Patent Application
20250077327
The subject disclosure relates to a predictive automation for wireless devices (e.g., IoT/V2X devices) using intelligent data collection (e.g., KPI collection).
IoT and V2X monitoring monitors pre-configured error conditions and will alarm when there is an actual error. There is no alerting which can prevent an error before it happens. Also, current systems do not learn of the types of signals IoT equipment send out before an error, so there is no way to do predictive analysis on those conditions.
Event Data Recorders (EDRs) on vehicles are such that data needs to be downloaded from the EDR at the site of collision and correlated with the date, time, and location of the collision. This is a time consuming and error prone process.
Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:
The subject disclosure describes, among other things, illustrative embodiments for collecting training data from IoT devices, generating pre-failure thresholds from the training data, collecting data from IoTs in operation, and generating alarms when operations of the IoTs violate one or more pre-failure thresholds.
In one or more embodiments, automated intelligent widgets can be utilized to gather KPIs for IoTs and V2X devices (e.g., connected car, robots, factory equipment, etc.). In one or more embodiments, data can be sent to a data lake (storage system) to be used for predictive analysis of failure conditions. One or more of the exemplary embodiments can be connected to electronic alarming systems where a subscribed application can alert users to potential failure conditions. KPIs can be inherently policy based, and can include such items as time, date, location, part age, dimensions, etc. In one or more embodiments, emergency and mission critical events can be predicted, detected, reported, and/or prevented.
In one or more embodiments, the IoT and V2X monitoring does not need to be limited to pre-configured error conditions. In one or more embodiments, alerting can prevent an error before it happens. In one or more embodiments, the systems can learn of the types of signals IoT equipment send out before an error, which can further enable predictive analysis on those conditions. In one or more embodiments, a more dynamic and proactive system is provided. In one or more embodiments for a connect car, date, time and location can be collected to correlate with the other collected data.
In one or more embodiments, collection software (e.g., part of the IoT) can be configurable (e.g., by a server via provisioning including OTA provisioning) for adding and removing fields/KPIs to be monitored. In one or more embodiments, a crash or failure can be predicted before it happens.
In one or more embodiments, EDR typically records data for 15 to 120 seconds, consistently overwriting that data with fresh data as it is generating it. This data or a portion of it can be provided to the server prior to it being overwritten at the EDR. The EDR data can include, but is not limited to, vehicle speed change in velocity (delta-V), longitudinal acceleration, lateral acceleration, brake application, ABS activity, seatbelt usage, percent throttle, engine speed, steering wheel angle, yaw rate, roll angle, gear selection, tire pressure, and so forth. Additional data can be collected including date, time, location or weather condition (e.g., rain, snow, etc.) to correlate with data collected.
In one or more embodiments, a configurable widget or software can monitor equipment KPIs (which can be defined in a policy created based on the equipment type and/or usage profile-KPIs are often inherently policy based, and can include such items as time, date, location, part age, dimensions, etc.). The configurable widget can be provisioned to the IoT devices, accessible to the IoT devices or otherwise used in conjunction with the operation of the IoT devices. In one or more embodiments, KPIs can be automatically monitored and pushed to a data storage or data lake. In one or more embodiments, code in the data lake and/or application server can analyze the KPIs and correlate with error conditions to create a catalog of pre-failure thresholds to use for alarming of potential future failures. In one or more embodiments, AI/ML modeling can be utilized where the KPIs can be collected as training data for generating the pre-failure thresholds.
In one or more embodiments, an application (e.g., at a server) can alarm the user of the device via email or SMS such that the user can proactively repair the equipment rather than react to a failure. In one or more embodiments, the alarm can be utilized with other mitigation actions, such as being sent directly to the machine being monitored so that the machine can make an operational adjustment to avoid the potential failure. In one or more embodiments, the adjustment by the machine and the resulting operational parameters/KPIs collected for the machine can be transmitted to the server/data lake for further analysis, which can include or result in adjustment of the pre-failure thresholds. As an example, an alarm can be sent to a factory machine to adjust its operation so as to maintain a particular characteristic for a product produced by the machine. In this example, the adjustment made and the resulting characteristic for the product can be analyzed by the server to determine (e.g., via AI/ML modeling) that the pre-failure threshold can be increased or decreased without effecting the maintaining of the particular characteristic for the product.
In one or more embodiments, a vehicle can collect data from an EDR, append the date, time, location, weather conditions, etc., and can send it to a server periodically. This can be stored for longer periods of time than the EDR and the retention period can be configured or otherwise be adjustable including based on the current operation of the vehicle. For example, if a vehicle is moving at a high speed then data can be recorded more frequently than when it is moving slowly. Thus, all (or selected portions thereof) of the data that the EDR records can exist in a database correlated with data, time, weather (e.g., rain, snow, etc) and location that can be easily accessed. In one or more embodiments, an owner or other source can provision the vehicle with VIN #, License plate, Make/Model, Year, Policy numbers, contact information of insurance and Law Enforcement Agency, family members and other contacts as needed. This data can also be provided to and saved in the server. Thus at any moment of time the server has all information that is needed to process in case of an emergency.
In one or more embodiments, cost efficiency is provided since a problem can be fixed or mitigated before it becomes a costly repair after a failure. In one or more embodiments, product safety is provided such as avoiding a car crash, which can be based at least in part on information collected from other vehicles that are in proximity to the vehicle or that have previously passed through a location where the vehicle is presently located. In one or more embodiments, the predictive failure techniques can be utilized with FirstNet, emergency services, or other government agencies such as for safety equipment and military vehicles/airplanes and equipment monitoring.
