[0032] FIG. 28a is a flow diagram of example computer or processer executable instructions for checking for anomalies amongst TEDs, according to an example embodiment.

[0033] FIG. 28b is a flow diagram of example computer or processer executable instructions for checking for anomalies amongst TEDs, according to another example embodiment. [0034] FIG. 29 is a schematic diagram showing the movement of TEDs between different locations, according to an example embodiment.

[0035] FIG. 30A is a flow diagram of computer or processor executable instructions for propagating updates amongst TEDs, according to an example embodiment.

[0036] FIG. 30B is a flow diagram of computer or processor executable instructions for propagating updates amongst TEDs, according to another example embodiment.

[0037] FIG. 31 is a flow diagram of computer of processor executable instructions to provision a new TED.

[0038] FIG. 32 is a schematic diagram of TEDs interacting with different devices according to an example computing architecture.

[0039] FIG. 33 is a schematic diagram of generator TEDs and discriminator TEDs that form a generative adversarial network (GAN), according to an example embodiment.

DETAILED DESCRIPTION

[0040] It will be appreciated that for simplicity and clarity of illustration, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the example embodiments described herein. However, it will be understood by those of ordinary skill in the art that the example embodiments described herein may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the example embodiments described herein. Also, the description is not to be considered as limiting the scope of the example embodiments described herein.

[0041] A tiny edge device (TED) dispenser (e.g. an apparatus) is herein provided that dispenses or deploys TEDs. In an example embodiment, the TED dispenser deploys the TEDs like particles or granules. Systems and methods are also provided that coordinate and control the characteristics of the TEDs and how and when the TEDs are deployed.

[0042] A TED herein refers to very small computers with communication capabilities. For example, a TED is a type of loT device. A TED is also sometimes called a mote or smart dust. In an example embodiment, a TED is sized similar to a coin (e.g. a few centimeters). In an example embodiment, a TED is sized at approximately 1 mm x 1 mm. In another example embodiment, a TED is sized at less than 0.5mm x 0.5mm. In an example embodiment, a TED is micro-sized. In another example embodiment, a TED is nano-sized. It will be appreciated that various dimensions of TEDs are applicable to the principles described herein. Smaller TEDs or larger TEDs may be desired to suit the application in which the TEDs operate.

[0043] Turning to FIG. 1 , a system is provided for remotely controlling the deployment of TEDs in a dynamic manner. In particular, it is herein recognized that it is desirable to change the computational functionality (e.g. code, microcode, data, etc.) that resides on a TED to suit different purposes. It is also herein recognized that it is desirable to change how the TEDs are being deployed based on different factors and conditions.

[0044] In FIG. 1 , three processes (e.g. Process A, Process B, Process C) respectively include three TED dispensers 105a, 105b, 105c. These dispensers and their respective processes could be at different geographical locations, or they could be in the same location. In an example embodiment, Process A, Process B and Process C are different parts of a larger process. In another example embodiment, Process A, Process B and Process C are disparate and unrelated processes.

[0045] It will be appreciated that the elements in at least FIG. 1 are referenced by reference numerals that include suffixes‘a’,‘b’,‘c’, etc. to refer to different instances of the same elements.

[0046] In Process A, a first dispenser 105a includes a container 106a that stores the TEDs 107a. The first dispenser 105a also includes a computing element 109a and a dispensing mechanism 108a.

[0047] In Process B, a second dispenser 105b includes similar or the same

components: a container 106b that stores the TEDs 107b; a computing element 109b; and a dispensing mechanism 108b. In Process C, a second dispenser 105c includes similar or the same components: a container 106c that stores the TEDs 107c; a computing element 109c; and a dispensing mechanism 108c.

[0048] A remote computing system 101 is in data communication with the respective computing elements of each dispenser. For example, the remote computing system 101 includes one or more server machines. In an example embodiment, the remote computing system is a cloud computing system.

[0049] One or more client computing devices 103, 104 can transmit data or commands, or both, to the remote computing system 101 (e.g. via one or more application programming interfaces (APIs)). The remote computing system 101 then uses the data or commands, or both, from the client computing devices 103, 104 to communicate with one or more of the computing elements 109a, 109b, 109c. Based on the communication received at the one or more computing elements 109a, 109b, 109c, these one or more computing elements control operations of their respective dispensers 105a, 105b, 105c. For example, operations of the dispensers include, and are not limited to, one or more of the following: affecting the code or data, or both, on the TEDs; controlling the deployment of the TEDs; communicating with other devices; reading data from the TEDs; providing power to the TEDs; providing data to the remote computing system 101 ; and acting as an intermediary edge node to perform one or more of processing, data storage, and data communication.

[0050] For example, with respect to the first dispenser 105a, the computing element 109a executes computing operations that include, and are not limited to, one or more of the following: creating/reading/uploading/deleting/combinations thereof code or data, or both, on the TEDs 107a within the container 106a; controlling the dispenser mechanism 108a regarding when and how the TEDs 107a are to be deployed; communicating with other devices; reading data from the TEDs that have already been dispensed; providing power to the TEDs; providing data to the remote computing system 101 ; and acting as an

intermediary edge node to perform one or more of processing, data storage, and data communication.

[0051] In another example aspect, the computing element 109a wirelessly flashes the TEDs to affect the data or code, or both, on the TEDs. In this way, the computing element 109a can affect the data on many TEDs stored within the container 106a at the same time.

In an example aspect, one or multiple internal communication modules or devices (e.g. radio devices) are attached or integrated into the container 106a, and these internal

communication devices wirelessly communicate with the TEDs in the container to update their data or code. This allows for batches of TEDs to be updated at the same time. These internal communication devices are part of the computing element 109a or are controlled by the computing element 109a.

[0052] In an example aspect, the computing element 109a uses data science, such as machine learning or artificial intelligence software, to control the deployment of the TEDs. In another example aspect, the computing element 109a communicates with the remote computing system 101 to provide feedback on storage (e.g. such as the number of TEDs within the container) and status information about the container (e.g. power level, operating environment conditions like temperature and humidity, location, operations of the container, security breach information, information regarding the addition or removal of TEDs into the container, maintenance information, and deployment operations of the TEDs). In another example aspect, the computing element 109a tracks and can provide reporting on or more of: which TEDs have been deployed (e.g. as identified by different TED IDs); time stamps of the deployment of each TED; environmental conditions under which the TEDs were deployed; and the manner that each TED was deployed (e.g. direction of deployment, mechanism used for deployment, speed of deployment, location of deployment, whether it was deployed in isolation, whether it was deployed in series with other TEDs, whether it was deployed as a batch with other TEDs, etc.).

[0053] In another example aspect, the TEDs include mechanisms to wirelessly receive power and the container 106a includes mechanisms to wirelessly transmit power to the TEDs stored therein. For example, the TEDs include radio antenna or radio receives that receive electrical energy via radio waves that are emitted one or more radio wave emitters in the container. For example, the internal space of the container is“bathed” in radio waves to transmit electrical power to the TEDs. In another example embodiment, the TEDs can receive power through thermal energy, and the container changes the thermal energy conditions within the internal space to supply thermal throughout the container to all the TEDs. In another example embodiment, the TEDs include electrically conductive contact points and the container includes electrically conductive contact points to transfer power to the TEDs. In another example embodiment, the TEDs receive power by light waves, and the container includes light sources that bathe the internal space with light to provide power to the TEDs. In another example embodiment, the TEDs receive power by mechanical energy (e.g. vibration), and the container includes mechanical agitators to provide mechanical energy to the TEDs. It will be appreciated that other currently known and future known technologies for transferring power, and converting the same to electrical energy are applicable to the principles herein. More generally, the container 106a includes one or more devices that transfer power to the TEDs, so that the TEDs can renew their electrical power store (e.g. a battery).

[0054] In an example aspect, the storage container 106a is a physically secured and protected (e.g. from environmental conditions) so that the TEDs within the container are safe. In other words, a bad actor is deterred from damaging or stealing the TEDs. For example, the storage container 106a includes a mechanism that controllably seals an aperture that allows for adding TEDs into the container. The mechanism can only be opened by specialized tools, or requires security authentication or both. In another example aspect, the container is designed so that the only exit of TEDs from the container is through the dispenser mechanism 108a, which is controlled by the computing element 109a. In another example aspect, the body of the container is permanently sealed and no additional TEDs can be added into the container after it has been sealed. [0055] In another example aspect, and only the body of the storage container 106a includes a shielding device that acts a faraday cage, or more generally includes radio signal shielding, to prevent unwanted external radio signals from wirelessly flashing the TEDs 107a within the container 106a. This shielding lines the walls of the container. The shielding can be solid or can be like a mesh. In this way, only the computing element 109a and its internal communication device(s) can wirelessly flash the TEDs within the container 106a.

[0056] In an example aspect, the dispenser mechanism 108a includes an internal communication device, which is connected to and controlled by the computing element 109a. In operation, the dispenser mechanism 108a uses this internal communication device to wirelessly flash one or more TEDs as each of these TEDs are being dispensed.

[0057] In other words, the TEDs can be wirelessly flashed altogether in the container, or the one or more TEDs can be wirelessly flashed as they are being dispensed by the dispensing mechanism, or both.

