The computing system of FIG. 1 utilizes blockchain-encoded records to securely represent assets of the computing system. The assets include physical assets like any of the objects and computing devices and virtual assets like a model of a portion of the premise and a representation of a software process (e.g., an AI software module associated with optimizing operation of a bank of lighting controllers).

In an embodiment, a blockchain of the blockchain-encoded records is utilized to record steps of an asset lifecycle for an asset such as creation, initial and subsequent ownership (e.g., by a controlling entity), deployment, configuration, establishing trust, service-life utilization, and decommissioning. For instance, a new blockchain is created when a new computing system is deployed for a new premise to enjoy the benefits of a digital twin solution. A new block representing a new or transferred asset of the computing system is created by an associated computing entity on behalf of an initial owner. The blockchain is updated when the asset transitions through the lifecycle. The blockchain is updated when control (e.g., ownership) of the asset is changed.

The blockchain includes a plurality of blocks 2-4. Each block includes a header section and a transaction section. The header section includes one or more of a nonce, a hash of a preceding block of the blockchain, where the preceding block was under control of a preceding device (e.g., a real-world environment computing entity, a premise interface computing entity, a blockchain node computing device, a meshing node, the computing entity of the digital twin environment, etc.) in a chain of control of the blockchain, and a hash of a current block (e.g., a current transaction section), where the current block is under control of a current device in the chain of control of the blockchain.

The transaction section includes one or more of a public key of the current device, a signature of the preceding device, smart contract content, change of control from the preceding device to the current device, and content information from the previous block as received by the previous device plus content added by the previous device when transferring the current block to the current device.

FIG. 5D further includes computing devices 2-3 (e.g., devices #2 and #3) to facilitate illustration of generation of the blockchain. Each device includes a hash function, a signature function, and storage for a public/private key pair generated by the device.

In an example of operation of the generating of the blockchain, when the device 2 has control of the blockchain and is passing control of the blockchain to the device 3 (e.g., the device 3 is transacting a transfer of content from device 2), the device 2 obtains the device 3 public key from device 3, performs a hash function 2 over the device 3 public key and the transaction 2 to produce a hashing resultant (e.g., preceding transaction to device 2) and performs a signature function 2 over the hashing resultant utilizing a device 2 private key to produce a device 2 signature.

Having produced the device 2 signature, the device 2 generates the transaction 3 to include the device 3 public key, the device 2 signature, device 3 content request to 2 information, and the previous content plus content from device 2. The device 3 content request to device 2 information includes one or more of a detailed content request, a query request, background content, and specific instructions from device 3 to device 2 for access to an object. The previous content plus content from device 2 includes one or more of content from an original source, content from any subsequent source after the original source, an identifier of a source of content, a serial number of the content, an expiration date of the content, content utilization rules, and results of previous blockchain validations.

Having produced the transaction 3 section of the block 3 a processing module (e.g., of the device 2, of the device 3, of a transaction mining computing entity, of another computing device), generates the header section by performing a hashing function over the transaction section 3 to produce a transaction 3 hash, performing the hashing function over the preceding block (e.g., block 2) to produce a block 2 hash. The performing of the hashing function may include generating a nonce such that when performing the hashing function to include the nonce of the header section, a desired characteristic of the resulting hash is achieved (e.g., a desired number of preceding zeros is produced in the resulting hash which is subsequently verified, and where the number of zeros is adapted for a subset of blocks).

Having produced the block 3, the device 2 sends the block 3 to the device 3, where the device 3 initiates control of the blockchain. Having received the block 3, the device 3 validates the received block 3. The validating includes one or more of verifying the device 2 signature over the preceding transaction section (e.g., transaction 2) and the device 3 public key utilizing the device 2 public key (e.g., a re-created signature function result compares favorably to device 2 signature) and verifying that an extracted device 3 public key of the transaction 3 compares favorably to the device 3 public key held by the device 3. The device 3 considers the received block 3 validated when the verifications are favorable (e.g., the authenticity of the associated content is trusted). A technological improvement is provided over prior art communication and computing systems associated with data management since only the device possessing control over a block may modify the block as part of such a tightly integrated overall data management process.