In one or more embodiments for a connected car or vehicle, EDR data and/or other vehicle data can be accessible to the application and the vehicle can have direct or indirect cellular connectivity; crash emergency notification can be utilized with all data; stolen vehicle recovery can be utilized; all data can be made available from anywhere, anytime and does not have to depend on going to the vehicle to extract information such that the data can be immediately available. In one or more embodiments, since data is stored for a longer period of time, better evaluation of the situation can be done. In one or more embodiments, in case of a crash and the EDR gets destroyed, the system can still get the latest data on the server. Other embodiments are described in the subject disclosure.
One or more aspects of the subject disclosure include a device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations. The operations include receiving first data from a group of wireless devices, where the group of wireless devices each collect the first data that indicates first operational parameters and operational errors at each of the group of wireless devices; and analyzing the first data to generate pre-failure thresholds according to the first operational parameters and the operational errors. The operations include receiving second data from a wireless device, where the wireless device collects the second data that indicates second operational parameters at the wireless device; and analyzing the second data according to the pre-failure thresholds resulting in a pre-failure analysis. The operations include generating an alarm when the pre-failure analysis indicates that one or more of the second operational parameters violates one or more of the pre-failure thresholds, where the alarm is generated prior to a failure of the wireless device that is predictable based on the second operational parameters.
One or more aspects of the subject disclosure include a non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor of a wireless device, facilitate performance of operations. The operations include transmitting first data to a server according to a data policy, where the first data indicates first operational parameters and operational errors of the wireless device, where the first data and other first data is analyzed by the server to generate pre-failure thresholds, where the other first data is provided by a group of wireless devices to the server, where the group of wireless devices each collect the other first data that indicates the first operational parameters and the operational errors at each of the group of wireless devices. The operations include subsequently transmitting second data to the server, where the wireless device collects the second data that indicates second operational parameters at the wireless device, wherein the server analyzes the second data according to the pre-failure thresholds resulting in a pre-failure analysis, where an alarm is generated by the server when the pre-failure analysis indicates that one or more of the second operational parameters violates one or more of the pre-failure thresholds, and where the alarm is generated prior to a failure of the wireless device that is predictable based on the second operational parameters.
One or more aspects of the subject disclosure include a method, comprising: receiving, by a processing system including a processor of a server, first data from a group of wireless devices of a group of vehicles, where the group of wireless devices each collect the first data that indicates first operational parameters and operational errors at each of the group of vehicles. The method includes analyzing, by the processing system, the first data to generate pre-failure thresholds according to the first operational parameters and the operational errors; and receiving, by the processing system, second data from a wireless device of a particular vehicle, where the wireless device collects the second data that indicates second operational parameters at the particular vehicle. The method includes analyzing, by the processing system, the second data according to the pre-failure thresholds resulting in a pre-failure analysis; and providing, by the processing system, an alarm to the particular vehicle when the pre-failure analysis indicates that one or more of the second operational parameters violates one or more of the pre-failure thresholds, where the alarm is generated prior to a failure of the particular vehicle that is predictable based on the second operational parameters.
Referring now to
For example, system 100 can facilitate in whole or in part obtaining training from wireless devices, where the wireless devices collect the training data that indicates first operational parameters and operational errors at the wireless devices; analyzing the training data to generate pre-failure thresholds according to the first operational parameters and the operational errors; receiving operational data from a particular wireless device, where the particular wireless device collects the operational data that indicates second operational parameters at the particular wireless device; analyzing the operational data according to the pre-failure thresholds resulting in a pre-failure analysis; and providing an alarm when the pre-failure analysis indicates that one or more of the second operational parameters violates one or more of the pre-failure thresholds, where the alarm is generated prior to a failure of the particular wireless devices that is predictable based on the second operational parameters.
In particular, a communications network 125 is presented for providing broadband access 110 to a plurality of data terminals 114 via access terminal 112, wireless access 120 to a plurality of mobile devices 124 and vehicle 126 via base station or access point 122, voice access 130 to a plurality of telephony devices 134, via switching device 132 and/or media access 140 to a plurality of audio/video display devices 144 via media terminal 142. In addition, communication network 125 is coupled to one or more content sources 175 of audio, video, graphics, text and/or other media. While broadband access 110, wireless access 120, voice access 130 and media access 140 are shown separately, one or more of these forms of access can be combined to provide multiple access services to a single client device (e.g., mobile devices 124 can receive media content via media terminal 142, data terminal 114 can be provided voice access via switching device 132, and so on).
The communications network 125 includes a plurality of network elements (NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110, wireless access 120, voice access 130, media access 140 and/or the distribution of content from content sources 175. The communications network 125 can include a circuit switched or packet switched network, a voice over Internet protocol (VOIP) network, Internet protocol (IP) network, a cable network, a passive or active optical network, a 4G, 5G, or higher generation wireless access network, WIMAX network, UltraWideband network, personal area network or other wireless access network, a broadcast satellite network and/or other communications network.
In various embodiments, the access terminal 112 can include a digital subscriber line access multiplexer (DSLAM), cable modem termination system (CMTS), optical line terminal (OLT) and/or other access terminal. The data terminals 114 can include personal computers, laptop computers, netbook computers, tablets or other computing devices along with digital subscriber line (DSL) modems, data over coax service interface specification (DOCSIS) modems or other cable modems, a wireless modem such as a 4G, 5G, or higher generation modem, an optical modem and/or other access devices.
In various embodiments, the base station or access point 122 can include a 4G, 5G, or higher generation base station, an access point that operates via an 802.11 standard such as 802.11n, 802.11ac or other wireless access terminal. The mobile devices 124 can include mobile phones, e-readers, tablets, phablets, wireless modems, and/or other mobile computing devices.
