BACKGROUND

Technical Field

[0001] The embodiments herein generally relate to field of remote processing, and more specifically, to a processor less devices connected to a remote processing unit for processing and execution.

Description of the Related Art

[0002] Over the years, with the technological advancement in the network of connected device, it has become easier for one to control all the devices that are connected to the network.

However, these devices are not capable of functioning automatically. More often the user has to manually create some rules or mimic their function in order to make the system to perform certain tasks automatically.

[0003] However, the system is not capable of identifying the relevant situation and perform certain tasks in accordance with the given situation. One has to manually override the conditions in accordance with the given situation.

[0004] Further network connected devices requires individual high-end processing unit or computing devices in order to perform their functions. This in turn increase the cost to maintain and manage computer systems, cost to purchase and install servers, update cost, and cost to purchase software, time, and human power.

[0005] Accordingly, there remains a need for a system and method for automatic functioning of the devices that predicts the future operating condition of the network connected devices and also learns from previous situations and automatically execute the desired functions. Further there is also a need for a common remote processing unit which can be leased/accessed by the user devices which reduces the requirement of individual high-end processing unit or computing unit thereby reducing the overall cost and maintenance cost of the user devices.

SUMMARY

[0006] In view of a foregoing, an embodiment herein provides a system for processor less computing in machines, devices and consumer electronics for processing as service. The system includes one or more user devices and a remote processing unit. The one or more user devices performs one or more functions. The remote processing unit includes a memory that stores (a) a set of modules and (b) a database that stores (i) one or more of input data from the one or more user devices and the remote processing unit which executes the set of modules. The remote processing unit includes one or more processing stations to perform the one or more functions. The set of modules includes an input data receiving unit and an artificial intelligence engine. The input data receiving unit receives one or more inputs from the one or more user devices. The one or more inputs include at least one of (a) an identification number for the one or more user devices, (b) request for accessing the remote processing unit, (c) request for registering the one or more user devices into the remote processing unit or (d) one or more performance data associated with the one or more user devices. The artificial intelligence engine assigns the one or more functions to the one or more processing stations based on the one or more inputs received from the one or more user devices. A first processing station is configured to (i) receive the one or more functions and identifies priority of each of the one or more functions and nature of each functions, (ii) distribute the one or more functions to the subsequent processing stations connected in a network, (iii) collect processed functions from the subsequent processing stations and compile the processed functions and (iv) communicate the combined processed functions to the remote processing unit.

[0007] In one embodiment, the subsequent processing stations receives the one or more functions from the first processing station and processes the one or more functions assigned to each of the subsequent processing stations and transmits the processed function to the first processing station.

[0008] In another embodiment, the one or more user devices request the remote processing unit to perform the one or more functions. The remote processing unit receives the request from the one or more user devices through the input data receiving unit and assigns the received request to the one or more processing stations PS 1-PSn connected in the network.

[0009] In yet another embodiment, the remote processing station includes a machine feedback receiving unit that receives one or more machine feedback data and communicate the one or more machine feedback data to the artificial intelligence engine to forecast the future processing requirements of the one or more user devices.

[0010] In yet another embodiment, the artificial engine includes a machine behavior predicting unit that forecasts future behavior of the one or more user devices by analyzing the one or more machine feedback data with past behavioral data to forecast the future processing requirements of the one or more user devices.

[0011] In yet another embodiment, the remote processing unit further includes executable commands generating unit and executable commands transmitting unit. The executable commands generating unit generates one or more executable commands to the one or more user devices based on forecasted future behavior of the one or more user devices. The executable commands transmitting unit transmits the one or more executable commands to the one or more user devices to perform the functions assigned to the one or more user devices.

[0012] In yet another embodiment, the remote processing unit transmits necessary control commands to the one or more user devices based on a behavior of the one or more user devices.

[0013] In yet another embodiment, the one or more user devices includes a unique identification number for accessing the remote processing unit and/or associate with the remote processing unit to perform functions assigned to the one or more user devices.

[0014] In yet another embodiment, the system further includes one or more remote processing units that communicates with (a) the one or more processing stations, (b) the one or more user devices which is an interface device through a wired or wireless communication device, (c) machines through the communication driver and (d) a user through one or more wireless devices.

[0015] In one aspect, a method for performing one or more functions assigned to the one or more user devices using an artificial intelligence engine is provided. The method includes steps of (i) receiving, using an input data receiving unit, one or more inputs from the one or more user devices, (ii) assigning, using an artificial intelligence engine, the one or more functions to one or more processing stations based on the one or more inputs received from the one or more user devices by identifying priority of each of the one or more functions and nature of each functions, (iii) collecting, using the first processing station, processed functions from the one or more processing stations and compiling the processed functions and (iv) communicating, using the first processing station, the combined processed functions to the remote processing unit.