Only a present trusted device may pass the control to a next trusted device that is part of the overall data management process. Only blocks with nonces of an expected number of zeros are trusted.

The method described above in conjunction with a processing module of any computing entity of the computing system can alternatively be performed by other specialty modules of the computing system of FIG. 1 or by other specialty devices. In addition, at least one memory section that is non-transitory (e.g., a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing entities of the computing system, cause one or more computing devices of the computing system to perform any or all of the method steps described above.

FIGS. 6A-6B are schematic block diagrams of another embodiment of a computing system illustrating an example of a self-forming communication and control system. The computing system includes the object 24-1 of FIG. 1, the computing entity 21-N of FIG. 1, and the computing entity 22 of FIG. 1. The object 24-1 includes a physical object such as material in a factory premise where the material includes one or more of factory machinery, work in progress, and finished goods of inventory. The factory includes production steps where the material is moving or in a static condition. When moving, the movement includes one or more of circular motion (e.g., a circular motion vector), linear motion (e.g., a uniform motion vector), and complex motion (e.g., varying vectors).

FIG. 6A illustrates an example of operation of a method for processing data of the self-forming communication and control system where a first step includes the processor of the computing entity 21-N executing environment interpretation software from a first memory causing the processor to detect object 24-1 of an environment based on environment signaling 30 of the environment to produce a detected object. The object includes at least one of a physical object and a virtual object. For example, the processor interprets environment signaling 30 from a visual sensor 122 of the environment sensor module 14 that reveals an image of object 24-1.

The detecting of the object includes a variety of sub-steps. A first sub-step includes the processor obtaining the environment signaling of the environment from an environment sensor module. For example, and image sensor of the environment sensor module 14 captures imagery of the object 24-1, such as widgets moving down a conveyor belt.

A second sub-step includes the processor indicating the physical object as the detected object when identifying a physical object pattern from at least one of an unencoded direct electromagnetic emission, an unencoded indirect electromagnetic emission, and an unencoded mechanical wave of the environment signaling. For example, processor utilizes object image detecting software to detect the object 24-1 based on the image from the image sensor (e.g., unencoded direct electromagnetic emission such as light). The detecting includes at least one of detecting a specific object, object type, and an unknown object. The image detecting software further includes a variety of approaches including comparing pixels of the captured image to pixels of a stored image recovered from the twin memory 58. A second approach includes utilizing machine learning to detect the object when a likelihood of detection is greater than a threshold level when comparing the image from the image sensor to knowledge of the AI memory 59.

Alternatively, the second sub-step includes the processor indicating the virtual object as the detected object when identifying a virtual object pattern from at least one of an encoded electromagnetic emission, an encoded electronic signal, and an encoded mechanical wave of the environment signaling. For example, the processor interprets data from the wireless communication modem 86 that results from the wireless communication modem 86 receiving an encoded electronic admission (e.g., an encoded wireless signal from a device associated with the object 24-1). As another example, the process interprets the data from the network interface module 72 of FIG. 3 from the wired LAN 88 sourced by a device (e.g., a factory computer) associated with the object 24-1.

Having detected the object 24-1, a second step of the example method of operation includes the processor of the computing entity 21-N executing object identification software from a second memory to facilitate intercommunication between the environment interpretation software and the object identification software to produce an identified object and object profile information based on the detected object and an object knowledge database (e.g., the memory 58, the AI memory 59). The object profile information includes the object information 202 of FIG. 5A, and further includes object basics, object deployment information, and object availability information.

The producing of the identified object includes a series of sub-steps. A first sub-step includes the processor accessing a portion of the twin memory 58 (e.g., hereafter interchangeably referred to as twin memory or digital twin memory) that includes the object knowledgebase based on the detected object (e.g., utilizing identity of the detected object as an index into the knowledgebase). A second sub-step includes the processor comparing the detected object to the portion of the digital twin memory that includes the object knowledgebase to produce the identified object. For example, the processor compares the widget detected object to the portion of the twin memory and matches to a particular widget (e.g., a candy) as the identified object.