In various embodiments, the switching device 132 can include a private branch exchange or central office switch, a media services gateway, VOIP gateway or other gateway device and/or other switching device. The telephony devices 134 can include traditional telephones (with or without a terminal adapter), VOIP telephones and/or other telephony devices.
In various embodiments, the media terminal 142 can include a cable head-end or other TV head-end, a satellite receiver, gateway or other media terminal 142. The display devices 144 can include televisions with or without a set top box, personal computers and/or other display devices.
In various embodiments, the content sources 175 include broadcast television and radio sources, video on demand platforms and streaming video and audio services platforms, one or more content data networks, data servers, web servers and other content servers, and/or other sources of media.
In various embodiments, the communications network 125 can include wired, optical and/or wireless links and the network elements 150, 152, 154, 156, etc. can include service switching points, signal transfer points, service control points, network gateways, media distribution hubs, servers, firewalls, routers, edge devices, switches and other network nodes for routing and controlling communications traffic over wired, optical and wireless links as part of the Internet and other public networks as well as one or more private networks, for managing subscriber access, for billing and network management and for supporting other network functions.
In one embodiment, analyzing the first data to generate the pre-failure thresholds includes deriving a particular pre-failure threshold for a particular second operational parameter according to one or more of the first operational parameters that correspond to a different type of IoT device than the wireless device.
In one embodiment, the group of wireless devices 2010 and the particular wireless device providing the second data can include different models of a same type of IoT device. In one embodiment, the server 185 can provide a notice of the alarm to an end user device of a user associated with the particular wireless device.
In one embodiment, the notice of the alarm is at least one of an email or a SMS communication. In one embodiment, at least one of the first operational parameters corresponds to at least one of the second operational parameters. In one embodiment, at least one of the first operational parameters is different from at least one of the second operational parameters. In one embodiment, analyzing the first data to generate the pre-failure thresholds includes deriving a particular pre-failure threshold for a particular second operational parameter according to one or more of the first operational parameters that is different from the particular second operational parameter.
In one embodiment, analyzing the first data to generate the pre-failure thresholds utilizes an AI/ML model. In one embodiment, the group of wireless devices 2010 and the wireless device include IoT devices. In one embodiment, the group of wireless devices 2010 and the wireless device include different models of a same type of IoT device. In one embodiment, the group of wireless devices 2010 and the wireless device include different types of IoT device.
In one embodiment, analyzing the first data to generate the pre-failure thresholds includes deriving a particular pre-failure threshold for a particular second operational parameter according to one or more of the first operational parameters that correspond to a different type of IoT device than the wireless device.
In one embodiment, the first operational parameters to be collected are according to a data policy that is based on types and usage profiles associated with the group of wireless devices 2010. In one embodiment, the server 185 can generate the data policy and provision the group of wireless devices 2010 with the data policy. In one embodiment, the first data is transmitted by the group of wireless devices 2010 at time periods according to a data policy (which can be fixed or dynamic) provisioned the group of wireless devices.
In one embodiment, the server 185 can receive first data from a group of wireless devices 2010 of a group of vehicles, where the group of wireless devices each collect the first data that indicates first operational parameters and operational errors at each of the group of vehicles. In one embodiment, the server 185 can analyze the first data to generate pre-failure thresholds according to the first operational parameters and the operational errors. In one embodiment, the server 185 can receive second data from a particular wireless device of a particular vehicle, where the particular wireless device collects the second data that indicates second operational parameters at the particular vehicle. In one embodiment, the server 185 can analyze the second data according to the pre-failure thresholds resulting in a pre-failure analysis. In one embodiment, the server 185 can provide an alarm to the particular vehicle when the pre-failure analysis indicates that one or more of the second operational parameters violates one or more of the pre-failure thresholds, where the alarm is generated prior to a failure of the particular vehicle that is predictable based on the second operational parameters.
In one embodiment, the group of wireless devices 2010 and the wireless device comprise EDRs that write over previously collected data, where the receiving the first data from the group of wireless devices occurs prior to the event data recorders writing over the previously collected data. In one embodiment, the group of wireless devices appends time and/or location information to the first operational parameters in the first data prior to being transmitted to the server.
In one or more embodiments, the IoTs can be EDRs that are continuously (or frequently) recording various data associated with the vehicle and its operation, and then overwriting a previous recording (e.g., a few minutes of collected data) unless a crash or other event stops the overwriting/recording function. EDRs can record a wide range of data elements, including brake usage, speed, steering, seat belt usage, component parameters (e.g., temperature, pressure, on, off, etc.). In one or more embodiments, the EDRs store information internally on an EEPROM until overwritten or recovered (e.g., after a crash), and can push the information to the server 185 (e.g., before the overwrite).
Process 220 can utilize code or algorithms in the server and/or data lake that will analyze the KPIs and correlate with error conditions to create a catalog of pre-failure thresholds to use for alarming of potential future failures. The application can alarm the user of the device, such as via email or SMS, such that the user or another device, entity, etc. can proactively repair the equipment rather than react to a failure.
In process 240, a V2X application can collect data from an EDR of the vehicle, append the date, time and location, and send it over a cellular network to a server to be recorded if or when the vehicle has network connectivity.
In process 240, a vehicle and/or an IoT device can be provisioned with various data such as a VIN number, license plate, make/model, year, insurance policy numbers, contact numbers of insurance and LEA, family members and other contacts as needed. For example, upon an emergency, the server can notify all or some of the contacts, such as via phone, text email or other notification technique.
In process 240, the time period for the history to be kept on the server can be configurable and encrypted, and can be accessible only by authorized personnel.
