Patent Publication Number: US-2021173866-A1

Title: Transport sound profile

Description:
TECHNICAL FIELD 
     This application generally relates to building a sound profile from a media segment related to a vehicle impact, and more particularly, to a transport sound profile. 
     BACKGROUND 
     Transports, such as cars, motorcycles, trucks, planes, trains, etc., generally provide transportation needs to occupants and/or goods in a variety of ways. Functions related to transports may be identified and utilized by various computing devices, such as a smartphone or a computer. 
     Evidence and data for vehicular accidents takes much time to gather and process. In many cases, trained accident forensics investigators must be physically at the accident scene and personally gather evidence over hours. What is needed is a process to combine existing media capture devices and technologies in transports and with almost all pedestrians. Video and audio, along with other forms of data, are provided to a computing device that processes the media and creates immediately usable content for requiring organizations, The present application discloses obtaining media both preceding and immediately following a vehicular accident. The media is transferred to a computing device, and a sound profile is constructed from the media. The sound profile includes analysis that may be useful to first responders, law enforcement, or insurance providers. 
     SUMMARY 
     One example embodiment provides a method that includes one or more of associating a vehicle with an impact, saving media captured before and after the impact as a media segment, and transmitting, by a computing device associated with the vehicle, the media segment to another computing device, and building a sound profile from the media segment. 
     Another example embodiment provides a vehicle that includes a processor and a memory, coupled to the processor. The memory includes instructions that when executed by the processor are configured to perform one or more of detect an impact, save media captured before and after the impact as a media segment, transmit, by a computing device associated with the vehicle, the media segment to another computing device and build a sound profile from the media segment. 
     A further example embodiment provides a non-transitory computer readable medium comprising instructions, that when read by a processor, cause the processor to perform one or more of associating a vehicle with an impact, saving media captured before and after the impact as a media segment, transmitting, by a computing device associated with the vehicle, the media segment to another computing device, and building a sound profile from the media segment. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1A  is a diagram illustrating obtaining sounds from a transport in an impact, according to example embodiments. 
         FIG. 1B  is a diagram illustrating obtaining multimedia content from devices within geolocation boundaries, according to example embodiments. 
         FIG. 1C  is a diagram illustrating capturing media from transports within a geofence following a dangerous driving situation, according to example embodiments. 
         FIG. 1D  is a diagram illustrating building a sound profile from a media segment corresponding to a transport impact, according to example embodiments. 
         FIG. 2A  illustrates a transport network diagram, according to example embodiments. 
         FIG. 2B  illustrates another transport network diagram, according to example embodiments. 
         FIG. 2C  illustrates yet another transport network diagram, according to example embodiments. 
         FIG. 2D  illustrates a further transport network diagram, according to example embodiments. 
         FIG. 2E  illustrates a yet further transport network diagram, according to example embodiments. 
         FIG. 2F  illustrates a yet further transport network diagram, according to example embodiments. 
         FIG. 3A  illustrates a flow diagram, according to example embodiments. 
         FIG. 3B  illustrates another flow diagram, according to example embodiments. 
         FIG. 3C  illustrates yet another flow diagram, according to example embodiments. 
         FIG. 3D  illustrates yet another flow diagram, according to example embodiments. 
         FIG. 4  illustrates a machine learning transport network diagram, according to example embodiments. 
         FIG. 5A  illustrates an example transport configuration for managing database transactions associated with a transport, according to example embodiments. 
         FIG. 5B  illustrates another example transport configuration for managing database transactions conducted among various transports, according to example embodiments. 
         FIG. 6A  illustrates a blockchain architecture configuration, according to example embodiments. 
         FIG. 6B  illustrates another blockchain configuration, according to example embodiments. 
         FIG. 6C  illustrates a blockchain configuration for storing blockchain transaction data, according to example embodiments. 
         FIG. 6D  illustrates example data blocks, according to example embodiments. 
         FIG. 7  illustrates an example system that supports one or more of the example embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     It will be readily understood that the instant components, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of at least one of a method, apparatus, non-transitory computer readable medium and system, as represented in the attached figures, is not intended to limit the scope of the application as claimed but is merely representative of selected embodiments. 
     The instant features, structures, or characteristics as described throughout this specification may be combined in any suitable manner in one or more embodiments. For example, the usage of the phrases “example embodiments”, “some embodiments”, or other similar language, throughout least this specification refers to the fact that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at one embodiment. Thus, appearances of the phrases “example embodiments”, “in some embodiments”, “in other embodiments”, or other similar language, throughout this specification do not necessarily all refer to the same group of embodiments, and the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the diagrams, any connection between elements can permit one-way and/or two-way communication even if the depicted connection is a one-way or two-way arrow. In the current application, a transport may include one or more of vehicles, cars, trucks, motorcycles, scooters, bicycles, boats, recreational transports, planes, and any object that may be used to transport people and or goods from one location to another. 
     In addition, while the term “message” may have been used in the description of embodiments, the application may be applied to many types of network data, such as, a packet, frame, datagram, etc. The term “message” also includes packet, frame, datagram, and any equivalents thereof. Furthermore, while certain types of messages and signaling may be depicted in exemplary embodiments they are not limited to a certain type of message, and the application is not limited to a certain type of signaling. 
     Example embodiments provide methods, systems, components, non-transitory computer readable media, devices, and/or networks, which provide at least one of: a transport (also referred to as a transport herein) a data collection system, a data monitoring system, a verification system, an authorization system and a transport data distribution system. The transport status condition data, received in the form of communication update messages, such as wireless data network communications and/or wired communication messages, may be received and processed to identify transport/transport status conditions and provide feedback as to the condition changes of a transport. In one example, a user profile may be applied to a particular transport/transport to authorize a current transport event, service stops at service stations, and to authorize subsequent transport rental services. 
     Within the communication infrastructure, a decentralized database is a distributed storage system which includes multiple nodes that communicate with each other. A blockchain is an example of a decentralized database which includes an append-only immutable data structure (i.e. a distributed ledger) capable of maintaining records between untrusted parties. The untrusted parties are referred to herein as peers, nodes or peer nodes. Each peer maintains a copy of the database records and no single peer can modify the database records without a consensus being reached among the distributed peers. For example, the peers may execute a consensus protocol to validate blockchain storage entries, group the storage entries into blocks, and build a hash chain via the blocks. This process forms the ledger by ordering the storage entries, as is necessary, for consistency. In a public or permissionless blockchain, anyone can participate without a specific identity. Public blockchains can involve cryptocurrencies and use consensus based on various protocols such as proof of work (PoW). On the other hand, a permissioned blockchain database provides a system which can secure interactions among a group of entities which share a common goal, but which do not or cannot fully trust one another, such as businesses that exchange funds, goods, information, and the like. The instant application can function in a permissioned and/or a permissionless blockchain setting. 
     Smart contracts are trusted distributed applications which leverage tamper-proof properties of the shared or distributed ledger (i.e., which may be in the form of a blockchain) database and an underlying agreement between member nodes which is referred to as an endorsement or endorsement policy. In general, blockchain entries are “endorsed” before being committed to the blockchain while entries which are not endorsed are disregarded. A typical endorsement policy allows smart contract executable code to specify endorsers for an entry in the form of a set of peer nodes that are necessary for endorsement. When a client sends the entry to the peers specified in the endorsement policy, the entry is executed to validate the entry. After validation, the entries enter an ordering phase in which a consensus protocol is used to produce an ordered sequence of endorsed entries grouped into blocks. 
     Nodes are the communication entities of the blockchain system. A “node” may perform a logical function in the sense that multiple nodes of different types can run on the same physical server. Nodes are grouped in trust domains and are associated with logical entities that control them in various ways. Nodes may include different types, such as a client or submitting-client node which submits an entry-invocation to an endorser (e.g., peer), and broadcasts entry-proposals to an ordering service (e.g., ordering node). Another type of node is a peer node which can receive client submitted entries, commit the entries and maintain a state and a copy of the ledger of blockchain entries. Peers can also have the role of an endorser, although it is not a requirement. An ordering-service-node or orderer is a node running the communication service for all nodes, and which implements a delivery guarantee, such as a broadcast to each of the peer nodes in the system when committing entries and modifying a world state of the blockchain, which is another name for the initial blockchain entry which normally includes control and setup information. 
     A ledger is a sequenced, tamper-resistant record of all state transitions of a blockchain. State transitions may result from smart contract executable code invocations (i.e., entries) submitted by participating parties (e.g., client nodes, ordering nodes, endorser nodes, peer nodes, etc.). An entry may result in a set of asset key-value pairs being committed to the ledger as one or more operands, such as creates, updates, deletes, and the like. The ledger includes a blockchain (also referred to as a chain) which is used to store an immutable, sequenced record in blocks. The ledger also includes a state database which maintains a current state of the blockchain. There is typically one ledger per channel. Each peer node maintains a copy of the ledger for each channel of which they are a member. 