[0058] The dispensing mechanism 108a can dispense one TED at a time, or can dispense multiple TEDs at the same time. In an example aspect, the dispensing mechanism uses a gravity fed mechanism to deploy the TEDs. In another example aspect, the dispensing mechanism ejects one or more TEDs by force. In another example aspect, the dispensing mechanism ejects one or more TEDs using one or more of: pneumatic/hydraulic power; fluidic flow; electromagnetism; mechanical force; magnetic force; and explosive force (e.g. from an explosion). In an example aspect, the dispensing mechanism can deploy or shoot the one or more TEDs in a certain direction, or at a certain speed, or both.

[0059] The functionality of the components of the first dispenser 105a apply to the other dispensers described herein.

[0060] In Process A, one or more of the TEDs that have been dispensed from the first dispenser 105a wirelessly communicate with one or more intermediary edge nodes 1 15, and these one or more intermediary edge nodes 1 15 transmit data back and forth with the remote computing system 101 . In other words, data sensed by a TED can be transmitted to the remote computing system 101 via one or more intermediary edge nodes.

[0061] In Process B, one more TEDs that have been dispensed by the dispenser 105b wirelessly communicate with the computing element 109b. In turn, data that is sensed by these TEDs is transmitted to the computing element 109b, and to the remote computing system 101. [0062] As shown in Process C, one or more TEDs that have been dispensed by the dispenser 105c can directly transmit data to a given client device (e.g. client device 104), or indirectly via one or more third party devices 1 16, which could be other edge nodes.

[0063] The TEDs themselves also form a communication network, or a chain of communication, that allows for the data to be transmitted amongst each other.

[0064] The system shown in FIG. 1 allows client devices 103, 104 to update or control: how and when the TEDs are dispensed; what intelligence/computations are being executed by the TEDs; uploading or flashing the TEDs with security data and ID data; and determine the status of the dispensers (e.g. location, operational information, number of TEDs stored in a container, the type(s) of TEDs stored in the container, security breach information, power levels of the TEDs, etc.). For example, numerous client devices interact with the remote computing system 101 via APIs 102, and the remote computing system 101 in turn controls the dispensers and provides data back to the client devices.

[0065] In an example embodiment, a first client device 103 exclusively controls the first dispenser 105a; a second client device 104 exclusively controls the third dispenser 105c; and the second dispenser 105b is shared amongst different client devices.

[0066] In other words, some dispensers and the TEDs stored therein are controlled by one party through their respective client device. This allows them to privately control what data and code is uploaded onto the TEDs, to privately control the deployment of the TEDs and to privately control the flow of data with the TEDs.

[0067] In another example, such as with the second dispenser 105b, multiple parties can control the dispensing of TEDs for their own purposes. As the second dispenser allows for TEDs to be re-flashed with new code and new data, the second dispenser and the TEDs stored therein become a shared utility that is also customizable to different clients. For example, the first client device 103 remotely causes the computing element 109b to wirelessly flash the TEDs 107b with first code and data, and to deploy X number of TEDs according to certain conditions (e.g. time of release, frequency of release, etc.). At a later time, the second client device 104 remotely causes the computing element 109b to wirelessly flash the remaining TEDs 107b in the container with second code and data, and to deploy Y number of TEDs according to certain conditions (e.g. time of release, frequency of release, etc.).

[0068] These motes could be released into food processes, pharmaceuticals, manufacturing of metal, manufacturing of materials (textiles, carbon fibre, plastics, fuel, soil, fertilizers, seeds, etc.), water supplies, waste management processes, mail, clothing, etc. [0069] FIG. 2 shows an example process flow 201 , such as a process that includes the flow of material (e.g. objects, goods, liquids, gases, particles, devices, mixtures, etc.). The process flow could be a pipe, a biological vein or artery, a conveyor system, a track, a road, a tube, etc. A dispenser 105 deploys TEDs 107 in the flow 201 . Downstream from the dispenser is a downstream device 202 that interacts with the TEDs.

[0070] The downstream device 202 includes a body 203 that includes components to at least one of: obtain data or code (or both) from the TEDs as they flow by the downstream device 202; transmit data or code (or both) to the TEDs as they flow by the downstream device 202; transfer power to the TEDs as they flow by the downstream device 202; and destroy one or more functions of the TEDs as they flow by the downstream device 202. The downstream device is configured, for example, for the flow 201 to at least one of: pass over, pass through, pass under, or pass beside the downstream device. In the example shown in FIG. 2, there is an internal passage way 204 in the downstream device 202 that facilitates the flow 201 and the TEDs to pass therethrough.

[0071] In an example operation, the TEDs are dispensed from the dispenser into the flow 201 . The TEDs include sensors that measure data, create data, and receive data, etc. while moving with the flow 201 , and this data is locally stored in the TEDs. As the TEDs pass through (or otherwise near enough) to the downstream device 202, the downstream device obtains this data from the TEDs. This data is fed back from the computing element 205 of the downstream device 202, to the computing element 109 of the dispenser 105. The computing element 109 then uses this data to, for example, adjust the data or code on the TEDs within the container of the dispenser, or adjust the how the TEDs are being dispensed (e.g. frequency of dispensing, number being dispensed, time of dispensing, etc.), or both.

[0072] This feedback data can also be transmitted to other devices 210 that are upstream or downstream from the flow 201 . For example, the other device 210 takes an action that includes one or more of the following: change the material or objects in the flow 201 ; change the manufacturing or assembly of the material or objects in the flow 201 ;

change upstream sensor parameters and functionality in the flow 201 ; and change data science being used to control the flow 201 . These changes could also be made to downstream devices or processes, or both.

[0073] This system is applicable to different embodiments, such as manufacturing, medicine, construction, packaging, delivery, etc.

[0074] In an example embodiment, the dispenser 105 is incorporated into a drug delivery system that includes the other device 201 to control the flow of chemicals or drugs into a person’s body (e.g. their blood stream, their abdomen, etc.). For example, chemotherapy introduces chemicals into the bloodstream. In another example, peritoneal dialysis introduces a fluid into a person’s abdomen. The dispenser 105 dispenses TEDs into the flow of fluid into the person’s body, and the TEDs capture data about the person’s body. For example, the data collected from the TEDs is used to determine the physical condition of the person’s body and to determine the effectiveness or progress of the medical treatment. Based on the feedback data collected by the TEDs using the downstream device 202, the the other device 210 is used to change the flow of chemical or fluid into the person’s body. For example, based on the feedback data, the other device 210 increases the flow of fluid into the person’s body.

[0075] FIG. 3 shows a similar example to FIG. 2, as it includes a process flow 301 and a downstream device 302 that is downstream from a dispenser 105. The downstream device 302 is similar to the downstream device 202. However, the downstream device 302 includes a serpentine internal structure 304 that increases the time duration that a TED is in proximity with the body 303 of the device 302. This allows the computing element 305 and its related internal communication devices to have more time to flash or communicate with the TEDs, or re-power the TEDs, etc. In other examples, the structure could be a circular structure, a helical structure, etc. or another structure that lengthens the proximity time of a TED as it passes through or in proximity to the downstream device 302. In another example, there is a mechanism or device that slows down the flow 301 as the flow passes through or in proximity to the downstream device 302.

[0076] FIG. 4 shows another example, in which the process includes a first process P1 that has material, goods, objects, etc. and TEDs (which have been dispensed from the dispenser 105) get stored in a holding structure 401 . This downstream holding structure 401 , for example, is part of a distribution process, or a manufacturing process. For example, the holding structure is a holding box, or a holding cell, or a holding bag, or a holding shelf, or a holding tank. This holding structure could, optionally, be part of a transport vehicle or carried by a transport vehicle. After the material, goods, objects, etc. are held in the holding structure 401 , they are then released or deployed, along with the TEDs 107 as part of the process P2.

[0077] The holding structure 401 also includes a computing element 403 that is able to perform or control at least one of: obtain data or code (or both) from the TEDs as they are held in the structure 401 ; transmit data or code (or both) to the TEDs while they are held; transfer power to the TEDs as they are held; and destroy one or more functions of the TEDs as they are held. [0078] Data from the TEDs that are being held in the structure 401 is transmitted to the computing element 403, and the computing element in turn can transmit this feedback data to the dispenser 103 and to other devices 210. These devices 103 and 210 can then use this data to adjust their functions/actions.

[0079] In an example aspect, the body of the holding structure 401 includes radio signal shielding to prevent the TEDs from being wirelessly flashed by bad actors.

[0080] FIG. 5 shows that a vehicle 501 carries a dispenser 105 to deploy the TEDs at different locations. For example, the vehicle is an aircraft that deploys the TEDs in the air, on to the ground, on top of vegetation, on the water, on a surface, etc. The aircraft, for example, is an airplane, lighter than air aircraft (e.g. balloon), a helicopter, a missile, etc. In another example, the vehicle is a drone. In another example, the vehicle is a ground vehicle. In another example, the vehicle is a marine vehicle or an underwater vehicle. In another example, the vehicle is a legged robot. In another example, the vehicle is a drilling device and the TEDs are deployed underground. In another example, the vehicle is a satellite and the TEDs are deployed in space. In another example, the vehicle is spacecraft and the TEDs are deployed on a surface of a moon, an asteroid, a planet, etc. The vehicle can be manned or unmanned.

[0081] FIG. 6 shows that a vehicle 601 includes a first dispenser 105a and a second dispenser 105b. For example, these dispensers contain and deploy different types of TEDs. Mixtures or combinations of different TEDs can be deployed, or different TEDs are deployed under different conditions (e.g. time, location, etc.).