At 2630, the server can receive second data from a wireless device which can be one of the group of wireless devices or a different device. The wireless device can collect the second data that indicates second operational parameters at the wireless device. At 2640, the server can analyze the second data according to the pre-failure thresholds. At 2650, an alarm can be generated and transmitted (to various devices) when the pre-failure analysis indicates that one or more of the second operational parameters violates one or more of the pre-failure thresholds. In one or more embodiments, the alarm can be generated prior to a failure of the wireless device that is predictable based on the second operational parameters.
While for purposes of simplicity of explanation, the respective processes are shown and described as a series of blocks in
In one embodiment, operational data can be collected for IoT devices that are connected to a network to monitor the IoT devices, and, in some cases, the IoT devices can be sending information about anything it monitors, such as where the IoT device is a sensor that monitors performance of another device, such as an appliance, factory machine, and so forth. In one embodiment, the IoT device(s) can be part of or associated with vehicles, factory machines, homes, or other devices that can be monitored and/or managed by the IoT device(s).
In one embodiment, vehicles can include monitoring systems which can monitor vehicle performance and operations including locations, operations, KPIs, etc. In one embodiment, event data recorders inside a vehicle (e.g., a car) can record data (e.g., over 15 to 120 second periods) before overwriting the data. In one embodiment, the EDRs do not provide a date and time stamp for the collected data, but the exemplary embodiment can do so on behalf of or in conjunction with the operation of the EDRs.
In one embodiment, a vehicle can collect information (e.g., via an EDR or other sensors), and can send the collected data (or portions or summaries thereof) to a server/database along with a date and time stamp and/or location information.
In one embodiment, IoT device(s) data and/or vehicle data can be periodically collected (e.g., 15 or 30 seconds) which can be an adjustable collection period and which can be selected based on transmitting the data before it is overwritten at the IoT device, EDR or sensor device.
In one embodiment, while a vehicle is in operation (including moving), operational data can be collected including speed, time and location data. This information can be analyzed utilizing an AI/ML model to predict certain errors that could happen. In one embodiment, the collected data can include component utilization (e.g., brakes), engine temperature, oil pressure, RPM, and so forth. As an example, the received data can be analyzed to predict if a potential failure is predicted, such as an engine overheating. In one embodiment, crashes or vehicle failures can be identified by the server with or without the vehicle identifying that it has crashed or that it has experienced a failure (e.g., flat tire, engine seizing, and so forth) and an alarm can be generated, such as sending notifications of the crash or failure to the vehicle, end user device(s) associated with the vehicle such as the driver and/or passenger, insurance agents, contacts identified in a vehicle profile, and so forth.
In one embodiment, the alarm can be transmitted from the server to the vehicle so that the vehicle can take mitigation action, such as shutting down an air conditioner in the vehicle if the vehicle is near overheating, and so forth.
In one embodiment, IoT device data and/or vehicle data (e.g., from an EDR and/or other vehicle sensors) can be collected and analyzed as training data in order to generate AI/ML modeling so that predictions can be made for other IoT devices including other vehicles. In one embodiment, IoT data and/or vehicle data can be used for predicting behavior (e.g., potential failure, fault or error) of another IoT device and/or vehicle (which may be of a same type or of a different type) that may be subject to similar circumstances such as location, present operational parameters, and so forth.
In one embodiment, a slippery road can be identified for one vehicle and communicated to another vehicle (or a driver of the other vehicle) that is approaching the slippery road. In one embodiment, a failure or undesired condition detected (or a prediction therefor) for a factory machine or other device can be utilized to adjust operation of other similarly situated factory machines or other devices, such as in the same factory, residence, area, etc. In one embodiment, edge computing can be utilized for an entity for monitoring IoT devices of the entity based on predicted failure scenarios that are generated from training AI/ML models. The training data for the AI/ML models can come from the IoT devices and/or IoT devices of other entities.
Referring now to
In particular, a cloud networking architecture is shown that leverages cloud technologies and supports rapid innovation and scalability via a transport layer 350, a virtualized network function cloud 325 and/or one or more cloud computing environments 375. In various embodiments, this cloud networking architecture is an open architecture that leverages application programming interfaces (APIs); reduces complexity from services and operations; supports more nimble business models; and rapidly and seamlessly scales to meet evolving customer requirements including traffic growth, diversity of traffic types, and diversity of performance and reliability expectations.
In contrast to traditional network elements-which are typically integrated to perform a single function, the virtualized communication network employs virtual network elements (VNEs) 330, 332, 334, etc. that perform some or all of the functions of network elements 150, 152, 154, 156, etc. For example, the network architecture can provide a substrate of networking capability, often called Network Function Virtualization Infrastructure (NFVI) or simply infrastructure that is capable of being directed with software and Software Defined Networking (SDN) protocols to perform a broad variety of network functions and services. This infrastructure can include several types of substrates. The most typical type of substrate being servers that support Network Function Virtualization (NFV), followed by packet forwarding capabilities based on generic computing resources, with specialized network technologies brought to bear when general purpose processors or general purpose integrated circuit devices offered by merchants (referred to herein as merchant silicon) are not appropriate. In this case, communication services can be implemented as cloud-centric workloads.
As an example, a traditional network element 150 (shown in
In an embodiment, the transport layer 350 includes fiber, cable, wired and/or wireless transport elements, network elements and interfaces to provide broadband access 110, wireless access 120, voice access 130, media access 140 and/or access to content sources 175 for distribution of content to any or all of the access technologies. In particular, in some cases a network element needs to be positioned at a specific place, and this allows for less sharing of common infrastructure. Other times, the network elements have specific physical layer adapters that cannot be abstracted or virtualized, and might require special DSP code and analog front-ends (AFEs) that do not lend themselves to implementation as VNEs 330, 332 or 334. These network elements can be included in transport layer 350.