A third sub-step includes the processor accessing the portion of the digital twin memory that includes the object knowledgebase based on the detected object to produce the object profile information. For example, the processor accesses the twin memory 58 to retrieve the object profile information with regards to the particular widget. For instance, the object profile information includes parameters associated with the candy such as volume per unit of time ranges, weight ranges, size information, expected movement vector information while on the conveyor or other device within manufacturing, etc.

As another example, the processor sources the object profile information from the object knowledgebase based on one or more of the identified object and the environment signaling 30. The object knowledgebase within the AI memory 59 includes information stored as knowledge with regards to objects that is part of one or more AI computing entities. For example, the processor applies machine learning to the detected object while accessing the object knowledgebase stored in the AI memory 59 to produce the identified object. The processor indexes the twin memory 58 utilizing the identified object to recover the object profile information (e.g., retrieve, generate using a portion affiliated with the identified object of the object knowledgebase as new object profile information based on most recent observations).

FIG. 6B further illustrates the example of operation of the method for processing data of the self-forming communication and control system where a third step includes the processor of the computing entity 21-N executing instruction generation software for generating processor-executable instructions for use with the object profile information and further environment signaling to provide object tracking for the object 24-1. The instructions includes object tracking of the identified object within the environment.

The generating of the instructions includes the processor executing the instruction generation software from a third memory to facilitate intercommunication between the object identification software and the instruction generation software to generate the processor-executable instructions for use with the object profile information and further environment signaling. The instructions pertain to object tracking of the identified object within the environment in a format ready to use by the processor such as one or more of track the circular motion, track the linear motion, track the complex motion, detect when a static object moves, count objects passing by, and count rotations of a part of a machine.

The generation of the instructions includes a series of sub-steps based on a variety of approaches. Example approaches include extracting instructions from the AI memory based on the object profile information, generating new instructions based on the object profile information and previous instructions from the AI memory for similar objects, and decoding the instructions from at least one of the environment signaling 30 and the premise messages 33.

A first sub-step of the generating of the instructions includes the processor determining tracking parameters of the object tracking of the identified object based on the object profile information. For example, establish instructions based on particular expected movements of the identified object. For instance, the processor extracts the tracking parameters from the object profile information for the identified object from the twin memory 58.

A second sub-step includes the processor determining signaling parameters of the further environment signaling based on the identified object. For example, establish what types of signaling to expect of the further environment signaling for the identified object by extracting the signaling parameters for the identified object from the twin memory 58. As another example, the processor utilizes generative AI and the knowledgebase of the AI memory 59 to predict what present and/or future types of signaling to expect of the further environment signaling for the identified object.

A third sub-step includes the processor generating the processor-executable instructions based on the tracking parameters and the signaling parameters to produce the processor-executable instructions to facilitate subsequent collection of the further environment signaling associated with the identified object to provide the object tracking of the identified object within the environment. For example, the processor utilizes generative AI and a processor instruction portion of the knowledgebase of the AI memory 59 to produce the processor-executable instructions for the particular tracking parameters and the particular signaling parameters expected for the identified object from the further environment signaling.

A fourth step of the example method of operation for the processing data of the self-forming communication and control system includes the processor executing object tracking software from a fourth memory to facilitate intercommunication between the instruction generation software and the object tracking software to store object tracking information for the identified object within the digital twin memory by applying the processor-executable instructions to the further environment signaling. The object tracking information includes historical documentation of motions of the object 24-1 such as number of revolutions per unit of time over an observation timeframe, a count of passing objects that are similar, a complex motion pattern, etc.

The storing of the object tracking information by applying the instructions to the further environment signaling includes a series of sub-steps. A first sub-step includes the processor obtaining the further environment signaling. For example, the processor collects further image output from the image sensor of the environment sensor module 14 in accordance with the signaling parameters and the processor-executable instructions.

A second sub-step includes the processor interpreting the further environment signaling utilizing the processor-executable instructions associated with the object tracking of the identified object to produce the object tracking information. For example, the processor identifies movement of the identified object and records the movement as movement vector information over a movement timeframe.