[0082] FIG. 7 shows that a ruggedized TED 701 that includes the hardware

components 703 surrounding or encapsulated with a coating or shell 703. The coating or shell physically protects the TED so that it can be used in relatively inhospitable

environments (e.g. in liquids, in heat, in cold, under pressure, under impact, with abrasive materials, etc.). The ruggedized TED 701 is, for example mixed with or placed in another thing or material 801 as shown in FIG. 8. This thing or material 801 could be a solid, liquid, fluid, semisolid, a mixture, particles, etc.

[0083] In an example embodiment shown in FIG. 9, the ruggedized TED 701 is placed in a pill 901 and is surrounded by the pill material 902. The pill can be a capsule-type pill or a tablet-type pill. A dispenser dispenses one or more TEDs into each pill. While the pill material 902 is digested by a person or an animal, the ruggedized TED 701 will be passed through the person or the animal. [0084] FIG. 10 shows a container 1001 for holding pills 901 with TEDs within the pills. The container body 1002 of the container includes radio signal shielding to prevent unwanted flashing or communicating with the TEDs from bad actors.

[0085] A computing element 1003 is incorporated into the container 1001 , and it has the same or similar abilities as the computing element 403. The computing element 1003 also senses the environment within or external to the container, or both. For example, the computing element 1003 includes sensors to sense if the pills have been compromised due to environmental conditions (e.g. too hot, too cold, too humid, too dry, etc.), or if the pills are too old (e.g. as identified by a production date stored in the TEDs), or both. The computing element 1003 then transmits a message to a pharmacist, a drug company, a user, etc. to take action to destroy the pills if necessary.

[0086] In another example, the computing element 1003 exchanges data with the TEDs to count the number of pills stored in the container body. For example, if there is one TED embedded in each pill, then the computing element communicates with all the TEDs in the container body to count the number of TEDs. In turn, the computing element determines the number of pills. This information, along with a time stamp, can be transmitted by the computing element to the remote computing system 101. In this way, the number of pills within a container at a known time can be tracked. For example, this allows pill consumption to be tracked as a time series (e.g. 2 pills were removed from the container on Monday; 2 pills were removed from the container on Tuesday; 1 pill was removed from the container on Wednesday; etc.).

[0087] In another example embodiment, the computing element 1003 includes a location positioning system that identifies the location of the container. The computing element also transmits this information along with a time stamp to the remote computing system. In another example, the location of the container is determined by the Internet Protocol (IP) address that the computing element uses to communicate over the Internet.

[0088] FIG. 1 1 shows a similar embodiment as in FIG. 10. The container 1001 in FIG.

1 1 includes a container body 1002, a barrier 1 103 that opens and closes an opening 1 104 in the container body, a computing element 1003, and an action device 1 102. For example, the action device 1 102 controls the locking state of the barrier (e.g. locks or unlocks the barrier, or both) to control access to the pills. The computing element wirelessly communicates with the TEDs, and the computing element controls the action device 1 102 to at least one of unlock and lock the barrier. [0089] In an example aspect, the barrier 1 103 is a lid that can be locked or unlocked using a latching mechanism incorporated into the action device 1 102. In another example embodiment, the barrier 1 103 is a door or flap that is locked and unlocked by the action device.

[0090] In another example aspect, the action device 1 102 is configured to destroy the pills within the container. For example, the action device 1102 performs a pill destruction process that uses chemicals to destroy the pills; uses heat to destroy the pills; uses light to destroy the pills; or, a combination thereof. It will be appreciated that one or more different mechanisms and processes can be used to destroy the pills.

[0091] In another example embodiment, the TEDs also store a reactive chemical in a manner that allows the reactive chemical to be released. When the reactive chemical is stored in the TED, the reactive chemical does not react with the pill’s ingredients. When the TED releases the reactive chemical, the reactive chemical reacts with one or more ingredients of the pill to destroy those ingredients. The TED releases the reactive chemical in response to a signal from the computing element 1003 In this way, the computing element 1003 can trigger the destruction of the pills. In a further example aspect, the remote computing system 101 or a client device 103,104 (or both) communicate with the computing element 1004 to trigger the description of the pills.

[0092] In another example embodiment, the container 1001 the action device 1 102 locks and unlocks the container. The remote computing system 101 or a client device 103 104 or both, exchange data with the computing element 1003 to control the locking and unlocking of the container. For example, the remote computing system 101 and the computing element 1003 execute a verification process and an authorization process that confirms that the remote computing system authorizes the computing element 1003 to command the action device 1102 to unlock the container 1001 . In a further example aspect, the remote computing system 101 triggers these verification and authorization computations in response to receiving a request to unlock or lock the specific container from a client device 103 or a client device 104

[0093] For example, a client device 103 is a computer device of a pharmacist that wishes to unlock the container of pills. The client device 104 is a computer device (e.g. a mobile device) of a person to whom the pills are prescribed. The pharmacist uses their client device 103 send a request to the remote computing system 101 to unlock the specific container 1001 that contains the identified pills, whereby the pills can be identified by the computing element 1003 interacting with the TEDs in the pills. The pharmacist can also use their client device 103 to enable authorization of the person’s client device 104 to also send a request to the remote computing system 101 to unlock the container 1001 . For example, the pharmacist’s client device 103 sends over identification information (e.g. personal identification information, mobile device identification information, proxy identification information, etc.) that allows the client device 104 to interact with the remote computing system 101. The remote computing system 101 receives the request from the client device 103 or the client device 104 and then the remote computing system 101 initiates the process with the computing element 1003 to unlock the container 1001 . This helps to increase the safety of consuming pills, so that only verified people (e.g. the pharmacist, the person with a prescription, a doctor, etc.) are able to unlock the container 1001 or to lock the container 1001 .

[0094] In another example, the remote computing system 101 receives a request from the pharmacist client device 103 to destroy the pills in the specific container 1001 , and the remote computing system 101 then commands the computing element 1003 to trigger the action device 1 102 to initiate a pill destruction process. This also improves drug safety by preventing unauthorized drug usage.

[0095] FIG. 12 shows an example tablet manufacturing system. A dispenser 1201 is positioned upstream from the compression rolls. The dispenser 1201 dispenses one or more ruggedized TED into each tablet die as they pass below the dispenser. The pill material and the TED are then compressed into a tablet and ejected.

[0096] FIG. 13 shows a manufacturing device 1301 that includes a build chamber 1303 and building tools 1302. An object 1304 is built in the build chamber 1303 by the tools 1302. TEDs 107 are embedded into the object 1304 as part of the build process. The computing element 1305, which has the functionality as other computing elements described herein, can communicate with and wirelessly flash the TEDs within the build chamber 1303.

Examples of a manufacturing device 1301 include: a 3D printer, an injection moulding machine, a vacuum molding machine, a die-press machine, a blow molding machine, a subtractive manufacturing machine, etc.

[0097] FIG. 14 shows a 3D printer for fused deposition modelling (FDM). A spool of wire filament 1404 has embedded within the filament TEDs 701 at different spots along the length of the filament. For example, the TEDs are ruggedized. The spool 1404 is contained within the container body 1403 of the dispenser 1401 . A computing element 1402 is used to flash the TEDs within the container. As the TEDs are in the filament, then the TEDs become embedded into the manufactured part. [0098] FIG. 15 show a stereolithography 3D printer. A dispenser 1501 includes a dispensing head 1502 that moves back and forth over the liquid resin to dispense TEDs 701 , which become incorporated into the printed part. In an example aspect, the dispensing head 1502 also serves to be a recoater blade of the liquid resin.

[0099] FIG. 16 shows a selective laser sintering 3D printer. The TEDs 701 are premixed into the powder. A computing element 1602 is able to communicate with, and to flash the TEDs while they are in the powder delivery system. The TEDs then become embedded into the 3D printed part.

[00100] FIG. 17 shows another example of a selective laser sintering 3D printer. A dispenser 1701 includes a dispensing head 1702 that moves back and forth over the building chamber of the sintered part to specifically deposit TEDs 701 into the part as the powder is being sintered around the TEDs.

[00101] FIG. 18 shows an injection mold machine 1801 that includes a hopper and dispenser system 1802. The hopper and dispenser system 1802 deposits granules 1803 and TEDs 701 into the barrel of injection mold machine to form an injection molded part. For example, the granules 1803 are plastic granules. In an example embodiment, one or more TEDs can collect data during the injection molding process. In another example

embodiment, the one or more TEDs are incorporated into the injection molded part.

[00102] FIG. 19 shows another injection mold machine with a dispenser 1901 that dispenses TEDs 701 downstream from the heater and the screw, and upstream from the mold cavity.

[00103] FIG. 20 shows a cement mixer combined with a dispenser 2001 . The dispenser 2001 dispenses TEDs 701 downstream from the mixing device and into the pouring channels (e.g. the pouring ducts). In this way, the TEDs are incorporated into the cement mix. The TEDs collect data from the cement mix. In an example embodiment, this data is fed back to the cement mixer to vary the cement mixing parameters (e.g. add more water, increase mixing speed, etc.) or the cement pouring parameters (e.g. increase pour volume, change pour location, etc.), or both.