The virtualized network function cloud 325 interfaces with the transport layer 350 to provide the VNEs 330, 332, 334, etc. to provide specific NFVs. In particular, the virtualized network function cloud 325 leverages cloud operations, applications, and architectures to support networking workloads. The virtualized network elements 330, 332 and 334 can employ network function software that provides either a one-for-one mapping of traditional network element function or alternately some combination of network functions designed for cloud computing. For example, VNEs 330, 332 and 334 can include route reflectors, domain name system (DNS) servers, and dynamic host configuration protocol (DHCP) servers, system architecture evolution (SAE) and/or mobility management entity (MME) gateways, broadband network gateways, IP edge routers for IP-VPN, Ethernet and other services, load balancers, distributers and other network elements. Because these elements don't typically need to forward large amounts of traffic, their workload can be distributed across a number of servers—each of which adds a portion of the capability, and overall which creates an elastic function with higher availability than its former monolithic version. These virtual network elements 330, 332, 334, etc. can be instantiated and managed using an orchestration approach similar to those used in cloud compute services.
The cloud computing environments 375 can interface with the virtualized network function cloud 325 via APIs that expose functional capabilities of the VNEs 330, 332, 334, etc. to provide the flexible and expanded capabilities to the virtualized network function cloud 325. In particular, network workloads may have applications distributed across the virtualized network function cloud 325 and cloud computing environment 375 and in the commercial cloud, or might simply orchestrate workloads supported entirely in NFV infrastructure from these third party locations.
Turning now to
Generally, program modules comprise routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the methods can be practiced with other computer system configurations, comprising single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.
As used herein, a processing circuit includes one or more processors as well as other application specific circuits such as an application specific integrated circuit, digital logic circuit, state machine, programmable gate array or other circuit that processes input signals or data and that produces output signals or data in response thereto. It should be noted that while any functions and features described herein in association with the operation of a processor could likewise be performed by a processing circuit.
The illustrated embodiments of the embodiments herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.
Computing devices typically comprise a variety of media, which can comprise computer-readable storage media and/or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media can be any available storage media that can be accessed by the computer and comprises both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable instructions, program modules, structured data or unstructured data.
Computer-readable storage media can comprise, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or other tangible and/or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se.
Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium.
Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and comprises any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media comprise wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.
With reference again to
The system bus 408 can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 406 comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can be stored in a non-volatile memory such as ROM, erasable programmable read only memory (EPROM), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer 402, such as during startup. The RAM 412 can also comprise a high-speed RAM such as static RAM for caching data.
The computer 402 further comprises an internal hard disk drive (HDD) 414 (e.g., EIDE, SATA), which internal HDD 414 can also be configured for external use in a suitable chassis (not shown), a magnetic floppy disk drive (FDD) 416, (e.g., to read from or write to a removable diskette 418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or, to read from or write to other high capacity optical media such as the DVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can be connected to the system bus 408 by a hard disk drive interface 424, a magnetic disk drive interface 426 and an optical drive interface 428, respectively. The hard disk drive interface 424 for external drive implementations comprises at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are within contemplation of the embodiments described herein.
The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer 402, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to a hard disk drive (HDD), a removable magnetic diskette, and a removable optical media such as a CD or DVD, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, can also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein.
A number of program modules can be stored in the drives and RAM 412, comprising an operating system 430, one or more application programs 432, other program modules 434 and program data 436. All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM 412. The systems and methods described herein can be implemented utilizing various commercially available operating systems or combinations of operating systems.
A user can enter commands and information into the computer 402 through one or more wired/wireless input devices, e.g., a keyboard 438 and a pointing device, such as a mouse 440. Other input devices (not shown) can comprise a microphone, an infrared (IR) remote control, a joystick, a game pad, a stylus pen, touch screen or the like. These and other input devices are often connected to the processing unit 404 through an input device interface 442 that can be coupled to the system bus 408, but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a universal serial bus (USB) port, an IR interface, etc.
A monitor 444 or other type of display device can be also connected to the system bus 408 via an interface, such as a video adapter 446. It will also be appreciated that in alternative embodiments, a monitor 444 can also be any display device (e.g., another computer having a display, a smart phone, a tablet computer, etc.) for receiving display information associated with computer 402 via any communication means, including via the Internet and cloud-based networks. In addition to the monitor 444, a computer typically comprises other peripheral output devices (not shown), such as speakers, printers, etc.
The computer 402 can operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s) 448. The remote computer(s) 448 can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically comprises many or all of the elements described relative to the computer 402, although, for purposes of brevity, only a remote memory/storage device 450 is illustrated. The logical connections depicted comprise wired/wireless connectivity to a local area network (LAN) 452 and/or larger networks, e.g., a wide area network (WAN) 454. Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet.
When used in a LAN networking environment, the computer 402 can be connected to the LAN 452 through a wired and/or wireless communication network interface or adapter 456. The adapter 456 can facilitate wired or wireless communication to the LAN 452, which can also comprise a wireless AP disposed thereon for communicating with the adapter 456.
When used in a WAN networking environment, the computer 402 can comprise a modem 458 or can be connected to a communications server on the WAN 454 or has other means for establishing communications over the WAN 454, such as by way of the Internet. The modem 458, which can be internal or external and a wired or wireless device, can be connected to the system bus 408 via the input device interface 442. In a networked environment, program modules depicted relative to the computer 402 or portions thereof, can be stored in the remote memory/storage device 450. It will be appreciated that the network connections shown are example and other means of establishing a communications link between the computers can be used.