A third sub-step includes the processor storing the object tracking information in a portion of the digital twin memory associated with the identified object. For example, the processor stores the object tracking information in the twin memory 58 of the computing entity 21-N along with timestamp information, indexed by at least one of time, object type, object identifier (ID), premise ID, or any other feasible index to facilitate subsequent generation of dashboard information and more. As another example, the processor issues premise messages 33 to the computing entity 22, where the premise messages 33 includes the object tracking information for storage in the twin memory 58 of the computing entity 22.

Having produced and stored the object tracking information, a fifth step of the example method of operation includes the processor executing dashboard software from a fifth memory to facilitate intercommunication between the object tracking software and the dashboard software to interpret a portion of the object tracking information for the identified object from the digital twin memory to produce dashboard information. The dashboard information includes a representation of status of the identified object based on the further environment signaling and in accordance with the object profile information. Examples include graphical representations of an image of the object, the number of revolutions per unit of time over several observation timeframes, the counts of passing objects that are similar within another timeframe, the complex motion pattern, and any other representation of the real-world environment.

In the example, the processor of the computing entity 22 executes the dashboard software to carry out a series of sub-steps. Generally, subsequent steps may utilize the processor of the computing entity 21-N and/or the computing entity 22. In a first sub-step the processor of the computing entity 22 obtains the portion of the object tracking information that corresponds to the further environment signaling for the identified object from the digital twin memory (e.g., via the index for the identified object). In a second sub-step the processor interprets the portion of the object tracking information in accordance with the object profile information to produce the dashboard information. For example, the processor generates a tabular output indicating position information of the identified object over a series of time frames. As another example, the processor generates an animated representation of the identified object along with summary metrics indicating volume per unit of time for similar objects (e.g., pieces of candy moving down a conveyor belt or chute).

In another example, the processor selects a portion of the object tracking information based on detecting an anomaly and interpreting the selected portion of the object tracking information recovered from the twin memory 58 utilizing the object profile information (e.g., expected movement, graphic backgrounds, graphic foregrounds, etc.) to produce the dashboard information to bring attention to the anomaly. The processor stores the dashboard information in the digital twin memory 58 for subsequent utilization including providing a graphic output to a user interface output device, processing the dashboard information by an AI processor to provide a high level summary and/or to produce prescriptive information (e.g., corrective actions to abate the anomaly), and sending the dashboard information to another computing entity.

Having produce the dashboard information, a sixth step of the example method of operation includes the processor executing prescriptive software from a sixth memory to facilitate intercommunication between the dashboard software and the prescriptive software to process a portion of the dashboard information to produce prescriptive information within the artificial intelligence (AI) memory 59. The prescriptive information includes one or more of an interpretation of the portion of the dashboard information, an evaluation of the portion of the dashboard information against a standard, and adaptive processor-executable instructions for use with the object profile information and the further environment signaling to cause change with regards to the identified object within the environment.

The processing of the portion of the dashboard information to produce the prescriptive information includes a series of sub-steps. In a first sub-step the processor obtains the portion of the dashboard information corresponding to a prescriptive timeframe from the digital twin memory. For example, the processor accesses the twin memory 58 to obtain the portion of the dashboard information for a timeframe associated with the example anomaly.

In a second sub-step the processor processes the portion of the dashboard information in accordance with the object profile information to produce preliminary prescriptive information. The preliminary prescriptive information indicates issues, high level suggested actions, course corrections, etc. associated with the example anomaly. For example, the processor applies generative AI to the portion of the dashboard information in accordance with the object profile information to identify high level information and suggestions.

In a third sub-step the processor determines a format for the prescriptive information based on the preliminary prescriptive information and an object knowledgebase of the AI memory. For example, the processor applies the generative AI to the preliminary prescriptive information in accordance with the object profile information to obtain more the format that is more granular for the situation.