     A chain is an entry log which is structured as hash-linked blocks, and each block contains a sequence of N entries where N is equal to or greater than one. The block header includes a hash of the block&#39;s entries, as well as a hash of the prior block&#39;s header. In this way, all entries on the ledger may be sequenced and cryptographically linked together. Accordingly, it is not possible to tamper with the ledger data without breaking the hash links. A hash of a most recently added blockchain block represents every entry on the chain that has come before it, making it possible to ensure that all peer nodes are in a consistent and trusted state. The chain may be stored on a peer node file system (i.e., local, attached storage, cloud, etc.), efficiently supporting the append-only nature of the blockchain workload. 
     The current state of the immutable ledger represents the latest values for all keys that are included in the chain entry log. Because the current state represents the latest key values known to a channel, it is sometimes referred to as a world state. Smart contract executable code invocations execute entries against the current state data of the ledger. To make these smart contract executable code interactions efficient, the latest values of the keys may be stored in a state database. The state database may be simply an indexed view into the chain&#39;s entry log, it can therefore be regenerated from the chain at any time. The state database may automatically be recovered (or generated if needed) upon peer node startup, and before entries are accepted. 
     A blockchain is different from a traditional database in that the blockchain is not a central storage but rather a decentralized, immutable, and secure storage, where nodes must share in changes to records in the storage. Some properties that are inherent in blockchain and which help implement the blockchain include, but are not limited to, an immutable ledger, smart contracts, security, privacy, decentralization, consensus, endorsement, accessibility, and the like. 
     Example embodiments provide a way for providing a transport service to a particular transport and/or requesting user associated with a user profile that is applied to the transport. For example, a user may be the owner of a transport or the operator of a transport owned by another party. The transport may require service at certain intervals and the service needs may require authorization prior to permitting the services to be received. Also, service centers may offer services to transports in a nearby area based on the transport&#39;s current route plan and a relative level of service requirements (e.g., immediate, severe, intermediate, minor, etc.). The transport needs may be monitored via one or more sensors which report sensed data to a central controller computer device in the transport, which in turn, is forwarded to a management server for review and action. 
     A sensor may be located on one or more of the interior of the transport, the exterior of the transport, on a fixed object apart from the transport, and on another transport near to the transport. The sensor may also be associated with the transport&#39;s speed, the transport&#39;s braking, the transport&#39;s acceleration, fuel levels, service needs, the gear-shifting of the transport, the transport&#39;s steering, and the like. The notion of a sensor may also be a device, such as a mobile device. Also, sensor information may be used to identify whether the transport is operating safely and whether the occupant user has engaged in any unexpected transport conditions, such as during the transport access period. Transport information collected before, during and/or after a transport&#39;s operation may be identified and stored in a transaction on a shared/distributed ledger, which may be generated and committed to the immutable ledger as determined by a permission granting consortium, and thus in a “decentralized” manner, such as via a blockchain membership group. 
     Each interested party (i.e., company, agency, etc.) may want to limit the exposure of private information, and therefore the blockchain and its immutability can limit the exposure and manage permissions for each particular user transport profile. A smart contract may be used to provide compensation, quantify a user profile score/rating/review, apply transport event permissions, determine when service is needed, identify a collision and/or degradation event, identify a safety concern event, identify parties to the event and provide distribution to registered entities seeking access to such transport event data. Also, the results may be identified, and the necessary information can be shared among the registered companies and/or individuals based on a “consensus” approach associated with the blockchain. Such an approach could not be implemented on a traditional centralized database. 
     Every autonomous driving system is built on a whole suite of software and an array of sensors. Machine learning, lidar projectors, radar, and ultrasonic sensors all work together to create a living map of the world that a self-driving car can navigate. Most companies in the race to full autonomy are relying on the same basic technological foundations of lidar+radar+cameras+ultrasonic, with a few notable exceptions. 
     In another embodiment, GPS, maps and other cameras and sensors are used in autonomous transports without lidar as lidar is often viewed as being expensive and unnecessary. Researchers have determined that stereo cameras are a low-cost alternative to the more expensive lidar functionality. 
     The instant application includes, in certain embodiments, authorizing a transport for service via an automated and quick authentication scheme. For example, driving up to a charging station or fuel pump may be performed by a transport operator and the authorization to receive charge or fuel may be performed without any delays provided the authorization is received by the service station. A transport may provide a communication signal that provides an identification of a transport that has a currently active profile linked to an account that is authorized to accept a service which can be later rectified by compensation. Additional measures may be used to provide further authentication, such as another identifier may be sent from the user&#39;s device wirelessly to the service center to replace or supplement the first authorization effort between the transport and the service center with an additional authorization effort. 
     Data shared and received may be stored in a database, which maintains data in one single database (e.g., database server) and generally at one particular location. This location is often a central computer, for example, a desktop central processing unit (CPU), a server CPU, or a mainframe computer. Information stored on a centralized database is typically accessible from multiple different points. A centralized database is easy to manage, maintain, and control, especially for purposes of security because of its single location. Within a centralized database, data redundancy is minimized as a single storing place of all data also implies that a given set of data only has one primary record. 
       FIG. 1A  is a diagram illustrating obtaining sounds from a transport in an impact  100 , according to example embodiments. Transports or  104  may sometimes be involved in an impact incident. Impacts may be with a stationary object as shown, such as a light pole, a building, or various permanent or temporary structures. Impacts may also be with one or more other transports  108 . In some cases, instead of a direct impact or collision with a stationary object or another transport  104 , the embodiments associated with  FIG. 1A  may also be associated with a near-impact or a dangerous driving situation. Examples of a near-impact or a dangerous driving situation may include hard breaking, swerving, weaving in traffic, driving into and/or across lanes, driving off road, tailgating, not stopping at stop signs, running red lights, or any other similar type of event. 
     Later model transports  104  include various computers, communication devices, and sensors. These resources collectively provide navigation functions, hands-free communication, parking assistance, collision detection, and monitoring of nearby other transports  108  in order to provide more information and convenience to passengers and reduce the opportunity for impacts or accidents. These computers, communication devices, and sensors may communicate with other computers, communication devices, and sensors either within or outside of the transport  104  through various technologies such as a transport&#39;s Controller Area Network (CAN) bus, BLUETOOTH, WIFI, or the like. 
     Transports  104  and other transports  108  may include any type of self-propelled conveyance, including cars, motorcycles, trucks, construction equipment, or even local single passenger transports  104 ,  108  such as SEGWAYs or similar devices. Transports  104 ,  108  may have a human driver or be a driverless transport, and may or may not have any passengers. Transports  104 ,  108  may include cargo transports including delivery vans, mail delivery transports, and unmanned package delivery drones. 
     Transports  104  may include one or more front, rear, side, or dash-mounted devices such as cameras to capture and display video to the driver, and possibly to transmit outside the transport  104 . Transports  104  may also include microphones to capture audio both inside the cabin as well as outside as well. Such audio may be used to communicate with outside driver assistance services such as ONSTAR or similar. In some cases, the audio may accompany video from one or more onboard cameras. 
     Transports  104  often include hand-free wireless interfaces in order to more safely utilize mobile communication devices such as cell phones, smart phones, or various tablets and other communication devices. These interfaces may operate with embedded applications such as CARPLAY to replicate mobile device applications and functionality with transport  104  entertainment, communication, or computing devices. 
     With transports  104  now able to communicate more globally with outside services and communication providers, it may be beneficial to identify individual transports  104  in order to distinguish from other transports  108 . Thus, an identifier may be permanently stored or assigned to each transport  104 , and the identifier may accompany any rich media content  112  (i.e. any combination of static images, video, audio, sensor data, etc) transmitted from the transport  104 . Sensor data may include radar data, sonar data, magnetic detector data, optical sensor data, laser rangefinder data, or any other form of data produced by sensors associated with the transport  104 ,  108 . 
     When a transport  104  is involved with an impact or accident, as previously discussed, the transport  104  is associated with the impact. In one embodiment, an event is created within an onboard computer of transport  104 , and the transport  104  identifier is associated with the event. In one embodiment, the onboard computer may include a memory device to store the event and associated identifier. In one embodiment, the impact or accident is in proximity to one or more other transports  108 . Proximity may be determined by several means, including a distance from a location, a distance from a GPS coordinate, a street intersection, a line of sight, a hearing limit, a same street, or a street address range. In one embodiment, the proximity may be associated with a distance from the impact defined by a geofence. Within proximity of the impact, there may be any number of other transports  108  and any number of devices  120 , such as devices associated with an individual. Devices  120  may include any type of communication devices, including cell phones, smart phones, tablets, smart watches, wearable computers, or implantable computers. Devices  120  may be in the possession of a pedestrian, bicycle rider, transport  104 ,  108  driver, or transport  104 ,  108  passenger. Device  120  may also be a static device within proximity of the impact, including traffic cameras, business video cameras, or aerial drone-mounted cameras. Devices  120  may also include inherent (i.e. part of the transport  104 ,  108 ) communication devices not associated with or in the possession of any individual. 
     When an impact or accident occurs, a device  120  in proximity to the impact transmits media and/or data  112  related to the impact to a server  116 . The media  112  may include any combination of an audio file, a video file, a text file, an image file, transport telemetry, environmental data, traffic data, or sensor data. In one embodiment, the media  112  may also include a direction for each of the sounds. 