[00104] FIG. 21 shows a package or container 2101 embedded with a first TED 2102 and an object 2103 in the container or package that has its own TED 2104. The TEDs 2102, 2104 can communicate with each other and with other devices. For example, this can be used to confirm contents of the package or the container, or to confirm that the contents and the packaging match each other. [00105] FIG. 22 shows two parts 2202, 2201 that have their own TEDs. This can be used to confirm that different parts are designed to be with each other, or not.

[00106] FIG. 23 shows an automated car manufacturing line with robotic arms. A dispenser 2302 dispenses TEDs through an end effector of a robotic arm 2301 . For example, the TED is adhered, affixed, embedded, etc. onto a car part using the end effector of the robotic arm.

[00107] In FIG. 24, TEDs are embedded or affixed onto different parts of a vehicle. A scanner, not shown, can determine which parts originate from which manufacturing processes or manufacturers, and on which dates the parts are manufactured. This data is stored in the TED.

[00108] FIG. 25 shows example components of a TED 107. It includes a processing module 2501 , a memory module 2502, a communication module 2503, a power module 2504, an actuator module 2505 and a sensor module 2506.

[00109] FIG. 26 shows example components a computing element on a dispenser, or on a holding structure, or some other TED-interactive device. The computing element 2600 includes a sensor module 2601 , a power module 2602, an external communication module 2603 (e.g. to communicate with external devices like the remote computing system 105), an internal communication module 2604 (e.g. to communicate with the TEDs stored in a container, passing through a downstream device, stored in a holding structure, etc.), a processing module 2606, a memory module 2605, a secure element 2607, and power transfer module 2608 (e.g. to transfer power to the TEDs). The computing element 2600 communicates and controls an actuator module 2609 that is part of the TED-interactive device.

[00110] In an example aspect, the secure element is at least one of: a UICC secure element, micro SD card secure element, an embedded secure element, and a virtual secure element (which is cloud based). The secure element helps to track and authenticate data communications to and from the TEDs and to and from other devices.

[00111] The modules described in FIG. 26 can form one physical device, or some of the modules may be physically separated, although interact and function as a system of the computing element.

[00112] FIG. 27 shows that the TEDs can interact with each other in a peer-to-peer manner, or over a network 2700, or both. For example, the TEDs 102a, 102b, 102e from a data communication chain using peer-to-peer communication. Other devices, such as the client devices and the remote computer system and the dispensers communicate through the network 2700.

[00113] Computation and Data Management Examples

[00114] Below are examples of using TEDs to manage computations and data amongst each other. The TEDs are also herein interchangeably called Intelligent Endpoint Systems.

It will be appreciated that other computational methods can be used.

[00115] Turning to FIG. 28A, an example embodiment is shown in which a first Intelligent Endpoint 102a locally detects a certain condition (block 2801 ) and performs a check with n- nearest neighbors to determine if they have detected the same certain condition (block 2802). The certain condition, for example, can be an anomaly or some other known condition. For example, the Intelligent Endpoint System 102a identifies the n-nearest neighbors (or finds any neighboring devices within a given distance, or find devices on a given bandwidth, or finds other devices according to some other condition), and transmits a request to these other devices to check for the certain condition.

[00116] For example, another Intelligent Endpoint System 102b receives this request (block 2803), performs a check to see if the same certain condition is detected locally (block

2804), and transmits the results back to the first Intelligent Endpoint System 102a (block

2805). The Intelligent Endpoint System 102b, for example, also takes action based on the results of performing the check (block 2806). These operations 2803, 2804, 2805, 2806 are also performed by other Intelligent Endpoint Systems in parallel (or in serial), such as the Intelligent Endpoint System 102c.

[00117] The first Intelligent Endpoint System 102a receives the results from the one or more other Intelligent Endpoint Systems (block 2807). The first Intelligent Endpoint System 102a, for example, also takes action based on these received results (block 2808).

[00118] For example, the other Intelligent Endpoint Systems 102b, 102c do not detect the certain condition and continue to locally monitor to see if they are able to detect the certain condition in the future. These Endpoints 102b, 102c also propagate a risk of the certain condition (e.g. an anomaly) to other Intelligent Endpoint Systems (e.g. which could be further removed from the first Intelligent Endpoint System 102a), which in turn initiates these other Intelligent Endpoint Systems to also monitor for the certain condition.

[00119] In another example, the other Intelligent Endpoint Systems 102b, 102c do detect the certain condition. A message is spread through the network of Intelligent Endpoint Systems with respect to the detected certain condition. Actions may be taken by one or more of the Intelligent Endpoint Systems in reaction to detecting the certain condition.

[00120] In an example embodiment, the certain condition is an anomaly. If an anomaly is detected, one or more Intelligent Endpoint Systems, which are in a larger network of Intelligent Endpoint Systems, are hived off or isolated to form a sandbox. In an example aspect, the one or more Intelligent Endpoint Systems that form the sandbox are selected (e.g. self-selected or appointed by other Intelligent Endpoint Systems in the network) based on some condition. For example, the condition is that the selected Intelligent Endpoint Systems are: the ones that detected the anomaly; the n-nearest Intelligent Endpoint Systems that are closest to the Intelligent Endpoint System that detected the anomaly; the Intelligent Endpoint Systems that have certain hardware or certain software (or both) to compute response actions; or a combination thereof. In an example embodiment, one or more TED dispensers flash TEDs with data and algos specific to forming the sandbox, and then deploy the TEDs. After the sandbox of Intelligent Endpoint Systems (aka TEDs) is formed, these sandboxed TEDs compute response actions. For example, the response actions include one or more of: identifying the source or cause of the anomaly; recreating the anomaly; identifying the effects of the anomaly; removing of the anomaly; and amplifying the effects of the anomaly. The desired data, processes, and outcomes obtained from the sandbox are then transmitted to other Intelligent Endpoint Systems in the network. If the Intelligent Endpoint Systems in the sandbox are compromised, damaged, misappropriated, etc. during the computing of the response actions, then these sandboxed Intelligent Endpoint Systems are permanently removed from the network, or are shutdown, or both.

[00121] FIG. 28B shows another example embodiment, however, that is specific to the situation in which no anomaly is detected by neighboring Intelligent Endpoint Systems.

[00122] In particular, at block 2810, a first Intelligent Endpoint System 102a detects an anomaly and checks with a neighboring device if they detects the same anomaly (block 281 1 ). For example the second Intelligent Endpoint System 102b is the closest neighbor to the first Intelligent Endpoint System 102a and therefore, it receives the request to check for the anomaly (block 2812). The second Intelligent Endpoint System 102b checks to see if it detects the same anomaly (block 2813), does not detect the anomaly, and then checks with a neighboring third Intelligent Endpoint System 102c to see if it has detected the same anomaly (block 2814).

[00123] The third Intelligent Endpoint System 102c executes the same operations (blocks 2812 to 2814). The result or results from the Intelligent Endpoint Systems 102b and 102c are transmitted back to the first Intelligent Endpoint System 102a (block 2815), namely that no anomaly has been detected by other devices. These operations could, for example, also be repeated by n additional Intelligent Endpoint Systems.

[00124] The second Intelligent Endpoint System 102b runs or executes a diagnostic check on the first Intelligent Endpoint System 102a (block 2816). For example, the diagnostic check helps to determine if the first Intelligent Endpoint System 102a has been compromised, damaged, hacked, misappropriated, anomalously relocated, etc. Depending on the result of the diagnostic check, the second Intelligent Endpoint System 102b could take action based on the result (block 2817). The same operations at blocks 2816, 2817 are also repeated by the third Intelligent Endpoint System 102c.

[00125] The first Intelligent Endpoint System 102a, in response to receiving that no anomaly has been detected by other devices, runs a self-diagnostic check (block 818). Depending on the results, it could also take action (block 819).

[00126] In an example operation at block 2817, if the one or more other Intelligent Endpoint Systems detect that the first Intelligent Endpoint System 102a is compromised, then they eject or ignore communications from the first Intelligent Endpoint System 102a and no longer transmit communications to the first Intelligent Endpoint System 102a.

[00127] In another example of block 2817, the one or more other Intelligent Endpoint Systems reflash the first Intelligent Endpoint System 102a.

[00128] In another example of block 2817, the one or more other Intelligent Endpoint Systems apply a lower weighting value on a data integrity score for data transmitted by the first Intelligent Endpoint System 102a.

[00129] In another example of block 2817, the one or more Intelligent Endpoint Systems create a condition that requires confirmation of n other Intelligent Endpoint Systems (e.g. that are in proximity to the first Intelligent Endpoint System 102a) to confirm the data from outputted from the first Intelligent Endpoint System 102a (e.g. including confirming that the data is a known known or an anomaly). In an example embodiment, the n other Intelligent Endpoint Systems are the n-nearest neighbors of the first Intelligent Endpoint System 102a.

[00130] In an example embodiment, if the first Intelligent Endpoint System 102a detects that it is compromised, then it self-destructs at block 2819.

[00131] In another example embodiment of block 2819, if the first Intelligent Endpoint System 102a detects that it is compromised, then it reflashes itself with new microcode. [00132] In other words, as per FIG. 28B, the first Intelligent Endpoint System itself is the anomaly and is dealt with by action.

[00133] In a different example embodiment of the Intelligent Endpoint Systems, the inherent architecture of the multiple Intelligent Endpoint Systems that are in relational communication with each other (e.g. peer-to-peer) is used to form a graph database.