In a fourth sub-step the processor interprets the portion of the dashboard information in accordance with the format for the prescriptive information to produce the prescriptive information. For example, the processor once again utilizes the generative AI approach to process the portion of the dashboard information utilizing the format to produce the prescriptive information that carries a more targeted outcome. In a fifth sub-step the processor stores the prescriptive information within the AI memory 59 to facilitate even better future prescriptive information and to provide an output for the present system. A technological improvement is provided by the self-forming communication and control system such that subsequent monitoring by the premise interface 15 of FIG. 1 alone can suffice to abate issues (e.g., without processing at the digital twin environment 17 level) and provide summary information to the digital twin environment 17.

In an embodiment, the processor of the computing entity 22 issues further premise messages 33 to the computing entity 21-N (e.g., and others) to include the prescriptive information for storage in the AI memory 59 of the computing entity 21-N and others. In the embodiment, the computing entity 21-N issues environment signaling 30 to a device associated the object 24-1 to facilitate commands when the prescriptive information includes adaptations to abate issues and improve results.

The method described above in conjunction with a processing module of any computing entity of the computing system can alternatively be performed by other specialty modules of the computing system of FIG. 1 or by other specialty devices. In addition, at least one memory section that is non-transitory (e.g., a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing entities of the computing system, cause one or more computing devices of the computing system to perform any or all of the method steps described above.

FIGS. 7A-7B are schematic block diagrams of another embodiment of a computing system illustrating an example of a self-forming communication and control system. The computing system includes the object 24-1 of FIG. 1, the object 24-2 of FIG. 1, the computing entity 21-1 of FIG. 1, and the computing entity 21-2 of FIG. 1. The objects 24-1 and 24-2 each include a lighting controller with a unique lighting controller identifier (ID) and a light controlled by the lighting controller.

FIG. 7A illustrates an example of operation of a method for processing data of the self-forming communication and control system where a first step includes the processor of the computing entity 21-1 executing environment interpretation software from a first memory causing the processor to detect a set of lighting objects of an environment based on at least one of environment signaling 30 of the environment and premise messages 33 exchanged with another processor (e.g., of computing entity 21-2) to produce a set of detected lighting objects. Each lighting object of the set of lighting objects includes an illumination component (e.g., a set of lights) and a lighting control component (e.g., a lighting controller).

For example, the processor interprets output of visual sensor 122 that portrays unique patterns of light from lights within visual range of the visual sensor 122 (e.g., encoded unique identifier that correlates to identifier information of the premise messages 33, simple timed flash pattern, etc.) as the environment signaling 30 from each of objects 24-1 and 24-2 to detect the lighting objects. As another example, the processor interprets premise messages 33 via the wireless communication modem 86 to identify lighting controllers common to a premise as the set of detected lighting objects. Some lighting controllers may not be in visual range of the visual sensor 122.

Having produced the set of detected lighting objects for the premise of the computing entity 21-1, a second step of the example method of operation includes the processor establishing lighting observations. The establishing includes a series of sub-steps. A first sub-step includes the processor executing instruction generation software from a second memory to facilitate intercommunication between the environment interpretation software and the instruction generation software to generate processor-executable instructions for use by the processor to subsequently monitor at least one of further environment signaling of the environment and further premise messages associated with the set of detected lighting objects. The instructions pertain to observing the lighting of the set of detected lighting objects within the environment in a format ready to use by the processor such as one or more of timestamping when, if possible, a particular light is on or off, when some light is on or off and what relative level of illumination.

The generation of the instructions includes a variety of approaches. Example approaches include extracting instructions from the AI memory based on object profile information for the set of detected lighting objects, generating new instructions based on the object profile information and previous instructions from the AI memory for similar objects, and decoding the instructions from at least one of the environment signaling 30 and the premise messages 33.

A second sub-step of the second step includes the processor executing object monitor software from a third memory to facilitate intercommunication between the instruction generation software and the object monitor software to store object monitor information for the set of detected lighting objects within a digital twin memory by applying the processor-executable instructions to the at least one of the further environment signaling of the environment and the further premise messages associated with the set of detected lighting objects. The object monitor information includes observations of the lights with regards to on/off times and the illumination levels in accordance with the instructions.