     In one embodiment, a transport  104  associated with the impact may transmit the media  112 . In another embodiment, another transport  108  not associated with the impact may transmit the media  112 . In another embodiment, a device  120  associated with a bystander or pedestrian in proximity to the impact may transmit the media  112 . In yet another embodiment, a device  120  associated with a passenger of the transport  104  associated with the impact or a device  120  associated with a passenger of another transport  108  not associated with the impact may transmit the media  112 . In yet another embodiment, a device  120  associated with static camera or sensor in proximity to the impact or accident may transmit the media  112 . 
     The server  116  may be located anywhere, including in proximity to the impact or accident or outside the proximity to the impact or accident. The server  116  receives the media  112  over any type of data connection, but most likely through wireless connections, including but not limited to cellular connections (i.e. 3G, 4G, 5G, LTE), internet or broadband connections, WIFI connections, or the like. The server  116  may include one or more applications in a memory  706 , which may determine or more sounds based on the media  112 . Each of the sounds may include an identification of a type of sound source (e.g. an automotive noise of a transport  104 , an automotive noise of another transport  108 , an impact sound associated with the transport  104 , a human voice or exclamation, a warning alarm, a skidding transport  104 ,  108 , or any other type of detected sound), a time stamp associated with a sound, a sound level or volume of the sound, a sound duration of time, and an indication of association with a different sound (e.g. a passenger voice of a passenger within the transport  104 ). In one embodiment, determining one or more sounds based on the media  112  may include time stamping one or more of a start time and an end time for each sound and identifying a sound source for each of the one or more sounds. The sounds may have been recorded by the device  120  within a first predetermined time before the impact or accident and a second predetermined time after the impact or accident. 
     Determining the one or more sounds based on the media  112  may in some embodiments result in a file created by the server  116  with a group of parameters (identifiers, time stamps, etc as previously discussed). In some embodiments, the file may be transmitted by the server  116  or stored in a database (not shown). The server  116  may include one or more applications that perform speech recognition on sounds identified as voices. In one embodiment, the speech recognition application may determine a context based on recognized speech, where the context may include a location, a threat, a cause of the impact or accident, a fire or explosion, an injury or medical status, a name, an action, a controlled substance, a hazardous material, a crime, or actual or implied violence. In one embodiment, law enforcement may be notified if the context is applicable to law enforcement. In one embodiment, EMS or a fire department may be notified if the context is applicable to a medical condition, a fire, a hazardous material, or an explosion. In one embodiment, an insurance provider may be notified if the context is applicable to a cause of the impact or accident. The media  112  may include any number of videos. In one embodiment, the server  116  determines a number of videos based on the received media  112  and synchronizes sounds with the videos. This helps to establish more context for improved understanding by combining different but related media types  112 . 
     Once the server  116  has determined one or more sounds based on the media  112 , the server  116  may associate sounds (from devices  120  in proximity to the impact or accident) with the transport  104  involved in the impact or accident or another transport  108 . By performing this association, the server  116  creates a data-driven narrative of the impact event that helps describe the roles and contribution to the impact by the transports  104 ,  108 . It should be noted that any of the actions taken with respect to an impact or accident herein apply equally to both near-impacts as well as dangerous driving situations. 
       FIG. 1B  is a diagram illustrating obtaining multimedia content from devices within geolocation boundaries  130 , according to example embodiments. Transports  104  may sometimes be involved in an impact incident or collision. It is advantageous to gather relevant data quickly after an impact or accident. Relevant data is generally local to the impact or accident, where “local” may be defined in different ways. Data that is not local to the impact or accident may be considered as less accurate or possibly misleading—and therefore not helpful to establishing facts and evidence. 
     An impact involving one or more transports  104 , such as an impact between a first transport  104 A and a second transport  104 B, always produces a generally loud sound or series of sounds as a direct result of the collision. The sound level is generally measured in decibels (dB), in one example. From a point at which a sound is produced, the sound level decreases generally in proportion to distance from the sound source. That may be true for open and unimpeded areas, but is generally not true for complex environments such as cities with many buildings and structures, other transports  108 , and other sound sources of varying volume. For example, a downtown intersection in a major city at noontime may have various construction noises, trains, traffic sounds, horns, voices, and other sounds emanating from different points and unpredictably changing from moment to moment. Because of other sounds that may be occurring at or near the same time as the impact, the detection range of the impact or accident may change based on where the other sounds are sourced from. That is, from a first direction, the detection range may be correspondingly short if there are other sound sources nearby. From a second direction, the detection range may be correspondingly long if there are not other sound sources nearby. From this, it is possible to define a geolocation boundary  134  for the impact based on decibel levels associated with the impact. Impacts may be with one or more other transports  108 , as shown, or with a stationary object such as a light pole, a building, or various permanent or temporary structures. In some cases, instead of a direct impact or collision with a stationary object or another transport  104 , the embodiments associated with  FIG. 1B  may also be associated with a near-impact or a dangerous driving situation. Examples of a near-impact or a dangerous driving situation may include hard breaking, swerving, weaving in traffic, driving into and/or across lanes, driving off road, tailgating, not stopping at stop signs, running red lights, or any other similar type of event. 
     Within the geolocation boundary  134 , there may be one or more other transports  108 , a second transport  104 B involved in the impact, and various communication devices  138 A,  138 B. For example, a first pedestrian within the geolocation boundary  134  may use a first communication device  138 A to capture multimedia content  142  of the impact or accident, including any involved transports  104 A/ 104 B. The first pedestrian may also transmit the captured multimedia content  142  to a second communication device  138 B within the geolocation boundary, as well as to other communication devices  138 B associated with a transport  104  involved in the impact and other transports  108  not directly involved in the impact or accident. Other transports outside the geolocation boundary  146  would not receive the captured multimedia content  142  from the first communication device  138 A. 
     In some embodiments, the communication device  138  may be part of either a transport  104  involved in the impact, another transport within the geolocation boundary  108 , or with a passenger of either the transport  104  involved in the impact or another transport within the geolocation boundary  108 . 
     The captured multimedia content  142  may include any combination of audio file(s), video file(s), text file(s), image file(s), transport telemetry, environmental data, traffic data, and sensor data. The captured multimedia content  142  may be transmitted from the communication device within the geolocation boundary  138 . In one embodiment, a transport involved in the impact or accident  104 A,  104 B may transmit sensor data to the communication device  138 A, which then includes the sensor data in the captured multimedia content  142 . Sensor data may include radar data, sonar data, magnetic detector data, optical sensor data, laser rangefinder data, or any other form of data produced by sensors associated with the transport  104 ,  108 . The captured multimedia content  142  may include one or more sounds and one or more videos. In one embodiment, when communication devices  138 B receive the captured multimedia content, they include one or more applications that synchronize the one or more sounds with the one or more videos. 
     With transports  104  now able to communicate more globally with outside services and communication providers, it may be beneficial to identify individual transports  104  in order to distinguish from other transports  108 . Thus, an identifier may be permanently stored or assigned to each transport  104 , and the identifier may accompany any rich media content  112 . When a transport  104  is involved with an impact or accident, as previously discussed, the transport  104  is associated with the impact. In one embodiment, an event may be created within an onboard computer of transport  104 , and the transport  104  identifier may be associated with the event. In one embodiment, the onboard computer may include a memory device to store the event and associated identifier. In another embodiment, the identifier may be transmitted to the communication device  142 , which may include the identifier in the captured multimedia content  142 . 
     In one embodiment, the captured multimedia content  142  may also be transmitted to a server  116  outside the geolocation boundary  134 . The captured multimedia content  142  may also include one or more sounds associated with the impact and a direction for each of the sounds. The server  116  may determine a number of videos based on the received multimedia content  142 , and associate one or more of the sounds with the videos. The communication device  138 A or the server  116  may time stamp the multimedia content with one or more of a start time and an end time for each sound and identify a sound source for each of the sounds. 
     In one embodiment, one or more of the communication devices  138 A,  138 B transmits the captured multimedia content  142  to a cloud server  116 , which may store the multimedia content  142  to cloud storage. The server  116  may be located anywhere, including in proximity to the impact or accident or outside the proximity to the impact or accident. The server  116  receives the multimedia content  142  over any type of data connection, but most likely through wireless connections, including but not limited to cellular connections (i.e. 3G, 4G, 5G, LTE), internet or broadband connections, or WIFI connections. The server  116  may include one or more applications in a memory  706 , which may determine or more sounds based on the multimedia content  142 . Each of the sounds may include an identification of a type of sound source (e.g. an automotive noise of a transport  104 A,  104 B, an automotive noise of another transport  108 , an impact sound associated with the transport  104 A,  104 B, a human voice or exclamation, a warning alarm, a skidding transport  104 ,  108 , or any other type of detected sound), a time stamp associated with a sound, a sound level or volume of the sound, a sound duration of time, and an indication of association with a different sound (e.g. a passenger voice of a passenger within the transport  104 ). 