Typically a graph database is implemented on one server, or on one set of servers. A graph database comprises virtual nodes and virtual edges between the virtual nodes, representing relationships between the virtual nodes. However, in an example embodiment, a graph database is herein defined by nodes that are respectively the Intelligent Endpoint Systems, and the edges between the nodes in the graph database are the actual communication links between the Intelligent Endpoint Systems. The data or metadata associated with each node in the graph database is physically stored in memory devices of each respective Intelligent Endpoint System. For example, data stored in relation to a first node in the graph database is physically stored in a memory device on a corresponding first Intelligent Endpoint System; data stored in relation to a second node in the graph database is physically stored in a memory device on a corresponding second Intelligent Endpoint System; and so forth. In other words, the graph database takes on the shape and characteristics of the collection of Intelligent Endpoint Systems. In an example embodiment, the graph database contains both data from the IES sources and anomalies, and metadata such as data and anomaly trends, IES computing utilization, network issues, business goals achieved. Storing the data and metadata in a graph database makes current and future processing more effective and efficient because data science (e.g. Al, ML, and STRIPA) can identify patterns sooner and faster in the graph database and then realtime select the right resources to perform IES computing. Storing the data and metadata in a graph database can also help eliminate duplicate data, duplicate metadata, and duplicate knowns, which in turn reduces both computing, storing, and network processing costs and increases end to end compute efficiency.

[00134] In an example aspect of the graph database embodiment, a graph database mapping is provided that includes the Intelligent Endpoint Systems IDs and their edge relationships. The graph database mapping (which is different from the graph database itself) does not store the data of each Intelligent Endpoint itself. Instead, data of each node of the graph database is physically stored on the respective Intelligent Endpoint Systems.

[00135] In another example aspect, the network of Intelligent Endpoint Systems include public and private data stored on public and private systems. For example, a private Intelligent Endpoint System locally stores private data; a private Intelligent Endpoint System owns and retrieves its private data from a 3rd party system (e.g. a cloud computing system or other Intelligent Endpoint Systems); a private Intelligent Endpoint System locally stores public data; and a private Intelligent Endpoint Systems retrieves public data from a 3rd party system (e.g. a cloud computing system or other Intelligent Endpoint Systems). Therefore, the graph database is physically made of 3rd party systems and private Intelligent Endpoint Systems, with private and public data stored on a combination of the 3rd party systems and the private Intelligent Endpoint Systems. A graphing database mapping includes metadata about content stored on each node, such as whether the data is private or public, who it belongs to, the date of creation, etc.

[00136] In another example embodiment of Intelligent Endpoint Systems, a nearest neighbor blind processing and blind storage is applied for privacy objectives. In this example, self-identifying characteristics such as patient name, social security number, personally identifiable information, etc. are stripped out of the original Intelligent Endpoint System before executing computations to detect anomalies, or before cloud computing is performed. The resulting anonymized data is processed by nearest neighbor devices, compute clouds, third party processors, or any combination of the aforementioned, in order to compute first-to-detect and/or validate anomalies. In another example aspect, immutability and/or blockchain storage is used to store the anonymized data. Example applications could include, and are not limited to, Health Insurance Portability and

Accountability Act (HIPAA) compliance and General Data Protection Regulation (GDPR) compliance.

[00137] A different strategy is load balancing the Intelligent Endpoint Systems and/or compute clouds. In this example, the device and or cloud has intelligent compute thresholds, such as transactions per second or read/write actions per second, and the IES begins load balancing its compute with neighboring devices using software and/or hardware.

[00138] A different IES strategy is to make all or some of the IES devices and/or compute clouds role agnostic whereby any IES device can swap roles with another IES device or compute cloud; a compute cloud could swap roles with another IES compute cloud or IES device. Software or hardware, or both, can run scripts that make these changes and consequently swap IES endpoint roles.

[00139] Another example of an IES strategy is to intelligently combine IES devices and/or compute clouds and/or third party systems to collectively create a IES based neural network. Metaphorically, this is similar to a neuron and synapse where each IES device is a neuron and the synapses are the networks. The collective IES devices and or compute clouds and the networks perform computations to achieve a business goal, company objective, engineering task, etc. The IES and networks each have their own data science (e.g. Al, ML, and STRIPA algos) to perform specialized neural computing and or have overarching data science to optimize among the collective devices, computing clouds, and networks to achieve a goal or objective.

[00140] In an example embodiment, TEDs physically move and, at the same do one or more of the following: carry data, execute computations, sense or detect new data, perform actions, etc. The TEDs device can perform onboard compute to re-optimize its destination paths, processes or tasks to achieve a goal or optimize toward a goal, etc. These devices may confer with other devices or compute clouds for data and or computations in order to perform the recurring optimizations over time. These IES devices may confer with other devices and compute clouds to load balance and share work or tasks based on the outcomes, goals, tasks, business rules, conditions, or any combination of the

aforementioned.

[00141] Turning to FIG. 29, an example IES environment shows different locations, namely Location A, Location B, Location C, and Location D. Intelligent Endpoint Systems physically move from one location to another. For example, at Location A, there is a computing station 2901 and an Endpoint dispenser 2902. The computing station 2901 interacts with Intelligent Endpoint Systems located at Location A, for example, by exchanging code, data, etc. In an example embodiment, lower power Intelligent Endpoint Systems do not have the ability to connect to the Internet directly or to connect directly to other cloud computing devices. Therefore, the lower power Intelligent Endpoint Systems that are at Location A locally connect to the computing station 2901 , and via the computing station 2901 , can download or upload (or both) data or code to other networks and platforms (e.g. the Internet, other private networks, cloud compute platforms, etc.).

[00142] The computing station 2901 also interacts with the Endpoint dispenser 2902, which in turn dispenses Intelligent Endpoint Systems. For example, based on commands, objectives, feedback (from other Intelligent Endpoint Systems or from other computing devices), business rules, data science, conditions, etc., the computing station 2901 in turn commands or controls the Endpoint dispenser 2902 to dispense Intelligent Endpoint Systems. In an example embodiment, the Endpoint dispenser controls one or more of the following aspects: controls how many Intelligent Endpoint Systems are dispensed; controls the direction of where the Intelligent Endpoint Systems are dispensed; controls the data and the code residing on the Intelligent Endpoint Systems that are dispensed; and controls the frequency and timing of the dispensing of the Intelligent Endpoint Systems. [00143] In an example aspect, the Endpoint dispenser 2902 can upload data and code to the Intelligent Endpoint Systems. For example, the Endpoint dispenser flashes the Intelligent Endpoint Systems. In another example aspect, the Endpoint dispenser itself acts as an Intelligent Endpoint System in a network of Intelligent Endpoint Systems. In another aspect, the Endpoint dispenser includes actuators to dispense Intelligent Endpoint Systems. In other words, the Endpoint dispenser 2902 is a mechanism that provisions Intelligent Endpoint Systems.

[00144] In an example embodiment of an Endpoint dispenser 2902, the Endpoint dispenser 2902 includes a container that holds or stores Intelligent Endpoint Systems that are to be dispensed. In an example aspect, the Endpoint dispenser 2902 flashes all the Intelligent Endpoint Systems within the container at the same time. In another example aspect, the Endpoint dispenser 2902 flashes a given Intelligent Endpoint System as part of the process of dispensing the given Intelligent Endpoint System from the container.

[00145] In another example aspect, the Endpoint dispenser 2902 first flashes all the Intelligent Endpoint Systems within the container at the with a first code and/or data portion; and, at later time, secondly flashes one or more given Intelligent Endpoint Systems with a second code and/or data portion as the one or more given Intelligent Endpoint Systems are being dispensed from the container. For example, the first code and/or data portion is considered base code that applies to all the Intelligent Endpoint Systems stored within the container, and the second code and/or data portion is customized for the tasks, functions, or goals of the given Intelligent Endpoint Systems that are later being dispensed. This efficiently provides just-in-time flashing for customizable code and/or data portions.

[00146] In another example embodiment, an Endpoint dispenser 2902 does not store the Intelligent Endpoint Systems, but includes mechanisms (e.g. actuators) to dispense the Intelligent Endpoint Systems.

[00147] Continuing with FIG. 29, a transporter 2903 carries one or more Intelligent Endpoint Systems 2904 and one or more other things 2905 from Location A to Location B. The transporter 2903, for example, is a manned vehicle or an unmanned vehicle, or some other type of transport mode. Non-limiting examples include cars, trucks, trains, aircraft, spacecraft, boats, bicycles, scooters, people that carry an Intelligent Endpoint System, drones, conveyor systems, material handling robots, etc. When the transporter 2903 arrives at Location B, the computing station 2906 located at Location B can interact with the Intelligent Endpoint System(s) 2904. The computing station 2901 , which knows or plans that the Intelligent Endpoint System(s) 2904 travel from Location A to Location B, inserts data or code, or both, via the Endpoint dispenser 2902, into the Intelligent Endpoint Systems 2904.

[00148] In an example embodiment, while these Intelligent Endpoint Systems 2904 move from Location A to Location B, the Intelligent Endpoint Systems 2904 carry the data or the code; or the Intelligent Endpoint Systems 2904 execute computations; or the Intelligent Endpoint Systems 2904 sense, obtain or capture data from their local environment; or the Intelligent Endpoint Systems 904 manufacture, build, perform an action, etc.; or a combination thereof. For example, the Intelligent Endpoint Systems 2904 monitor the things 2905 while in transport. In another example, the Intelligent Endpoint Systems 2904 consume or modify, or both, the things 2905 while in transport. In another example, the Intelligent Endpoint Systems 2904 manufacture more of the things 2905 while in transport. The original data or code, or derivatives thereof, or outputs (e.g. digital or physical outputs, or both), or combinations of the aforementioned, are provided at Location B. For example, data or code are provided to the computing station 906 at Location B.