For example, the processor interprets the further environment signaling utilizing the processor-executable instructions to produce the object monitor information for storage in the twin memory 58, indexed by at least one of time, object type, object identifier (ID), premise ID, lights on/off, illumination levels, lighting types, lighting locations in the premise, or any other feasible index to facilitate subsequent generation of dashboard information and/or configuration information.

FIG. 7B further illustrates the example of operation of the method for processing data of the self-forming communication and control system where a third step includes the processor of the computing entity executing configuration software from a fourth memory to facilitate intercommunication between the object monitor software and the configuration software interpret a portion of the object monitor information for the set of detected lighting objects from the digital twin memory to produce as-is configuration information for the set of detected lighting objects. The as-is configuration information includes spatial placement information of at least some of the set of detected lighting objects, such as absolute locations in the premise and relative locations with reference to any other object or device in the premise. A technological improvement is provided in this auto-detection over determining or manually entering the as-is configuration. Subsequent modifications to the configuration can be automatically detected.

The processor produces the as-is configuration information utilizing a variety of approaches including selecting the portion of the object monitor information based on detecting a lighting anomaly (e.g., error, user input, improper illumination, etc.) and interpreting the selected portion of the object monitor information recovered from the twin memory 58 utilizing the object profile information (e.g., typical lighting deployments, etc.) to produce the as-is configuration information. The processor stores the as-is configuration information in the digital twin memory 58 for subsequent utilization including providing a graphic output to a user interface output device, processing the as-is configuration information by an AI processor to provide a high level summary and/or to produce prescriptive information (e.g., general lighting control, corrective actions to abate the anomaly), and sending the as-is configuration information to another computing entity.

Having produced the as-is configuration information, a fourth step of the example method operation includes the processor executing lighting control software from a fifth memory to facilitate intercommunication between the configuration software and the lighting control software to facilitate control of at least some of the set of detected lighting objects utilizing the as-is configuration information. The processor facilitates the control to achieve a higher level of energy efficiency as compared to control without the as-is configuration information.

The processor facilitates the control utilizing a variety of approaches including selecting the portion of the as-is configuration information based on detecting a lighting anomaly and interpreting the selected portion of the as-is configuration information recovered from the twin memory 58 utilizing the object profile information (e.g., what control aspects may abate the anomaly) to produce environment signaling 30 that includes a lighting command for a lighting controller. As another example, the processor issues premise message 33 to the computing entity 21-2 to facilitate the processor of computing entity 21-2 issuing environment signaling 33 to object 24-2 to control the light associated with object 24-2. As yet another example, the processor facilitates the control to shape a light pattern based in a user request. As a still further example, the processor facilitates the control to determine where a lighting object should be installed, moved, or decommissioned.

The method described above in conjunction with a processing module of any computing entity of the computing system can alternatively be performed by other specialty modules of the computing system of FIG. 1 or by other specialty devices. In addition, at least one memory section that is non-transitory (e.g., a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing entities of the computing system, cause one or more computing devices of the computing system to perform any or all of the method steps described above.

It is noted that terminologies as may be used herein such as bit stream, stream, signal sequence, etc. (or their equivalents) have been used interchangeably to describe digital information whose content corresponds to any of a number of desired types (e.g., data, video, speech, text, graphics, audio, etc. any of which may generally be referred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. For some industries, an industry-accepted tolerance is less than one percent and, for other industries, the industry-accepted tolerance is 10 percent or more. Other examples of industry-accepted tolerance range from less than one percent to fifty percent. Industry-accepted tolerances correspond to, but are not limited to, component values, integrated circuit process variations, temperature variations, rise and fall times, thermal noise, dimensions, signaling errors, dropped packets, temperatures, pressures, material compositions, and/or performance metrics. Within an industry, tolerance variances of accepted tolerances may be more or less than a percentage level (e.g., dimension tolerance of less than +/−1%). Some relativity between items may range from a difference of less than a percentage level to a few percent. Other relativity between items may range from a difference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for an example of indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operable to”, “coupled to”, or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item.