     Determining the one or more sounds based on the multimedia content  142  may in some embodiments result in a file created by the server  116  with a group of parameters (identifiers, time stamps, etc as previously discussed). In some embodiments, the file may be transmitted by the server  116  or stored in a database (not shown). The server  116  may include one or more applications that perform speech recognition on sounds identified as voices. In one embodiment, the speech recognition application may determine a context based on recognized speech, where the context may include a location, a threat, a cause of the impact or accident, a fire or explosion, an injury or medical status, a name, an action, a controlled substance, a hazardous material, a crime, or actual or implied violence. In one embodiment, law enforcement may be notified if the context is applicable to law enforcement. In one embodiment, EMS or a fire department may be notified if the context is applicable to a medical condition, a fire, a hazardous material, or an explosion. In one embodiment, an insurance provider may be notified if the context is applicable to a cause of the impact or accident. It should be noted that any of the actions taken with respect to an impact or accident herein apply equally to both near-impacts as well as dangerous driving situations. 
       FIG. 1C  is a diagram illustrating capturing media from vehicles within a geofence following a dangerous driving situation  150 , according to example embodiments. A dangerous driving situation is an event involving transport  104  that does not necessarily end in an impact or accident. For example, driving too fast for local conditions, driving an unsafe transport (weak/no brakes or almost flat tire(s)), driving intoxicated (drugs or alcohol), or swerving outside marked road lanes are all examples of dangerous driving situations. 
     Transports  104 ,  108  are able to detect proximity to close transports, and provide audible and/or visual warnings to a driver when reversing, changing lanes, or approaching another transport quickly with insufficient braking, Transports  104 ,  108  are also able to identify many dangerous driving situations. For example, a transport  104  may detect a driver is steering erratically, speeding, changing gears dangerously, or applying insufficient or too sudden braking. This may be due to an intoxicated driver under the influence or a driver experiencing a medical condition affecting driving. 
     In response to identifying the dangerous driving situation, the transport  104  may capture first media. The first media  162  may include any combination of an audio file, a video file, a text file, an image file, transport telemetry, environmental data, traffic data, or sensor data.  FIG. 1C  illustrates a vehicle involved in a dangerous driving situation  104  that captures first media  162  from four sources: a front camera  162 A, a rear camera  162 B, a left-side camera  162 C, and a right-side camera  162 D. In other embodiments, the first media  162  may include audio and various forms of vehicular and sensor data in addition to or instead of camera video from one or more camera sources. In one embodiment, the media  162  may also include one or more forms of audio and a direction for each of the sounds. 
     Next, the transport  104  involved in the dangerous driving situation establishes a geofence  134  based on the dangerous driving situation. In one embodiment, the geofence is within a predetermined distance  154  of the transport  104  involved in the dangerous driving situation. Within the geofence boundaries may be one or more other transports  108 , various pedestrians with communication devices, passengers with communication devices, or static communication devices associated with traffic control or local buildings. 
     In one embodiment, another transport  108  captures second media  158 , as long as the other transport  108  is within the geofence. The second media  158  includes content of the transport  104  involved in the dangerous driving situation. The second media  158  may include any combination of an audio file, a video file, a text file, an image file, transport telemetry, environmental data, traffic data, or sensor data.  FIG. 1C  illustrates another vehicle within the geofence  108  that captures second media  158  through a front camera, of the rear quarter of a swerving transport  104 . The first  162  and second  158  media are captured by one or more devices associated with the vehicle  104 , another vehicle  108 , an occupant of the vehicle  104 , or an occupant of the one or more other vehicles  108 . 
     Once the first  162  and the second  158  media have been captured, in one embodiment they may be transmitted to a server  116 . The server  116  correlates the first media  162  with the second media  158  to obtain a cause for the dangerous driving situation. Each of the first  162  and second  158  media also may include one or more sounds associated with the dangerous driving situation and a direction for each of the sounds. The server  116  determines a number of videos based on the received first  162  and second  158  media and synchronizes the one or more sounds with one or more videos. The server  116  may also time stamp the first  162  and second  158  media with one or more of a start time and an end time for each sound within the first  162  and second  158  media, respectively and identify a sound source for each of the one or more sounds. In some embodiments, the transport  104  and other transport  108  may additionally transmit one or more of the first  162  and second  158  captured media related to the dangerous driving situation to storage outside the geofence, where the storage outside the geofence may include cloud storage. 
       FIG. 1D  is a diagram illustrating building a sound profile from a media segment corresponding to a vehicle impact  170 , according to example embodiments. Determining the cause of an impact or accident involving a transport or transport  104  may require obtaining more data and information and just from the impact itself. While impact data or information may provide useful information as to the severity of the impact and likely damage or injuries, the preceding and following information may help to identify conditions leading up to the impact, mechanical malfunction, environmental conditions, driver distraction, a medical emergency, and passenger actions or statements following the impact. In some cases, instead of a direct impact or collision with a stationary object or another vehicle  104 , the embodiments associated with  FIG. 1D  may also be associated with a near-impact or a dangerous driving situation. Examples of a near-impact or a dangerous driving situation may include hard breaking, swerving, weaving in traffic, driving into and/or across lanes, driving off road, tailgating, not stopping at stop signs, running red lights, or any other similar type of event. 
     A transport  104  may have one or more computing devices  174  associated with the transport  104 . Some computing devices  174  may be directly associated with the transport  174  itself, such as an onboard navigation or communication system. Other computing devices  174  may be directly associated with one or more passengers or the driver of the transport  104 , such as but not limited to a cell phone, smart phone, tablet, or smart watch. In one embodiment, computing devices  174  capture and save media related to the impact or accident. In one embodiment, the transport  104  itself captures media related to the accident, transfers the media to a computing device  174  that stores the media, and the computing device  174  communicates the saved media related to the impact or accident as a media segment to a server  116 . In yet another embodiment, the transport  104  itself captures and saves media related to the accident, transfers the media to a computing device  174  that communicates the media related to the impact or accident as a media segment  178  to a server  116 . In yet another embodiment, the transport  104  itself captures the media related to the accident, transfers the media to an external computing device  174  (outside of and/or not associated with the transport  104 ), which communicates the media segment  178  to the server  116 . The media segment  178  includes media before the impact or accident, and media following the impact or accident. The media may be captured and/or stored during a first time period before the impact and/or during a second time period after the impact. The media may include any combination of one or more audio files, video files, text files, image files, transport telemetry, environmental data, traffic data, or sensor data. The environmental data may include data related to one or more of ambient temperature, road conditions, weather, wind speed or direction, time of day, and light sources. 
     The server  116  receives the media segment  178  from the computing device  174 . In one embodiment, one or more sounds may be extracted from the media segment  178 , and the media segment  178  may also include a direction for each of the sounds. The server  116  builds a sound profile  182  from the media segment  178 . In one embodiment, the sound profile  182  may include data cataloging each sound within the media segment  178 . The data may include a unique identifier, a starting time stamp, an ending time stamp, a duration of the sound, a maximum sound level, an identification of a sound source, a direction of the sound from the transport  104 , and a distance of the sound from the transport  104 . The unique identifier may include a number of a sequence (i.e. a next available number), a description of the sound (e.g. “mechanical  1 ”, “siren  3 ”, etc.), or any other identifier that may differentiate each sound from every other sound in the media segment  178 . The media may also include one or more videos, and the sound profile  182  may associate and/or synchronize each sound with one or more videos. 
     The sound profile  182  may include analysis that may be useful to first responders  184 , law enforcement  186 , or insurance providers  188 . In one embodiment, the sound profile  182  may include identification of more ore more needed emergency services  184 , such as EMS/paramedics, a fire department, hazardous material handling, or animal services. In one embodiment, the server  116  provides all or a relevant portion of the sound profile to one or more emergency service providers  184 . In another embodiment, the sound profile  182  may include identification of various law enforcement functions, such as police or accident investigation resources. In one embodiment, the server  116  provides all or a relevant portion of the sound profile to one or more law enforcement services  186 . In another embodiment, the sound profile  182  may include identification of more ore more needed insurance providers  188 , such as insurers for the transport  104  involved in the impact or accident, insurers for other transports or transports also involved in the accident or impact, or insurers of property involved in the accident or impact. In one embodiment, the server  116  provides all or a relevant portion of the sound profile to one or more insurance providers  184 . The sound profile  182  may be associated with one or more passwords, certificates, or any other form of security in order to guard privacy or protect confidential data or media. 
     Transports  104  involved in an impact or accident may include computing devices  174  that include storage resources for emergency services  184 , law enforcement  186 , and insurance providers  188 . Just after an impact or accident, involved transports  104  may automatically transfer a notification to any stored emergency services  184 , law enforcement  186 , and/or insurance providers  188 . In one embodiment, the emergency services  184 , law enforcement  186 , and/or insurance providers  188  may contact the server  116  to obtain all or part of the sound profile  182  from the server  116 . In another embodiment, the emergency services  184 , law enforcement  186 , and/or insurance providers  188  may contact the transport  104  itself to obtain any stored media related to the impact or accident, separately from or in addition to obtaining all or part of the sound profile  182  from the server  116 . It should be noted that any of the actions taken with respect to an impact or accident herein apply equally to both near-impacts as well as dangerous driving situations. 