The computer 402 can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, restroom), and telephone. This can comprise Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices.
Wi-Fi can allow connection to the Internet from a couch at home, a bed in a hotel room or a conference room at work, without wires. Wi-Fi is a wireless technology similar to that used in a cell phone that enables such devices, e.g., computers, to send and receive data indoors and out; anywhere within the range of a base station. Wi-Fi networks use radio technologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to provide secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wired networks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radio bands for example or with products that contain both bands (dual band), so the networks can provide real-world performance similar to the basic 10BaseT wired Ethernet networks used in many offices.
Turning now to
In addition to receiving and processing CS-switched traffic and signaling, PS gateway node(s) 518 can authorize and authenticate PS-based data sessions with served mobile devices. Data sessions can comprise traffic, or content(s), exchanged with networks external to the mobile network platform 510, like wide area network(s) (WANs) 550, enterprise network(s) 570, and service network(s) 580, which can be embodied in local area network(s) (LANs), can also be interfaced with mobile network platform 510 through PS gateway node(s) 518. It is to be noted that WANs 550 and enterprise network(s) 570 can embody, at least in part, a service network(s) like IP multimedia subsystem (IMS). Based on radio technology layer(s) available in technology resource(s) or radio access network 520, PS gateway node(s) 518 can generate packet data protocol contexts when a data session is established; other data structures that facilitate routing of packetized data also can be generated. To that end, in an aspect, PS gateway node(s) 518 can comprise a tunnel interface (e.g., tunnel termination gateway (TTG) in 3GPP UMTS network(s) (not shown)) which can facilitate packetized communication with disparate wireless network(s), such as Wi-Fi networks.
In embodiment 500, mobile network platform 510 also comprises serving node(s) 516 that, based upon available radio technology layer(s) within technology resource(s) in the radio access network 520, convey the various packetized flows of data streams received through PS gateway node(s) 518. It is to be noted that for technology resource(s) that rely primarily on CS communication, server node(s) can deliver traffic without reliance on PS gateway node(s) 518; for example, server node(s) can embody at least in part a mobile switching center. As an example, in a 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRS support node(s) (SGSN).
For radio technologies that exploit packetized communication, server(s) 514 in mobile network platform 510 can execute numerous applications that can generate multiple disparate packetized data streams or flows, and manage (e.g., schedule, queue, format . . . ) such flows. Such application(s) can comprise add-on features to standard services (for example, provisioning, billing, customer support . . . ) provided by mobile network platform 510. Data streams (e.g., content(s) that are part of a voice call or data session) can be conveyed to PS gateway node(s) 518 for authorization/authentication and initiation of a data session, and to serving node(s) 516 for communication thereafter. In addition to application server, server(s) 514 can comprise utility server(s), a utility server can comprise a provisioning server, an operations and maintenance server, a security server that can implement at least in part a certificate authority and firewalls as well as other security mechanisms, and the like. In an aspect, security server(s) secure communication served through mobile network platform 510 to ensure network's operation and data integrity in addition to authorization and authentication procedures that CS gateway node(s) 512 and PS gateway node(s) 518 can enact. Moreover, provisioning server(s) can provision services from external network(s) like networks operated by a disparate service provider; for instance, WAN 550 or Global Positioning System (GPS) network(s) (not shown). Provisioning server(s) can also provision coverage through networks associated to mobile network platform 510 (e.g., deployed and operated by the same service provider), such as the distributed antennas networks shown in
It is to be noted that server(s) 514 can comprise one or more processors configured to confer at least in part the functionality of mobile network platform 510. To that end, the one or more processor can execute code instructions stored in memory 530, for example. It is should be appreciated that server(s) 514 can comprise a content manager, which operates in substantially the same manner as described hereinbefore.
In example embodiment 500, memory 530 can store information related to operation of mobile network platform 510. Other operational information can comprise provisioning information of mobile devices served through mobile network platform 510, subscriber databases; application intelligence, pricing schemes, e.g., promotional rates, flat-rate programs, couponing campaigns; technical specification(s) consistent with telecommunication protocols for operation of disparate radio, or wireless, technology layers; and so forth. Memory 530 can also store information from at least one of telephony network(s) 540, WAN 550, SS7 network 560, or enterprise network(s) 570. In an aspect, memory 530 can be, for example, accessed as part of a data store component or as a remotely connected memory store.
In order to provide a context for the various aspects of the disclosed subject matter,
Turning now to
The communication device 600 can comprise a wireline and/or wireless transceiver 602 (herein transceiver 602), a user interface (UI) 604, a power supply 614, a location receiver 616, a motion sensor 618, an orientation sensor 620, and a controller 606 for managing operations thereof. The transceiver 602 can support short-range or long-range wireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, or cellular communication technologies, just to mention a few (Bluetooth® and ZigBee® are trademarks registered by the Bluetooth® Special Interest Group and the ZigBee® Alliance, respectively). Cellular technologies can include, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO, WiMAX, SDR, LTE, as well as other next generation wireless communication technologies as they arise. The transceiver 602 can also be adapted to support circuit-switched wireline access technologies (such as PSTN), packet-switched wireline access technologies (such as TCP/IP, VOIP, etc.), and combinations thereof.