[00149] In other words, while the Intelligent Endpoint Systems 2904 are in transit, they perform a function. The Intelligent Endpoint Systems 2904 can be low powered and may purposely avoid connecting to a larger data network while in transit in order to save power.

[00150] The computing station 2906 can act as a repeater and upload data and code (e.g. the original data or code from the computing station 2901 , or derived or outputted data from the Intelligent Endpoint System 2904 while moving to Location B, or both) to other Intelligent Endpoint Systems 908. In turn, the Intelligent Endpoint Systems 2908, along with other things 2909, are physically transported by a transporter 2907 from Location B to Location D. After it arrives at Location D, the Intelligent Endpoint Systems 2908 provide the data or code, or both, to the computation station 2910. Other Intelligent Endpoint Systems 91 1 are, for example, also held or aggregated at Location D. These Intelligent Endpoint Systems 291 1 can be deployed to other locations.

[00151] In another example aspect, Intelligent Endpoint Systems 2912 and 2913 can be incorporated or part of a transporter and, therefore, can move on their own between locations. In other words, various transport devices and transport vehicles 2912, 9213 themselves are Intelligent Endpoint Systems.

[00152] Data or processing, or both, can be shared amongst different Intelligent Endpoint Systems 2908, 2912, if they are in close enough proximity to each other. For example, the Intelligent Endpoint Systems 2908 and 2912 are on the same path (or are crossing paths) between Location B and Location C. [00153] The distribution of data, processing, and other actions (e.g. manufacturing, building, performing an action, etc.) can be distributed amongst these moving Intelligent Endpoint Systems and can be optimized based on their paths to different locations. Other parameters can be used to optimize the distribution of computing amongst these Intelligent Endpoint Systems, and these parameters could also be used to plan and affect the travel paths of the Intelligent Endpoint Systems.

[00154] Turning to FIG. 300A, another example embodiment shows Intelligent Endpoint Systems 102a, 102b, 102c, 102d coordinating data updates with each other. In this example embodiment, each of the Intelligent Endpoint Systems have stored thereon one or more models. A model is a set of code and data. A model could be one or more of: a blockchain, a database, an immutable ledger, a 3D virtual environment that represents a real world or physical world, a simulation, a social network model, a chemical model, a business model, a medical model, a manufacturing model, a distribution model, a model of a physical object or physical system, etc.

[00155] The first Intelligent Endpoint System 102a has stored thereon Model 1 and Model 2. The second Intelligent Endpoint System 102b has stored thereon Model 1 and Model 2. The third Intelligent Endpoint System 102c has stored thereon Model 2 and Model 3. The fourth Intelligent Endpoint System 102d has stored thereon Model 1 .

[00156] The first Intelligent Endpoint System 102a detects, generates, obtains, etc. data affects Model 2 (block 3001 ). At block 3002, the first Intelligent Endpoint System 102a then identifies other Intelligent Endpoint Systems that have Model 2 stored thereon. At block 3003, it propagates the data (or the updates to Model 2) to the other Intelligent Endpoint Systems with Model 2. Accordingly, the second and the third Intelligent Endpoint Systems 102b, 102c receive the propagation from the first Intelligent Endpoint System and they each respectively update Model 2 on their own hardware systems (blocks 3004, 3005).

[00157] In other words, the Intelligent Endpoint Systems can store different models and operate different models simultaneously. They can send relevant updates amongst each other, if they share the same model.

[00158] Turning to FIG. 30B, in a similar context as FIG. 10A, the first Intelligent Endpoint System 102a executes operations 3001 , 3002. At block 3006, the first Intelligent Endpoint System then determine which other Intelligent Endpoint Systems with Model 2 are affected by the data. In other words, there could be other Intelligent Endpoint Systems that have stored thereon Model 2, but would not be affected by the data obtained or detected or generated by the first Intelligent Endpoint System 102a. [00159] In this example, first Intelligent Endpoint System 102a determines that only the second Intelligent Endpoint System 102b is affected by the data.

[00160] At block 3007, the first Intelligent Endpoint System 102a sends the data or sends an updated Model 2, or both, to the second Intelligent Endpoint System 102b.

Accordingly, the second Intelligent Endpoint System 102b updates its copy of Model 2 (block 3004). This helps to reduce data transfers amongst the TED devices.

[00161] In another example embodiment of how the Intelligent Endpoint Systems interact with each other, a voting or consensus or governance approach is used to determine whether an action should be performed. For example, if enough neighboring Intelligent Endpoint Systems get the same results (e.g. a number of Intelligent Endpoint Systems greater than a threshold number), then a given Intelligent Endpoint System (or a collective of Intelligent Endpoint Systems) performs a given action or a given set of actions.

In an example embodiment, a voting or consensus or governance system is provided that biases the interaction amongst the Intelligent Endpoint Systems. The Intelligent Endpoint Systems interacts with this voting or consensus or governance system. In an example aspect, this voting or consensus or governance system is a remote computer system, or is implemented (e.g. physically resides) in a distributed manner on the Intelligent Endpoint Systems, or a combination thereof.

[00162] Turning to FIG. 31 , an example embodiment is provided for provisioning Intelligent Endpoint Systems. An extreme data network of existing Intelligent Endpoint Systems 3100 include the Intelligent Endpoint Systems 3101 , 3102. A potentially new Intelligent Endpoint System 3105, which potentially joins the network 3100, receives seed code and data 3103 from the first existing Intelligent Endpoint Systems 3101 and receives seed code and data 3104 from an nth existing Intelligent Endpoint System 3102.

[00163] At block 31 10, the new Intelligent Endpoint System 3105 receives the seed code and data from the multiple existing Intelligent Endpoint Systems in the network 3100. At block 31 11 , the new Intelligent Endpoint System 3105 detects the one or more provisioning conditions provided in the seed code and the data. At block 31 12, the new Intelligent Endpoint System 3105 determines if the one or more provisioning conditions are satisfied. This operation to make this determination could be made in combination with an existing Intelligent Endpoint System, for example, via a provisioning confirmation exchange 31 14. If and after the one or more provisioning conditions are satisfied, then the new Intelligent Endpoint System 3105 is provisioned to join the network 3100. [00164] In an example aspect, the provisioning process at block 31 13 includes providing the new Intelligent Endpoint System 3105 with one or more of: known knowns, anomalies to look out for, actions, IDs related to the network 3100, models, data science, etc.

[00165] In another example aspect, the provisioning conditions include one or more of the following: the new Intelligent Endpoint System receiving at least X seeds of code and data from respective X existing Intelligent Endpoint Systems, where X is a natural number; the new Intelligent Endpoint System receives the seeds of code and data from existing Intelligent Endpoint Systems within a certain threshold distance relative to the new Intelligent Endpoint System; the new Intelligent Endpoint System receives the seeds of code and data from existing Intelligent Endpoint Systems that have at least a certain rating; the new Intelligent Endpoint System receives the seeds of code and data from existing Intelligent Endpoint Systems that are of a certain device type; and the new Intelligent Endpoint System satisfies or successfully completes tests that are provided in the seed code and data (e.g. computation speed test, memory capacity test, data transmission test such as for bandwidth or speed, etc.).

[00166] In another example embodiment of provisioning, an Endpoint dispenser (e.g. such as the Endpoint dispenser 2902) physically dispenses one or more Intelligent Endpoint Systems in order to perform the provisioning.

[00167] In an example embodiment, the system of Intelligent Endpoint Systems or a centralized computing system, or both, provision one or more new Intelligent Endpoint Systems to replace existing Intelligent Endpoint Systems that are considered to be anomalies (e.g. including and not limited to compromised, damaged, misappropriated, functioning in an anomalous manner, etc.).

[00168] In an example embodiment, the system of Intelligent Endpoint Systems or a centralized computing system, or both, provision multiple new Intelligent Endpoint Systems when additional computing power, sensor capability, communication performance, memory capacity, or physical power, or a combination thereof is required. For example, this process of suddenly provisioning multiple Intelligent Endpoint Systems when needed is herein called bursting the Intelligent Endpoint Systems.

[00169] In an example embodiment, the system of Intelligent Endpoint Systems or a centralized computing system, or both, provision multiple new Intelligent Endpoint Systems based on predicted future requirements. For example, an event is scheduled in the future, or an event is predicted to take place in the future, that would likely use more computing power, sensor capability, communication performance, memory capacity, or physical power, or a combination thereof. Therefore, in anticipation of such a prediction, multiple new Intelligent Endpoint Systems are provisioned. For example, a natural disaster is predicted to take place, and multiple new Intelligent Endpoint Systems are automatically provisioned to accommodate the predicted additional computing, sensing, memory and communication to be performed in relation to the natural disaster. The Intelligent Endpoint Systems are inserted at or near a location (e.g. physical or digital locations, or both) where the predicted event will take place.