       FIG. 2A  illustrates a transport network diagram  200 , according to example embodiments. The network comprises elements including a transport node  202  including a processor  204 , as well as a transport node  202 ′ including a processor  204 ′. The transport nodes  202 ,  202 ′ communicate with one another via the processors  204 ,  204 ′, as well as other elements (not shown) including transceivers, transmitters, receivers, storage, sensors and other elements capable of providing communication. The communication between the transport nodes  202 ,  202 ′ can occur directly, via a private and/or a public network (not shown) or via other transport nodes and elements comprising one or more of a processor, memory, and software. Although depicted as single transport nodes and processors, a plurality of transport nodes and processors may be present. One or more of the applications, features, steps, solutions, etc., described and/or depicted herein may be utilized and/or provided by the instant elements. 
       FIG. 2B  illustrates another transport network diagram  210 , according to example embodiments. The network comprises elements including a transport node  202  including a processor  204 , as well as a transport node  202 ′ including a processor  204 ′. The transport nodes  202 ,  202 ′ communicate with one another via the processors  204 ,  204 ′, as well as other elements (not shown) including transceivers, transmitters, receivers, storage, sensors and other elements capable of providing communication. The communication between the transport nodes  202 ,  202 ′ can occur directly, via a private and/or a public network (not shown) or via other transport nodes and elements comprising one or more of a processor, memory, and software. The processors  204 ,  204 ′ can further communicate with one or more elements  230  including sensor  212 , wired device  214 , wireless device  216 , database  218 , mobile phone  220 , transport node  222 , computer  224 , I/O device  226  and voice application  228 . The processors  204 ,  204 ′ can further communicate with elements comprising one or more of a processor, memory, and software. 
     Although depicted as single transport nodes, processors and elements, a plurality of transport nodes, processors and elements may be present. Information or communication can occur to and/or from any of the processors  204 ,  204 ′ and elements  230 . For example, the mobile phone  220  may provide information to the processor  204  which may initiate the transport node  202  to take an action, may further provide the information or additional information to the processor  204 ′ which may initiate the transport node  202 ′ to take an action, may further provide the information or additional information to the mobile phone  220 , the transport node  222 , and/or the computer  224 . One or more of the applications, features, steps, solutions, etc., described and/or depicted herein may be utilized and/or provided by the instant elements. 
       FIG. 2C  illustrates another transport network diagram  240 , according to example embodiments. The network comprises elements including a transport node  202  including a processor  204  and a non-transitory computer readable medium  242 C. The processor  204  is communicably coupled to the computer readable medium  242 C and elements  230  (which were depicted in  FIG. 2B ). 
     The processor  204  performs one or more of receiving, in block  244 C, from a device  120  in proximity to an impact, media related to the impact, associating, in block  246 C, a transport  104  with the impact, the impact in proximity to one or more other transports  108 , determining, in block  248 C, one or more sounds based on the media, and associating, in block  250 C, the one or more sounds with one or more of the transport  104  and the one or more other transports  108 . 
       FIG. 2D  illustrates a further transport network diagram  260 , according to example embodiments. The network comprises elements including a transport node  202  including a processor  204  and a non-transitory computer readable medium  242 D. The processor  204  is communicably coupled to the computer readable medium  242 D and elements  230  (which were depicted in  FIG. 2B ). 
     The processor  204  performs one or more of establishing, in block  244 D, geolocation boundaries  134  based on decibels associated with an impact involving one or more transports  104 A/ 104 B, transmitting, by a communication device  138 A in block  246 D, multimedia content  142  related to the impact to one or more other communication devices  138 B within the geolocation boundaries  134 , and receiving, by the one or more other communication devices  138 B in block  248 D, the multimedia content  142 . 
       FIG. 2E  illustrates a yet further transport network diagram  270 , according to example embodiments. The network comprises elements including a transport node  202  including a processor  204  and a non-transitory computer readable medium  242 E. The processor  204  is communicably coupled to the computer readable medium  242 E and elements  230  (which were depicted in  FIG. 2B ). 
     The processor  204  performs one or more of identifying, in block  244 E, a dangerous driving situation, capturing, in block  246 E, first media  162  by a transport  104  involved in the dangerous driving situation, establishing a geofence  134 , in block  248 E, based on a distance associated with the dangerous driving situation  154 , and capturing second media  158 , in block  250 E, by one or more other transports  108  within the geofence  134 . 
     The processors and/or computer readable media may fully or partially reside in the interior or exterior of the transport nodes. The steps or features stored in the computer readable media may be fully or partially performed by any of the processors and/or elements in any order. Additionally, one or more steps or features may be added, omitted, combined, performed at a later time, etc. 
       FIG. 2F  illustrates a yet further transport network diagram  280 , according to example embodiments. The network comprises elements including a transport node  202  including a processor  204  and a non-transitory computer readable medium  242 F. The processor  204  is communicably coupled to the computer readable medium  242 F and elements  230  (which were depicted in  FIG. 2B ). 
     The processor  204  performs one or more of associating, in block  244 F, a transport  104  with an impact, saving, in block  246 F, media captured before and after the impact as a media segment  178 , transmitting, by a computing device  174  in block  248 F associated with the transport, the media segment  178  to another computing device  116 , and building, in block  250 F, a sound profile  182  from the media segment  178 . 
     The processors and/or computer readable media may fully or partially reside in the interior or exterior of the transport nodes. The steps or features stored in the computer readable media may be fully or partially performed by any of the processors and/or elements in any order. Additionally, one or more steps or features may be added, omitted, combined, performed at a later time, etc. 
       FIG. 3A  illustrates a flow diagram  300 , according to example embodiments. Referring to  FIG. 3A , a device in proximity to an impact receives media related to the impact  302 . In one embodiment, a transport  104  includes one or more accelerometers that detect rapid, unusual, and possibly instantaneous deceleration. The accelerometers may trigger initiation of video, audio, sensor, and data media capture for the transport  104 . This may be coupled with either instantaneous streaming or delayed transfer of the media to other devices  120 , which may be associated with the driver or one or more passengers of the transport  104 , or a device  120  outside the transport  104 . The media may include an identifier for the transport, as described herein. 
     A transport is associated with the impact, which is in proximity with other transports  304 . The media may include an identifier for a transport  104  involved in the impact or accident (there may be multiple transports  104 , and therefore multiple identifiers). The identifier, present within the media, associates the transport  104  with the impact. On or more other transports  108  are within proximity to the impact or accident, as previously described with respect to  FIG. 1A . Proximity may be defined in many ways, but generally is within direct line-of-sight of the impact and/or within hearing range of the impact. In one embodiment, proximity may be determined at one or more of before the impact, at the same time as the impact, or after the impact. 
     Media related to the impact is then transmitted to a server  306 . In some embodiments, the transport  104  or another transport  108  may directly transmit the media to the server  116  through a wireless connection including, but not limited to, BLUETOOTH, WIFI, a cellular connection, or a satellite connection. 
     The server determines one or more sounds, based on the media  308 . In one embodiment, the server  116  parses individual sounds from the media and creates a data structure identifying (to the extent possible) each detected sound. The data structure may include a unique identifier for each sound, an indication if the sound is associated or not associated with the impact, a start time stamp, an end time stamp, and duration for each sound, a sound level or volume, a sound source, and a type of each sound. For sounds identified as speech or a human exclamation, a voice recognition software application in the server  116  may determine text from the recognized speech, and context for the text. In one embodiment, if the context suggests or requests assistance in any way, the server may provide a notification to an appropriate service provider through email, a text message, a voice call, or any other communication method. 
     Finally, the server associates the sounds with the transport and the other transports  310 . The analysis performed by the server  116  attempts to identify each sound in the received media. As part of this analysis, the source of each sound is identified, if possible—including sounds associated with the transport  104  and each of the other transports  108 . If a specific transport  104 ,  108  may not be determined directly, volume and direction information may be extracted from the sounds, which may then indicate a specific transport  104 ,  108 . In one embodiment, the server  116  receives media and data  112  from multiple sources, possibly including a transport  104  involved in the impact, one or more other transports  108 , and one or more other devices  120 . An application of server  116  may cross-analyze each of the sounds between each of the received media and data streams  112 , and based on volume cues and location information (GPS coordinates, for example), be able to make intelligent estimation of sources for each sound element. From this, a map of events and actions may be determined to facilitate reliable and rapid accident investigation and impact or accident cause determination. 