The UI 604 can include a depressible or touch-sensitive keypad 608 with a navigation mechanism such as a roller ball, a joystick, a mouse, or a navigation disk for manipulating operations of the communication device 600. The keypad 608 can be an integral part of a housing assembly of the communication device 600 or an independent device operably coupled thereto by a tethered wireline interface (such as a USB cable) or a wireless interface supporting for example Bluetooth®. The keypad 608 can represent a numeric keypad commonly used by phones, and/or a QWERTY keypad with alphanumeric keys. The UI 604 can further include a display 610 such as monochrome or color LCD (Liquid Crystal Display), OLED (Organic Light Emitting Diode) or other suitable display technology for conveying images to an end user of the communication device 600. In an embodiment where the display 610 is touch-sensitive, a portion or all of the keypad 608 can be presented by way of the display 610 with navigation features.
The display 610 can use touch screen technology to also serve as a user interface for detecting user input. As a touch screen display, the communication device 600 can be adapted to present a user interface having graphical user interface (GUI) elements that can be selected by a user with a touch of a finger. The display 610 can be equipped with capacitive, resistive or other forms of sensing technology to detect how much surface area of a user's finger has been placed on a portion of the touch screen display. This sensing information can be used to control the manipulation of the GUI elements or other functions of the user interface. The display 610 can be an integral part of the housing assembly of the communication device 600 or an independent device communicatively coupled thereto by a tethered wireline interface (such as a cable) or a wireless interface.
The UI 604 can also include an audio system 612 that utilizes audio technology for conveying low volume audio (such as audio heard in proximity of a human ear) and high volume audio (such as speakerphone for hands free operation). The audio system 612 can further include a microphone for receiving audible signals of an end user. The audio system 612 can also be used for voice recognition applications. The UI 604 can further include an image sensor 613 such as a charged coupled device (CCD) camera for capturing still or moving images.
The power supply 614 can utilize common power management technologies such as replaceable and rechargeable batteries, supply regulation technologies, and/or charging system technologies for supplying energy to the components of the communication device 600 to facilitate long-range or short-range portable communications. Alternatively, or in combination, the charging system can utilize external power sources such as DC power supplied over a physical interface such as a USB port or other suitable tethering technologies.
The location receiver 616 can utilize location technology such as a global positioning system (GPS) receiver capable of assisted GPS for identifying a location of the communication device 600 based on signals generated by a constellation of GPS satellites, which can be used for facilitating location services such as navigation. The motion sensor 618 can utilize motion sensing technology such as an accelerometer, a gyroscope, or other suitable motion sensing technology to detect motion of the communication device 600 in three-dimensional space. The orientation sensor 620 can utilize orientation sensing technology such as a magnetometer to detect the orientation of the communication device 600 (north, south, west, and cast, as well as combined orientations in degrees, minutes, or other suitable orientation metrics).
The communication device 600 can use the transceiver 602 to also determine a proximity to a cellular, WiFi, Bluetooth®, or other wireless access points by sensing techniques such as utilizing a received signal strength indicator (RSSI) and/or signal time of arrival (TOA) or time of flight (TOF) measurements. The controller 606 can utilize computing technologies such as a microprocessor, a digital signal processor (DSP), programmable gate arrays, application specific integrated circuits, and/or a video processor with associated storage memory such as Flash, ROM, RAM, SRAM, DRAM or other storage technologies for executing computer instructions, controlling, and processing data supplied by the aforementioned components of the communication device 600.
Other components not shown in
The terms “first,” “second,” “third,” and so forth, as used in the claims, unless otherwise clear by context, is for clarity only and doesn't otherwise indicate or imply any order in time. For instance, “a first determination,” “a second determination,” and “a third determination,” does not indicate or imply that the first determination is to be made before the second determination, or vice versa, etc.
In the subject specification, terms such as “store,” “storage,” “data store,” data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component, refer to “memory components,” or entities embodied in a “memory” or components comprising the memory. It will be appreciated that the memory components described herein can be either volatile memory or nonvolatile memory, or can comprise both volatile and nonvolatile memory, by way of illustration, and not limitation, volatile memory, non-volatile memory, disk storage, and memory storage. Further, nonvolatile memory can be included in read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can comprise random access memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). Additionally, the disclosed memory components of systems or methods herein are intended to comprise, without being limited to comprising, these and any other suitable types of memory.
Moreover, it will be noted that the disclosed subject matter can be practiced with other computer system configurations, comprising single-processor or multiprocessor computer systems, mini-computing devices, mainframe computers, as well as personal computers, hand-held computing devices (e.g., PDA, phone, smartphone, watch, tablet computers, netbook computers, etc.), microprocessor-based or programmable consumer or industrial electronics, and the like. The illustrated aspects can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network; however, some if not all aspects of the subject disclosure can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.
In one or more embodiments, information regarding use of services can be generated including services being accessed, media consumption history, user preferences, and so forth. This information can be obtained by various methods including user input, detecting types of communications (e.g., video content vs. audio content), analysis of content streams, sampling, and so forth. The generating, obtaining and/or monitoring of this information can be responsive to an authorization provided by the user. In one or more embodiments, an analysis of data can be subject to authorization from user(s) associated with the data, such as an opt-in, an opt-out, acknowledgement requirements, notifications, selective authorization based on types of data, and so forth.
Some of the embodiments described herein can also employ artificial intelligence (AI) to facilitate automating one or more features described herein. The embodiments (e.g., in connection with automatically identifying acquired cell sites that provide a maximum value/benefit after addition to an existing communication network) can employ various AI-based schemes for carrying out various embodiments thereof. Moreover, the classifier can be employed to determine a ranking or priority of each cell site of the acquired network. A classifier is a function that maps an input attribute vector, X=(x1, x2, x3, x4, . . . , xn), to a confidence that the input belongs to a class, that is, f (x)=confidence (class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to determine or infer an action that a user desires to be automatically performed. A support vector machine (SVM) is an example of a classifier that can be employed. The SVM operates by finding a hypersurface in the space of possible inputs, which the hypersurface attempts to split the triggering criteria from the non-triggering events. Intuitively, this makes the classification correct for testing data that is near, but not identical to training data. Other directed and undirected model classification approaches comprise, e.g., naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models providing different patterns of independence can be employed. Classification as used herein also is inclusive of statistical regression that is utilized to develop models of priority.