[00170] In another example embodiment of processing data on Intelligent Endpoint Systems, each Intelligent Endpoint Systems has soft data and hard data. It will be appreciated that data in general includes, but is not limited to data, algorithms, data science, code, etc. Hard data in this example herein refers to data that is not often used by the Intelligent Endpoint System (e.g. used less than a given threshold frequency). Soft data is data that is often used by the Intelligent Endpoint System (e.g. used more than a given threshold frequency). Within the set of soft data, there is native soft data and visiting soft data. Native soft data originates from a given Intelligent Endpoint System, or is specific to the Intelligent Endpoint System. Visiting soft data is soft data that is on the given Intelligent Endpoint System, but originates from another device, or is for another device.

[00171] In an example aspect of this hard data and soft data embodiment, if the given Intelligent Endpoint Systems receives a signal or command from another device (e.g.

another Intelligent Endpoint System or some other computing device) to do more processing, and the given Intelligent Endpoint Systems required more data space, then the given Intelligent Endpoint System compresses the hard data and then sends the hard data away to an off-site memory storage system or device. In another example aspect, the given Intelligent Endpoint System converts the soft data and to hard data, compresses it, and then sends it to an off-site memory storage system or device.

[00172] In another example aspect of this hard data and soft data embodiment, if the Intelligent Endpoint System receives a signal that more native soft data is coming, or will be generated, or will be required, then the given Intelligent Endpoint System discards the visiting soft data. The discarding of the visiting soft data is also a signal to other Intelligent Endpoint Systems to do the same.

[00173] In another example aspect of this hard data and soft data embodiment, if the Intelligent Endpoint System receives a distress signal that the visiting soft data is potentially dangerous, then the Intelligent Endpoint System discards the visiting soft data. The discarding of the visiting soft data is also a signal to other Intelligent Endpoint Systems to discard their respective visiting soft data.

[00174] In another example embodiment, an Intelligent Endpoint System transmits test code and data to other devices (e.g. other Intelligent Endpoint Systems) to look for fertile devices (e.g. desirable computing devices). The test code and data are scripts that, when executed, determine if certain algorithms can be run and/or certain data can be stored. If there is a positive result transmitted from a fertile device back to the Intelligent Endpoint System, then the Intelligent Endpoint System sends real code and data to the found fertile device. In an example aspect, the found fertile device gives various resources to the Intelligent Endpoint System, including, but not limited to: data, communication bandwidth, data storage, processing power, access to other networks, etc. In an example aspect, after the Intelligent Endpoint System first finds a fertile device (e.g. a finding that is characterized as an anomaly), the Intelligent Endpoint System transmits messages to other Intelligent Endpoint Systems about the found fertile device so that these other Intelligent Endpoint Systems can utilize the found fertile device. In another example aspect, the Intelligent Endpoint System sends test code and data to an inhospitable device and, in response, receives a negative result from the found inhospitable device. In an example aspect, after the Intelligent Endpoint System first finds an inhospitable device (e.g. a finding that is characterized as an anomaly), the Intelligent Endpoint System transmits messages to other Intelligent Endpoint Systems about the found inhospitable device so that these other Intelligent Endpoint Systems can avoid interacting with the inhospitable device. The negative result in relation to the found inhospitable device includes, for example, data that identifies inhospitable features (e.g. insufficient memory capacity, insufficient processing power, insufficient security measures, insufficient communication performance, etc.). In a further example aspect, the Intelligent Endpoint System uses data science and machine learning to identify which of these inhospitable features may likely improve over time. If there are one or more inhospitable features that are classified to likely improve, then the Intelligent Endpoint System at a future time sends a second set of test code to the found inhospitable device to determine if it has changed to become a fertile device.

[00175] In another example embodiment, the Intelligent Endpoint Systems each adapts their processing, memory storage, actions, or combinations thereof, based on one or more of: current energy availability, predicted future energy availability, current energy

consumption, and predicted energy consumption. In an example aspect, the energy (e.g. electrical power) of an Intelligent Endpoint System is renewable. In another example aspect, the energy of an Intelligent Endpoint System is transferrable. In a further example aspect, the energy is transferrable amongst Intelligent Endpoint Systems, so that one Intelligent Endpoint System can renew the energy supply of another Intelligent Endpoint System. In an example aspect, the energy is stored in a battery.

[00176] Turning to FIG. 32, an example architecture of a system of Intelligent Endpoint Systems is provided. A first set of Intelligent Endpoint Systems 3201 interact with each other and one or more environments to collect data, sense data, capture data, communicate data, process data, store data, etc. The Intelligent Endpoint Systems 3201 , for example, interact with 3rd parties 3202 (e.g. 3rd party databases, 3rd party devices, 3rd party environments, 3rd party platforms, etc.). In an example embodiment, the one or more 3rd parties are Intelligent Third Party Endpoint Systems.

[00177] In another example aspect, the first set of Intelligent Endpoint Systems 3201 form a faceted database. A faceted database herein refers to multiple databases. In an example aspect, at least some of these databases are related to each other. For example, different subsets of the Intelligent Endpoint Systems 3201 are used in different environments or different applications, or both. In another example, different subsets of the Intelligent Endpoint Systems 3201 also have different functions or different capabilities, or both. These differences lead, for example, to developing different databases, which as a collective is herein called a faceted database. In an example aspect, there is commonality amongst the databases in the faceted database, including, but not limited to, one or more of the following commonalities: common index(es), common pattern(s), common thematic data, common type(s) of data, common topic(s) of data, common event(s) in the data, common action(s) in the data, etc.

[00178] The first set of Intelligent Endpoint Systems transmit data to a load balancer system 3203 (e.g. which comprises one or more load balancing devices). The load balancer system then transmits the data to one or multiple Intelligent Endpoint Systems 3204, which are part of a second set. This second set of Intelligent Endpoint Systems 3204 are also herein called Intelligent Synthesizer Endpoint Systems.

[00179] In an example embodiment, the second set of Intelligent Endpoint Systems 3204 form a master database. In another example embodiment, either in addition or in alternative, the second set of Intelligent Endpoint Systems execute computations to process the received data using additional data science. The second set of Intelligent Endpoint Systems synthesize the data received from the first set of Intelligent Endpoint Systems by applying STRIPA. In other words, the second set of Intelligent Endpoint Systems act as a centralized computing resource on behalf of the first set of Intelligent Endpoint Systems, even though the second set is actually a collective of separate and distributed devices.

[00180] The master database residing on the second set of Intelligent Endpoint Systems 1204 can be referenced or queried by one or more Intelligent Endpoint Systems 3201 from the first set. Conversely, one or more of the Intelligent Endpoint Systems 3204 in the second set can query one or more of the databases that form part of the faceted database, which is stored in the first set.

[00181] In an example aspect, the faceted database that resides on the first set of Intelligent Endpoint Systems includes one or more blockchains or one or more immutable ledgers. In another example aspect, the master database that resides on the second set of Intelligent Endpoint Systems includes a master blockchain or a master immutable ledger.

[00182] The load balancer 3203 manages the distribution of data, processing, and communication amongst the second set of Intelligent Endpoint Systems 3204. The load balancer also manages the distribution of data, processing and communication amongst the first set of Intelligent Endpoint Systems 3201 .

[00183] Turning to FIG. 33, another example embodiment of an architecture of Intelligent Endpoint Systems is provided. Different sets of Intelligent Endpoint Systems form different portions of a neural network system. The example shown in FIG. 33 relates to generative adversarial networks (GANs), which is used in artificial intelligence. A first set includes generator Intelligent Endpoint Systems 3301 and a second set includes discriminator Intelligent Endpoint Systems 3302. The generator Intelligent Endpoint Systems 3301 store and run a generator neural network in a distributed manner. The discriminator Intelligent Endpoint Systems 3302 store and run a discriminator neural network in a distributed manner.

[00184] In particular, the generator Intelligent Endpoint Systems 3301 obtain, sense or capture noise data 3303 and use this noise data to compute generated data or fake data 3304. The discriminator Intelligent Endpoint Systems 3302 obtain, sense or capture real data 3305. The discriminator Intelligent Endpoint Systems 3302 use the real data 3305 and the generated data 3304 to make classifications or predictions 3306 in relation to the obtained real data 3305. For example, the classifications or predictions include determining whether something is real or fake. In another example, the classifications or predictions include determine whether an anomaly has been detected or predicted, or whether a known known has been detected or predicted. [00185] In other neural network computing systems, not limited to GANs, different portions of the neural networks are implemented by different sets of Intelligent Endpoint Systems.

[00186] In an example aspect, a first dispenser flashes and dispenses TEDs that form the discriminator network. A second dispenser flashes and dispenses TEDs that form the generator network.

[00187] In some example embodiments, the at least one of the plurality of Intelligent Endpoint Systems can be configured to autonomously update a local data store, data science, graph database, immutable ledger or blockchain (or both), index, memory, or app, to include the local data and or non-local data store, applications, systems, and third-party systems, and optionally to take a corresponding autonomous decisions and/or autonomous action if the query results from at least another one of the plurality of Intelligent Endpoint Systems responds with answers indicating the data is known or unknown. In some embodiments, the corresponding action is in response to an evaluation of the local data and/or one or more non-local data stores, applications, systems, immutable ledgers or blockchains (or both) and third-party systems. In some embodiments, the evaluation of the local data may be determined in response to an application selected from the group consisting of business rules, data science, computing requirements, and workflow actions applied to the local data and/or non-local data stores, immutable ledgers or blockchains (or both), applications, systems, and third-party systems.