       FIG. 3B  illustrates another flow diagram  320 , according to example embodiments. Referring to  FIG. 3B , geolocation boundaries  134  are established based on impact-related decibels  322 . Geolocation boundaries  134  define an area of interest related to the impact or accident. The area of interest may be generally centered on the specific location if the impact or accident, but need not be circular in shape. It may be irregular, with a longer axis in one or more directions and a shorter axis in one or more other directions. For example, an accident or impact may occur at an intersection of two streets. Other media or communication devices  138  may be unpredictably oriented around the impact or accident site, such as by merchants, pedestrians, or workers nearby. Each such device  138  may receive and detect audio related to the impact or accident, and an application within each communication device  138  may determine if the received audio is above a predetermined threshold. If the application determines the received audio is not above a predetermined threshold, reflecting either too far away to be useful or too low a level to analyze the audio and produce useful data, the received audio may be disregarded. If instead the application determines the received audio is above a predetermined threshold, the application may save the audio, forward or stream the audio and any associated media to the server, and/or save geolocation coordinates for the current device  138 . The coordinates may additionally be transferred to a server  116  in order for an application in server  116  to construct a 2-D geolocation map of the impact or accident. In one embodiment, the coordinates may be provided along with a unique identifier for the devices  138 A,  138 B which transmit the coordinates and any accompanying video, audio, data, or sensor data from devices  138 A,  138 B. 
     Multimedia content  142  related to the impact is transmitted to one or more other devices  324 . Finally, the other devices  138  receive the multimedia content  326 . The device(s)  138  and each of the transports  104 A,  104 B within the geolocation boundaries  134  may transmit multimedia content  142  to other devices  138  within the geolocation boundaries  134 . 
       FIG. 3C  illustrates yet another flow diagram  340 , according to example embodiments. Referring to  FIG. 3C , a dangerous driving situation is identified  342 , as previously described. In one embodiment, accelerometers within the vehicle involved in the dangerous driving situation  104  may detect the vehicle  104  driving erratically, and may trigger any of camera, audio, or sensor capture thereafter. In another embodiment, one or more cameras  162  or sensors within the vehicle involved in the dangerous driving situation  104  may detect erratic video, unpredictable and rapid steering changes, or speeds well in excess of speed limits (for example), and in response trigger any of camera, audio, or sensor capture thereafter. In yet another embodiment, a microphone within the vehicle involved in the dangerous driving situation  104  may provide audio to a speech recognition application within the transport  104  and detect speech patterns or language suggesting, stating, or implying an impaired driver. 
     Next, after identifying the dangerous driving situation, first media is captured by the vehicle involved in the dangerous driving situation  344 . First media may include any combination of video, audio, sensor data, and environmental data from single or multiple sources. For example, a car  104  may have multiple cameras  162  that may be used to capture video or images to different directions relative to the vehicle  104 . 
     A geofence is then established around the vehicle  346 , based on distance  154  from the dangerous driving situation. In one embodiment, this distance  154  may be a predetermined value that is always the same. In another embodiment, the distance  154  may be based on the type of location where the dangerous driving situation occurs—for example, 100 feet for an urban situation, 200 feet for a suburban situation, and 1000 feet for a rural situation. In yet another embodiment, the distance  154  may be based on a number of other vehicles  108  in proximity to the event—for example, a distance  154  to allow media capture from the closest three sources may set the geofence distance to 85 feet if all three vehicles  108  are within an 85 foot radius of the dangerous driving situation. 
     Finally, second media is captured by one or more other vehicles  108  within the geofence  348 . The second media is from the other vehicle&#39;s perspective  108 , and likely includes at least partially media providing data on the dangerous driving situation. As with the first media, the second media may include any combination of video, audio, sensor data, and environmental data from single or multiple sources. For example, a vehicle  108  may have a front camera  158  that may be used to capture video or images from a front direction relative to the vehicle  108 . This may beneficially provide additional data that may support or refute data from the first media. After capturing the first and the second media, many follow-on actions may be possible. For example, one or more vehicles  104 ,  108  may transfer first and second media to law enforcement, insurance provider, or other resources to take appropriate action. The appropriate action may include providing a warning to the driver of the vehicle involved in the dangerous driving situation  104 , providing a traffic or other citation to the driver of the vehicle involved in the dangerous driving situation  104 , raising insurance rates for the driver of the vehicle involved in the dangerous driving situation  104 , notifying a next of kin for the driver of the vehicle involved in the dangerous driving situation  104 , or notifying police dispatch of the location and circumstances of the dangerous driving situation. 
       FIG. 3D  illustrates yet another flow diagram  360 , according to example embodiments. Referring to  FIG. 3D , a vehicle is associated with an impact  362 . A vehicle may be associated with the impact, which may be in proximity with other vehicles. Media related to the impact may be captured from several sources may and include an identifier for a transport  104  involved in the impact or accident (there may be multiple vehicles  104 , and therefore multiple identifiers). The identifier, present within the media, associates the vehicle  104  with the impact. 
     Media is captured both before and after the impact, and saved  364 . The transport  104  may include one or more computing devices  174  that may transmit a media segment  178 —including media captured both before and after the impact—to a server  116  for further processing. 
     The captured media is transmitted to another computing device  366 . Finally, a sound profile is built from the captured media  368 . This has been previously described in detail with respect to  FIG. 1D . 
       FIG. 4  illustrates a machine learning transport network diagram  400 , according to example embodiments. The network  400  includes a transport node  402  that interfaces with a machine learning subsystem  406 . The transport node includes one or more sensors  404 . 
     The machine learning subsystem  406  contains a learning model  408  which is a mathematical artifact created by a machine learning training system  410  that generates predictions by finding patterns in one or more training data sets. In some embodiments, the machine learning subsystem  406  resides in the transport node  402 . In other embodiments, the machine learning subsystem  406  resides outside of the transport node  402 . 
     The transport node  402  sends data from the one or more sensors  404  to the machine learning subsystem  406 . The machine learning subsystem  406  provides the one or more sensor  404  data to the learning model  408  which returns one or more predictions. The machine learning subsystem  406  sends one or more instructions to the transport node  402  based on the predictions from the learning model  408 . 
     In a further embodiment, the transport node  402  may send the one or more sensor  404  data to the machine learning training system  410 . In yet another embodiment, the machine learning subsystem  406  may sent the sensor  404  data to the machine learning subsystem  410 . One or more of the applications, features, steps, solutions, etc., described and/or depicted herein may utilize the machine learning network  400  as described herein. 
       FIG. 5A  illustrates an example transport configuration  500  for managing database transactions associated with a transport, according to example embodiments. Referring to  FIG. 5A , as a particular transport/transport  525  is engaged in transactions (e.g., transport service, dealer transactions, delivery/pickup, transportation services, etc.), the transport may receive assets  510  and/or expel/transfer assets  512  according to a transaction(s). A transport processor  526  resides in the transport  525  and communication exists between the transport processor  526 , a database  530 , a transport processor  526  and the transaction module  520 . The transaction module  520  may record information, such as assets, parties, credits, service descriptions, date, time, location, results, notifications, unexpected events, etc. Those transactions in the transaction module  520  may be replicated into a database  530 . The database  530  can be one of a SQL database, an RDBMS, a relational database, a non-relational database, a blockchain, a distributed ledger, and may be on board the transport, may be off board the transport, may be accessible directly and/or through a network, or be accessible to the transport. 
       FIG. 5B  illustrates an example transport configuration  550  for managing database transactions conducted among various transports, according to example embodiments. The transport  525  may engage with another transport  508  to perform various actions such as to share, transfer, acquire service calls, etc. when the transport has reached a status where the services need to be shared with another transport. For example, the transport  508  may be due for a battery charge and/or may have an issue with a tire and may be in route to pick up a package for delivery. A transport processor  528  resides in the transport  508  and communication exists between the transport processor  528 , a database  554 , a transport processor  528  and the transaction module  552 . The transport  508  may notify another transport  525  which is in its network and which operates on its blockchain member service. A transport processor  526  resides in the transport  525  and communication exists between the transport processor  526 , a database  530 , the transport processor  526  and a transaction module  520 . The transport  525  may then receive the information via a wireless communication request to perform the package pickup from the transport  508  and/or from a server (not shown). The transactions are logged in the transaction modules  552  and  520  of both transports. The credits are transferred from transport  508  to transport  525  and the record of the transferred service is logged in the database  530 / 554  assuming that the blockchains are different from one another, or, are logged in the same blockchain used by all members. The database  554  can be one of a SQL database, an RDBMS, a relational database, a non-relational database, a blockchain, a distributed ledger, and may be on board the transport, may be off board the transport, may be accessible directly and/or through a network. 
       FIG. 6A  illustrates a blockchain architecture configuration  600 , according to example embodiments. Referring to  FIG. 6A , the blockchain architecture  600  may include certain blockchain elements, for example, a group of blockchain member nodes  602 - 606  as part of a blockchain group  610 . In one example embodiment, a permissioned blockchain is not accessible to all parties but only to those members with permissioned access to the blockchain data. The blockchain nodes participate in a number of activities, such as blockchain entry addition and validation process (consensus). One or more of the blockchain nodes may endorse entries based on an endorsement policy and may provide an ordering service for all blockchain nodes. A blockchain node may initiate a blockchain action (such as an authentication) and seek to write to a blockchain immutable ledger stored in the blockchain, a copy of which may also be stored on the underpinning physical infrastructure. 