As will be readily appreciated, one or more of the embodiments can employ classifiers that are explicitly trained (e.g., via a generic training data) as well as implicitly trained (e.g., via observing UE behavior, operator preferences, historical information, receiving extrinsic information). For example, SVMs can be configured via a learning or training phase within a classifier constructor and feature selection module. Thus, the classifier(s) can be used to automatically learn and perform a number of functions, including but not limited to determining according to predetermined criteria which of the acquired cell sites will benefit a maximum number of subscribers and/or which of the acquired cell sites will add minimum value to the existing communication network coverage, etc.
As used in some contexts in this application, in some embodiments, the terms “component,” “system” and the like are intended to refer to, or comprise, a computer-related entity or an entity related to an operational apparatus with one or more specific functionalities, wherein the entity can be either hardware, a combination of hardware and software, software, or software in execution. As an example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, computer-executable instructions, a program, and/or a computer. By way of illustration and not limitation, both an application running on a server and the server can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components may communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software or firmware application executed by a processor, wherein the processor can be internal or external to the apparatus and executes at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, the electronic components can comprise a processor therein to execute software or firmware that confers at least in part the functionality of the electronic components. While various components have been illustrated as separate components, it will be appreciated that multiple components can be implemented as a single component, or a single component can be implemented as multiple components, without departing from example embodiments.
Further, the various embodiments can be implemented as a method, apparatus or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device or computer-readable storage/communications media. For example, computer readable storage media can include, but are not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips), optical disks (e.g., compact disk (CD), digital versatile disk (DVD)), smart cards, and flash memory devices (e.g., card, stick, key drive). Of course, those skilled in the art will recognize many modifications can be made to this configuration without departing from the scope or spirit of the various embodiments.
In addition, the words “example” and “exemplary” are used herein to mean serving as an instance or illustration. Any embodiment or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word example or exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form.
Moreover, terms such as “user equipment,” “mobile station,” “mobile,” subscriber station,” “access terminal,” “terminal,” “handset,” “mobile device” (and/or terms representing similar terminology) can refer to a wireless device utilized by a subscriber or user of a wireless communication service to receive or convey data, control, voice, video, sound, gaming or substantially any data-stream or signaling-stream. The foregoing terms are utilized interchangeably herein and with reference to the related drawings.
Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” and the like are employed interchangeably throughout, unless context warrants particular distinctions among the terms. It should be appreciated that such terms can refer to human entities or automated components supported through artificial intelligence (e.g., a capacity to make inference based, at least, on complex mathematical formalisms), which can provide simulated vision, sound recognition and so forth.
As employed herein, the term “processor” can refer to substantially any computing processing unit or device comprising, but not limited to comprising, single-core processors; single-processors with software multithread execution capability; multi-core processors; multi-core processors with software multithread execution capability; multi-core processors with hardware multithread technology; parallel platforms; and parallel platforms with distributed shared memory. Additionally, a processor can refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), a discrete gate or transistor logic, discrete hardware components or any combination thereof designed to perform the functions described herein. Processors can exploit nano-scale architectures such as, but not limited to, molecular and quantum-dot based transistors, switches and gates, in order to optimize space usage or enhance performance of user equipment. A processor can also be implemented as a combination of computing processing units.
As used herein, terms such as “data storage,” data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component, refer to “memory components,” or entities embodied in a “memory” or components comprising the memory. It will be appreciated that the memory components or computer-readable storage media, described herein can be either volatile memory or nonvolatile memory or can include both volatile and nonvolatile memory.
What has been described above includes mere examples of various embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing these examples, but one of ordinary skill in the art can recognize that many further combinations and permutations of the present embodiments are possible. Accordingly, the embodiments disclosed and/or claimed herein are intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.
In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained.
As may also be used herein, the term(s) “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via one or more intervening items. Such items and intervening items include, but are not limited to, junctions, communication paths, components, circuit elements, circuits, functional blocks, and/or devices. As an example of indirect coupling, a signal conveyed from a first item to a second item may be modified by one or more intervening items by modifying the form, nature or format of information in a signal, while one or more elements of the information in the signal are nevertheless conveyed in a manner than can be recognized by the second item. In a further example of indirect coupling, an action in a first item can cause a reaction on the second item, as a result of actions and/or reactions in one or more intervening items.
Although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement which achieves the same or similar purpose may be substituted for the embodiments described or shown by the subject disclosure. The subject disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, can be used in the subject disclosure. For instance, one or more features from one or more embodiments can be combined with one or more features of one or more other embodiments. In one or more embodiments, features that are positively recited can also be negatively recited and excluded from the embodiment with or without replacement by another structural and/or functional feature. The steps or functions described with respect to the embodiments of the subject disclosure can be performed in any order. The steps or functions described with respect to the embodiments of the subject disclosure can be performed alone or in combination with other steps or functions of the subject disclosure, as well as from other embodiments or from other steps that have not been described in the subject disclosure. Further, more than or less than all of the features described with respect to an embodiment can also be utilized.
This application claims the benefit of priority to U.S. Provisional Application No. 63/580,614, filed on Sep. 5, 2023. All sections of the aforementioned application(s) and/or patent(s) are incorporated herein by reference in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63580614 | Sep 2023 | US |