[00188] In some example embodiments, some or all of the aforementioned Intelligent Endpoint System embodiments can be configured to use immutable technologies (such as, but not limited to, blockchains), which involve anonymous, immutable and encrypted ledgers and records that span over N number of Intelligent Endpoint Systems. These distributed ledgers, which are distributed in over multiple Intelligent Endpoint Systems, can be in the form of blockchains or other types of currently-known and future-known immutability protocols. These immutable ledgers can reside in RAM, cache, solid state, and spinning disk drive stores. In an alternative embodiment, these aforementioned stores can span across an ecosystem of store devices involving technologies such as Memcached, Apache Ignite; graph databases such as Giraph, Titan, and Neo4j, and structure and unstructured data stores such as Hadoop, Oracle, MySQL, etc.

[00189] In some example embodiments, the compute related to the immutable technologies, which is intrinsically compute intensive, can span a plurality of Intelligent Endpoint Systems in order to distribute the computing intensity. [00190] It is appreciated that these computing and software architectures are for example. Other architectures can also be used to accelerate the processing of data.

[00191] Below are some example embodiments and related aspects.

[00192] In an example embodiment, a dispensing system includes a client computing device in data communication with a remote computing system, and the remote computing system is in data communication with a dispenser. The dispenser includes a container that stores TEDs, a computing element that wirelessly flashes the TEDs, and a dispenser mechanism that deploys the TEDs. Responsive to the remote computing system obtaining a first set of data from the client computing device, the remote computing system transmits a second set of data to the dispenser. Responsive to the dispenser obtaining the second set of data, the dispenser at least one of wirelessly flashes the TEDs and deploys one or more of the TEDs.

[00193] In an example aspect, the second set of data is computed from the first set of data or includes the first set of data, or both. In another example aspect, the dispenser wirelessly flashes the TEDs with a third set of data, and the third set of data is computed from the second set of data or includes the second set of data, or both.

[00194] In another example aspect, the dispenser uses the computing element to wirelessly flash the TEDs to affect code that is stored on the TEDs and is executable by the TEDs.

[00195] In another example aspect, the dispenser uses the computing element to wirelessly flash the TEDs to affect data that is stored on the TEDs.

[00196] In another example aspect, the dispenser is configured to wirelessly provide power to the TEDs.

[00197] In another example aspect, the dispenser, via the computing element, is configured to read data stored on the TEDs.

[00198] In another example aspect, the dispenser is movable.

[00199] In another example aspect, the dispenser is attached to a vehicle.

[00200] In another example aspect, the dispenser is integrated into a manufacturing system to deploy one more TEDs into manufactured objects.

[00201] In another example aspect, the dispenser is integrated into a pill manufacturing system to deploy one more TEDs into pills. [00202] In another example aspect, the dispenser is integrated into a 3D printing system to deploy one more TEDs into a 3D printed part.

[00203] In another example aspect, the dispenser is integrated into an injection molding apparatus to deploy one more TEDs into an injection molded part.

[00204] In another example aspect, the dispenser is integrated with a material dispenser that dispenses a material, and the TEDs are dispensed with the material. For example, the material is a liquid. In another example, the material is a mixture. In another example, the material is particulate. In another example, the material is a concrete mix.

[00205] In another example aspect, each one of the TEDs include a sensor module, a power module, a processing module, a memory module, and a communication module. In another example aspect, each one the TEDs further include an actuator module.

[00206] In another example aspect, the TEDs are in data communication with each other.

[00207] In another example aspect, the system further includes a downstream device in data communication with at least one of the dispenser and the remote computing system. The downstream device receives one or more of the TEDs that have been dispensed from the dispenser.

[00208] In another example aspect, the downstream device reads data from the one or more of the TEDs that have been received at the downstream device.

[00209] In another example aspect, the downstream device writes data to the one or more of the TEDs that have been received at the downstream device.

[00210] In another example aspect, the downstream device wirelessly provides power to the one or more of the TEDs that have been received at the downstream device.

[00211] In an example embodiment, a distributed computing system is provided that includes TEDs and a dispenser that dispenses the multiple TEDs. The dispenser includes a computing element that wirelessly transmits data to the TEDs. The TEDs are in data communication with each other and each of the TEDs includes a sensor, a power module, a processor, a memory device, and a communication device. After the TEDs have been dispensed from the dispenser, a given TED detects a certain condition using its given sensor and transmits data regarding the certain condition to one or more other TEDs that have been dispensed by the dispenser.

[00212] In an example aspect, the given TED transmits the data regarding the certain condition to n-nearest TEDs that have been dispensed, where n is a natural number. [00213] In another example aspect, responsive to receiving the data regarding the certain condition, each of the one or more TEDs then execute a checking process for the certain condition using their respective sensor.

[00214] In another example aspect, a result of the checking process is obtained by the given TED.

[00215] In another example aspect, a result of the checking process is obtained by the dispenser.

[00216] In another example aspect, the computing system further includes a remote computing system that controls the dispenser, and wherein a result of the checking process is obtained by the remote computing system.

[00217] In another example embodiment, a distributed computing system is provided that includes a first dispenser that dispenses a first set of TEDs. The first dispenser includes a computing element that wirelessly transmits data to the first set of TEDs while contained in the first dispenser. The distributed computing system further includes a second dispenser that dispenses a second set of TEDs. The second dispenser includes another computing element that wirelessly transmits data to the second set of TEDs while contained in the second dispenser. After the first set of TEDs and the second set of TEDs have been dispensed, the first set of TEDs and the second set of TEDs form different portions of a TED network.

[00218] In an example aspect, the TED network is a generative adversarial network, and the first set of TEDs form a discriminator network and the second set of TEDs form a generator network. For example, the first of set of TEDs comprise sensors to obtain noise data and the first set of TEDs use the noise data to compute generated data. In another example, the second set of TEDs comprise sensors to obtain real data, and the second set of TEDs use the real data and the generated data to compute a classification or a prediction in relation to the real data.

[00219] In an example aspect, the TED network is a distributed database. The first set of TEDs form a faceted database. The second set of TEDs form a master database. The faceted database and the master database form the distributed database. One or more TEDs from the first set of TEDs query the master database for data. In an example aspect, the master database is immutable.

[00220] In an example embodiment, a dispenser is provided that includes: a container that stores TEDs; a computing element positioned within the container that wirelessly and simultaneously flashes the TEDs while stored in the container; and a dispenser mechanism that deploys the TEDs.

[00221] In an example aspect, the container includes a shielding structure to prevent external radio signals from interacting the TEDs stored in the container.

[00222] In another example aspect, the dispenser mechanism dispenses one TED at a time.

[00223] In another example aspect, the dispenser mechanism dispenses multiple TEDs at a time.

[00224] In another example aspect, the computing element is in data communication with a remote computing system, and the computing element transmits data, which is receivable by the remote computing system. The computing element transmits data that includes, for example, one or more of: a number of TEDs stored in the container, a location of the dispenser, environmental data, and deployment data of the TEDs.

[00225] In an example embodiment, a container is provided that includes: a container body, a barrier that opens and closes an opening in the container body, a computing element, and an action device that locks and unlocks the barrier. The container body stores therein TEDs. The computing element wirelessly communicates with the TEDs, and the computing element controls the action device to at least one of unlock and lock the barrier.

[00226] In an example aspect, the computing element exchanges data with the TEDs to count the number of TEDs stored in the container body.

[00227] In an example aspect, the computing element includes a location positioning system that identifies the location of the container.

[00228] In an example aspect, the computing element receives a command, which is transmittable by a remote computing system, to unlock or lock the barrier, and responsive to receiving the command, the computing element controls the action device to unlocks or locks the barrier.

[00229] In an example aspect, the container body stores pills, and each one of the pills includes at least one TED. In an example aspect, for each one of the pills, the at least one TED is embedded into the respective pill.

[00230] In an example aspect, the action device further includes a destruction device to destroy the pills stored in the container body, and the computing element is configured to activate the destruction device. In an example aspect, the destruction device emits light inside the container body to destroy the pills. In an example aspect, the destruction device dispenses a chemical in the container body to destroy the pills.

[00231] In another example aspect, each one of the TEDs releasably store a reactive chemical that reacts with one or more ingredients in the pills and, responsive to a release command from the computing element, the TEDs release the reactive chemical.

[00232] It will be appreciated that any module or component exemplified herein that executes instructions may include or otherwise have access to computer readable media such as storage media, computer storage media, or data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by an application, module, or both. Any such computer storage media may be part of the servers or devices or accessible or connectable thereto. Any application or module herein described may be implemented using computer readable/executable instructions that may be stored or otherwise held by such computer readable media.

[00233] It will be appreciated that different features of the example embodiments of the system and methods, as described herein, may be combined with each other in different ways. In other words, different devices, modules, operations, functionality and components may be used together according to other example embodiments, although not specifically stated.

[00234] The steps or operations in the flow diagrams described herein are just for example. There may be many variations to these steps or operations according to the principles described herein. For instance, the steps may be performed in a differing order, or steps may be added, deleted, or modified.

[00235] It will also be appreciated that the examples and corresponding system diagrams used herein are for illustrative purposes only. Different configurations and terminology can be used without departing from the principles expressed herein. For instance, components and modules can be added, deleted, modified, or arranged with differing connections without departing from these principles. [00236] Although the above has been described with reference to certain specific embodiments, various modifications thereof will be apparent to those skilled in the art without departing from the scope of the claims appended hereto.