     The blockchain transactions  620  are stored in memory of computers as the transactions are received and approved by the consensus model dictated by the members&#39; nodes. Approved transactions  626  are stored in current blocks of the blockchain and committed to the blockchain via a committal procedure which includes performing a hash of the data contents of the transactions in a current block and referencing a previous hash of a previous block. Within the blockchain, one or more smart contracts  630  may exist that define the terms of transaction agreements and actions included in smart contract executable application code  632 , such as registered recipients, transport features, requirements, permissions, sensor thresholds, etc. The code may be configured to identify whether requesting entities are registered to receive transport services, what service features they are entitled/required to receive given their profile statuses and whether to monitor their actions in subsequent events. For example, when a service event occurs and a user is riding in the transport, the sensor data monitoring may be triggered, and a certain parameter, such as a transport charge level, may be identified as being above/below a particular threshold for a particular period of time, then the result may be a change to a current status which requires an alert to be sent to the managing party (i.e., transport owner, transport operator, server, etc.) so the service can be identified and stored for reference. The transport sensor data collected may be based on types of sensor data used to collect information about transport&#39;s status. The sensor data may also be the basis for the transport event data  634 , such as a location(s) to be traveled, an average speed, a top speed, acceleration rates, whether there were any collisions, was the expected route taken, what is the next destination, whether safety measures are in place, whether the transport has enough charge/fuel, etc. All such information may be the basis of smart contract terms  630 , which are then stored in a blockchain. For example, sensor thresholds stored in the smart contract can be used as the basis for whether a detected service is necessary and when and where the service should be performed. 
       FIG. 6B  illustrates a shared ledger configuration, according to example embodiments. Referring to  FIG. 6B , the blockchain logic example  640  includes a blockchain application interface  642  as an API or plug-in application that links to the computing device and execution platform for a particular transaction. The blockchain configuration  640  may include one or more applications which are linked to application programming interfaces (APIs) to access and execute stored program/application code (e.g., smart contract executable code, smart contracts, etc.) which can be created according to a customized configuration sought by participants and can maintain their own state, control their own assets, and receive external information. This can be deployed as an entry and installed, via appending to the distributed ledger, on all blockchain nodes. 
     The smart contract application code  644  provides a basis for the blockchain transactions by establishing application code which when executed causes the transaction terms and conditions to become active. The smart contract  630 , when executed, causes certain approved transactions  626  to be generated, which are then forwarded to the blockchain platform  652 . The platform includes a security/authorization  658 , computing devices which execute the transaction management  656  and a storage portion  654  as a memory that stores transactions and smart contracts in the blockchain. 
     The blockchain platform may include various layers of blockchain data, services (e.g., cryptographic trust services, virtual execution environment, etc.), and underpinning physical computer infrastructure that may be used to receive and store new entries and provide access to auditors which are seeking to access data entries. The blockchain may expose an interface that provides access to the virtual execution environment necessary to process the program code and engage the physical infrastructure. Cryptographic trust services may be used to verify entries such as asset exchange entries and keep information private. 
     The blockchain architecture configuration of  FIGS. 6A and 6B  may process and execute program/application code via one or more interfaces exposed, and services provided, by the blockchain platform. As a non-limiting example, smart contracts may be created to execute reminders, updates, and/or other notifications subject to the changes, updates, etc. The smart contracts can themselves be used to identify rules associated with authorization and access requirements and usage of the ledger. For example, the information may include a new entry, which may be processed by one or more processing entities (e.g., processors, virtual machines, etc.) included in the blockchain layer. The result may include a decision to reject or approve the new entry based on the criteria defined in the smart contract and/or a consensus of the peers. The physical infrastructure may be utilized to retrieve any of the data or information described herein. 
     Within smart contract executable code, a smart contract may be created via a high-level application and programming language, and then written to a block in the blockchain. The smart contract may include executable code which is registered, stored, and/or replicated with a blockchain (e.g., distributed network of blockchain peers). An entry is an execution of the smart contract code which can be performed in response to conditions associated with the smart contract being satisfied. The executing of the smart contract may trigger a trusted modification(s) to a state of a digital blockchain ledger. The modification(s) to the blockchain ledger caused by the smart contract execution may be automatically replicated throughout the distributed network of blockchain peers through one or more consensus protocols. 
     The smart contract may write data to the blockchain in the format of key-value pairs. Furthermore, the smart contract code can read the values stored in a blockchain and use them in application operations. The smart contract code can write the output of various logic operations into the blockchain. The code may be used to create a temporary data structure in a virtual machine or other computing platform. Data written to the blockchain can be public and/or can be encrypted and maintained as private. The temporary data that is used/generated by the smart contract is held in memory by the supplied execution environment, then deleted once the data needed for the blockchain is identified. 
     A smart contract executable code may include the code interpretation of a smart contract, with additional features. As described herein, the smart contract executable code may be program code deployed on a computing network, where it is executed and validated by chain validators together during a consensus process. The smart contract executable code receives a hash and retrieves from the blockchain a hash associated with the data template created by use of a previously stored feature extractor. If the hashes of the hash identifier and the hash created from the stored identifier template data match, then the smart contract executable code sends an authorization key to the requested service. The smart contract executable code may write to the blockchain data associated with the cryptographic details. 
       FIG. 6C  illustrates a blockchain configuration for storing blockchain transaction data, according to example embodiments. Referring to  FIG. 6C , the example configuration  660  provides for the transport  662 , the user device  664  and a server  666  sharing information with a distributed ledger (i.e., blockchain)  668 . The server may represent a service provider entity inquiring with a transport service provider to share user profile rating information in the event that a known and established user profile is attempting to rent a transport with an established rated profile. The server  666  may be receiving and processing data related to a transport&#39;s service requirements. As the service events occur, such as the transport sensor data indicates a need for fuel/charge, a maintenance service, etc., a smart contract may be used to invoke rules, thresholds, sensor information gathering, etc., which may be used to invoke the transport service event. The blockchain transaction data  670  is saved for each transaction, such as the access event, the subsequent updates to a transport&#39;s service status, event updates, etc. The transactions may include the parties, the requirements (e.g., 18 years of age, service eligible candidate, valid driver&#39;s license, etc.), compensation levels, the distance traveled during the event, the registered recipients permitted to access the event and host a transport service, rights/permissions, sensor data retrieved during the transport event operation to log details of the next service event and identify a transport&#39;s condition status, and thresholds used to make determinations about whether the service event was completed and whether the transport&#39;s condition status has changed. 
       FIG. 6D  illustrates blockchain blocks  680  that can be added to a distributed ledger, according to example embodiments, and contents of block structures  682 A to  682   n . Referring to  FIG. 6D , clients (not shown) may submit entries to blockchain nodes to enact activity on the blockchain. As an example, clients may be applications that act on behalf of a requester, such as a device, person or entity to propose entries for the blockchain. The plurality of blockchain peers (e.g., blockchain nodes) may maintain a state of the blockchain network and a copy of the distributed ledger. Different types of blockchain nodes/peers may be present in the blockchain network including endorsing peers which simulate and endorse entries proposed by clients and committing peers which verify endorsements, validate entries, and commit entries to the distributed ledger. In this example, the blockchain nodes may perform the role of endorser node, committer node, or both. 
     The instant system includes a blockchain which stores immutable, sequenced records in blocks, and a state database (current world state) maintaining a current state of the blockchain. One distributed ledger may exist per channel and each peer maintains its own copy of the distributed ledger for each channel of which they are a member. The instant blockchain is an entry log, structured as hash-linked blocks where each block contains a sequence of N entries. Blocks may include various components such as those shown in  FIG. 6D . The linking of the blocks may be generated by adding a hash of a prior block&#39;s header within a block header of a current block. In this way, all entries on the blockchain are sequenced and cryptographically linked together preventing tampering with blockchain data without breaking the hash links. Furthermore, because of the links, the latest block in the blockchain represents every entry that has come before it. The instant blockchain may be stored on a peer file system (local or attached storage), which supports an append-only blockchain workload. 
     The current state of the blockchain and the distributed ledger may be stored in the state database. Here, the current state data represents the latest values for all keys ever included in the chain entry log of the blockchain. Smart contract executable code invocations execute entries against the current state in the state database. To make these smart contract executable code interactions extremely efficient, the latest values of all keys are stored in the state database. The state database may include an indexed view into the entry log of the blockchain, it can therefore be regenerated from the chain at any time. The state database may automatically get recovered (or generated if needed) upon peer startup, before entries are accepted. 
     Endorsing nodes receive entries from clients and endorse the entry based on simulated results. Endorsing nodes hold smart contracts which simulate the entry proposals. When an endorsing node endorses an entry, the endorsing nodes creates an entry endorsement which is a signed response from the endorsing node to the client application indicating the endorsement of the simulated entry. The method of endorsing an entry depends on an endorsement policy which may be specified within smart contract executable code. An example of an endorsement policy is “the majority of endorsing peers must endorse the entry.” Different channels may have different endorsement policies. Endorsed entries are forward by the client application to an ordering service. 
     The ordering service accepts endorsed entries, orders them into a block, and delivers the blocks to the committing peers. For example, the ordering service may initiate a new block when a threshold of entries has been reached, a timer times out, or another condition. In this example, blockchain node is a committing peer that has received a data block  682 A for storage on the blockchain. The ordering service may be made up of a cluster of orderers. The ordering service does not process entries, smart contracts, or maintain the shared ledger. Rather, the ordering service may accept the endorsed entries and specifies the order in which those entries are committed to the distributed ledger. The architecture of the blockchain network may be designed such that the specific implementation of ‘ordering’ (e.g., Solo, Kafka, BFT, etc.) becomes a pluggable component. 
     Entries are written to the distributed ledger in a consistent order. The order of entries is established to ensure that the updates to the state database are valid when they are committed to the network. Unlike a cryptocurrency blockchain system (e.g., Bitcoin, etc.) where ordering occurs through the solving of a cryptographic puzzle, or mining, in this example the parties of the distributed ledger may choose the ordering mechanism that best suits that network. 
     Referring to  FIG. 6D , a block  682 A (also referred to as a data block) that is stored on the blockchain and/or the distributed ledger may include multiple data segments such as a block header  684 A to  684   n , transaction specific data  686 A to  686   n , and block metadata  688 A to  688   n . It should be appreciated that the various depicted blocks and their contents, such as block  682 A and its contents are merely for purposes of an example and are not meant to limit the scope of the example embodiments. In some cases, both the block header  684 A and the block metadata  688 A may be smaller than the transaction specific data  686 A which stores entry data; however, this is not a requirement. The block  682 A may store transactional information of N entries (e.g.,  100 ,  500 ,  1000 ,  2000 ,  3000 , etc.) within the block data  690 A to  690   n . The block  682 A may also include a link to a previous block (e.g., on the blockchain) within the block header  684 A. In particular, the block header  684 A may include a hash of a previous block&#39;s header. The block header  684 A may also include a unique block number, a hash of the block data  690 A of the current block  682 A, and the like. The block number of the block  682 A may be unique and assigned in an incremental/sequential order starting from zero. The first block in the blockchain may be referred to as a genesis block which includes information about the blockchain, its members, the data stored therein, etc. 
     The block data  690 A may store entry information of each entry that is recorded within the block. For example, the entry data may include one or more of a type of the entry, a version, a timestamp, a channel ID of the distributed ledger, an entry ID, an epoch, a payload visibility, a smart contract executable code path (deploy tx), a smart contract executable code name, a smart contract executable code version, input (smart contract executable code and functions), a client (creator) identify such as a public key and certificate, a signature of the client, identities of endorsers, endorser signatures, a proposal hash, smart contract executable code events, response status, namespace, a read set (list of key and version read by the entry, etc.), a write set (list of key and value, etc.), a start key, an end key, a list of keys, a Merkel tree query summary, and the like. The entry data may be stored for each of the N entries. 
     In some embodiments, the block data  690 A may also store transaction specific data  686 A which adds additional information to the hash-linked chain of blocks in the blockchain. Accordingly, the data  686 A can be stored in an immutable log of blocks on the distributed ledger. Some of the benefits of storing such data  686 A are reflected in the various embodiments disclosed and depicted herein. The block metadata  688 A may store multiple fields of metadata (e.g., as a byte array, etc.). Metadata fields may include signature on block creation, a reference to a last configuration block, an entry filter identifying valid and invalid entries within the block, last offset persisted of an ordering service that ordered the block, and the like. The signature, the last configuration block, and the orderer metadata may be added by the ordering service. Meanwhile, a committer of the block (such as a blockchain node) may add validity/invalidity information based on an endorsement policy, verification of read/write sets, and the like. The entry filter may include a byte array of a size equal to the number of entries in the block data  610 A and a validation code identifying whether an entry was valid/invalid. 
     The other blocks  682 B to  682   n  in the blockchain also have headers, files, and values. However, unlike the first block  682 A, each of the headers  684 A to  684   n  in the other blocks includes the hash value of an immediately preceding block. The hash value of the immediately preceding block may be just the hash of the header of the previous block or may be the hash value of the entire previous block. By including the hash value of a preceding block in each of the remaining blocks, a trace can be performed from the Nth block back to the genesis block (and the associated original file) on a block-by-block basis, as indicated by arrows  692 , to establish an auditable and immutable chain-of-custody. 
     The above embodiments may be implemented in hardware, in a computer program executed by a processor, in firmware, or in a combination of the above. A computer program may be embodied on a computer readable medium, such as a storage medium. For example, a computer program may reside in random access memory (“RAM”), flash memory, read-only memory (“ROM”), erasable programmable read-only memory (“EPROM”), electrically erasable programmable read-only memory (“EEPROM”), registers, hard disk, a removable disk, a compact disk read-only memory (“CD-ROM”), or any other form of storage medium known in the art. 
     An exemplary storage medium may be coupled to the processor such that the processor may read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (“ASIC”). In the alternative, the processor and the storage medium may reside as discrete components. For example,  FIG. 7  illustrates an example computer system architecture  700 , which may represent or be integrated in any of the above-described components, etc. 
       FIG. 7  is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the application described herein. Regardless, the computing node  700  is capable of being implemented and/or performing any of the functionality set forth hereinabove. 
     In computing node  700  there is a computer system/server  702 , which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server  702  include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like. 
     Computer system/server  702  may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server  702  may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices. 
     As shown in  FIG. 7 , computer system/server  702  in cloud computing node  700  is shown in the form of a general-purpose computing device. The components of computer system/server  702  may include, but are not limited to, one or more processors or processing units  704 , a system memory  706 , and a bus that couples various system components including system memory  706  to processor  704 . 
     The bus represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus. 
     Computer system/server  702  typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server  702 , and it includes both volatile and non-volatile media, removable and non-removable media. System memory  706 , in one embodiment, implements the flow diagrams of the other figures. The system memory  706  can include computer system readable media in the form of volatile memory, such as random-access memory (RAM)  708  and/or cache memory  710 . Computer system/server  702  may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, memory  706  can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to the bus by one or more data media interfaces. As will be further depicted and described below, memory  706  may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of various embodiments of the application. 
     Program/utility, having a set (at least one) of program modules, may be stored in memory  706  by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules generally carry out the functions and/or methodologies of various embodiments of the application as described herein. 
     As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method, or computer program product. Accordingly, aspects of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present application may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
     Computer system/server  702  may also communicate with one or more external devices via an I/O device  712  (such as an I/O adapter), which may include a keyboard, a pointing device, a display, a voice recognition module, etc., one or more devices that enable a user to interact with computer system/server  702 , and/or any devices (e.g., network card, modem, etc.) that enable computer system/server  702  to communicate with one or more other computing devices. Such communication can occur via I/O interfaces of the device  712 . Still yet, computer system/server  702  can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via a network adapter. As depicted, device  712  communicates with the other components of computer system/server  702  via a bus. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server  702 . Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. 
     Although an exemplary embodiment of at least one of a system, method, and non-transitory computer readable medium has been illustrated in the accompanied drawings and described in the foregoing detailed description, it will be understood that the application is not limited to the embodiments disclosed, but is capable of numerous rearrangements, modifications, and substitutions as set forth and defined by the following claims. For example, the capabilities of the system of the various figures can be performed by one or more of the modules or components described herein or in a distributed architecture and may include a transmitter, receiver or pair of both. For example, all or part of the functionality performed by the individual modules, may be performed by one or more of these modules. Further, the functionality described herein may be performed at various times and in relation to various events, internal or external to the modules or components. Also, the information sent between various modules can be sent between the modules via at least one of: a data network, the Internet, a voice network, an Internet Protocol network, a wireless device, a wired device and/or via plurality of protocols. Also, the messages sent or received by any of the modules may be sent or received directly and/or via one or more of the other modules. 
     One skilled in the art will appreciate that a “system” could be embodied as a personal computer, a server, a console, a personal digital assistant (PDA), a cell phone, a tablet computing device, a smartphone or any other suitable computing device, or combination of devices. Presenting the above-described functions as being performed by a “system” is not intended to limit the scope of the present application in any way but is intended to provide one example of many embodiments. Indeed, methods, systems and apparatuses disclosed herein may be implemented in localized and distributed forms consistent with computing technology. 
     It should be noted that some of the system features described in this specification have been presented as modules, in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom very large-scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, graphics processing units, or the like. 
     A module may also be at least partially implemented in software for execution by various types of processors. An identified unit of executable code may, for instance, comprise one or more physical or logical blocks of computer instructions that may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the module and achieve the stated purpose for the module. Further, modules may be stored on a computer-readable medium, which may be, for instance, a hard disk drive, flash device, random access memory (RAM), tape, or any other such medium used to store data. 
     Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within modules and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network. 
     It will be readily understood that the components of the application, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments is not intended to limit the scope of the application as claimed but is merely representative of selected embodiments of the application. 
     One having ordinary skill in the art will readily understand that the above may be practiced with steps in a different order, and/or with hardware elements in configurations that are different than those which are disclosed. Therefore, although the application has been described based upon these preferred embodiments, it would be apparent to those of skill in the art that certain modifications, variations, and alternative constructions would be apparent. 
     While preferred embodiments of the present application have been described, it is to be understood that the embodiments described are illustrative only and the scope of the application is to be defined solely by the appended claims when considered with a full range of equivalents and modifications (e.g., protocols, hardware devices, software platforms etc.) thereto.