[0016] The system eliminates the need for local processing in devices and complete/partial processing is decentralized to distant processor which provides service for connected devices. The system provides the user is introduced to a new form of modem which takes user requirements (i) in robots heavy processing is decentralized, (ii) in consumer computers CPU is eliminated, (iii) in autonomous cars and electric vehicles processing is decentralized, (iv) acts as AI engine, Telematics analytics and processing ends, knowledge bases and decision making end of advanced software applications, Predictive analysis of environment and communication to action taking devices and knowledge and decision making bots, (v) power of super computer is provided to every user to select configurations every time, (vi) reduces unutilized Computing space and processing capability, (vii) inputs and decision making for Virtual reality headsets and wearables is decentralized to remote stations, (viii) Virtual medicine and acts as processing ends for several sensor nodes neural implants and computational requirements of any biological system or implants ex: optogenetics and (ix) used for control device processing decentralization in energy generation and monitoring, telematics and home automation, e-commerce and business outsourcing.

[0017] The embodiment aims at providing a system and method for automatic functioning of the devices that predicts the future operating condition of the network connected devices and also learns from previous situations and also automatically execute the desired functions. Further the system also includes a remote processing unit that can be accessed by the other user device on a rental basis which reduces the requirement of individual high-end processing unit or computing devices and thereby reducing the overall cost and maintenance cost of the user devices. The system and method further provide processor less computing by outsourcing processing requirements and a device which enables the concept. The system provides a low cost portable device which provides computing power to user via cloud rather than local processor.

[0018] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS [0019] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:

[0020] FIG. 1 illustrates a schematic diagram of user devices connected to a remote processing unit through a network according to an embodiment herein;

[0021] FIG. 2 illustrates an exploded view of a remote processing unit according to an embodiment herein;

[0022] FIG. 3 illustrates an exemplary view that depicts work station clustering to share works assigned to the user device using the remote processing unit according to an embodiment herein;

[0023] FIG. 4 illustrates a schematic diagram of one or more user devices are connected to one or more processing stations through one or more remote processing units according to an embodiment herein;

[0024] FIG. 5 illustrates an architecture of the remote processing unit of FIG. 4 according to an embodiment herein;

[0025] FIG. 6 is a flow diagram that illustrates a method for predicting the behavior of the user devices and transmitting the commands to the network connected devices according to an embodiment herein;

[0026] FIG. 7 is a flow diagram that illustrates a method for accessing the remote processing unit by the plurality of user devices in specific to consumer gadgets according to an embodiment herein;

[0027] FIG. 8 is a flow diagram that illustrates a method for the remote processing unit receiving processing requirements from a cloud according to an embodiment herein;

[0028] FIG. 9 is a flow diagram that illustrates a method for the remote processing unit transmits the processing requirements to the cloud according to an embodiment herein;

[0029] FIG. 10 is a flow diagram that illustrates a method for transmission done when the remote processing unit is not within a final service device and local processor send processing requirements to the remote processing unit according to an embodiment herein;

[0030] FIG. 11 is a flow diagram that illustrates method for accessing the remote processing unit by the user devices in a subscription manner in specific to machines and devices according to an embodiment herein;

[0031] FIG. 12 illustrates an exploded view of the receiver employed in user devices according to an embodiment herein; and

[0032] FIG. 12 illustrates a hardware configuration of a computer architecture/computing device, according to an embodiment herein. DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0033] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0034] Various embodiments herein provide a system and method for automatic functioning of the devices that predicts the future operating condition of the network connected devices and also learns from previous situations and automatically execute the desired functions. Further the method includes a remote processing unit which can be leased/accessed by the other user device which reduces the requirement of individual high-end processing unit or computing devices thereby reducing the overall cost and maintenance cost of the user devices. Referring now to the drawings and more particularly to FIG. 1 to FIG. 13, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments

[0035] FIG. 1 illustrates a schematic diagram of user devices connected to a remote processing unit through a network according to an embodiment herein. The schematic diagram includes one or more user devices 104A-104N and a remote processing unit 102. The one or more user devices 104A-104N is connected with the remote processing unit 102 through a network 106. The one or more user devices may include, but not limited to large machining devices such as CNC machines, automated Lathe machines, industrial product designing equipment's, bio medical instruments such as MRI scanner, CT scanner and computing devices such as mobile phone, personal computer, laptop, notebook, PDA etc. and the like. The one or more user devices 104A-104N communicates with the remote processing unit 102 using wired or wireless communication such Bluetooth, Wi-Fi, ZigBee, mobile communication network. In an embodiment, the one or more user devices 104A-104N communicates with the remote processing unit 102 using high speed communication channels. The remote processing unit 102 acts as the common processing unit for the one or more user devices 104A-104N. The one or more user devices 104A-104N access the remote processing unit 102 for performing certain tasks or function. In an embodiment, the remote processing unit 102 is accessed by the one or more user devices 104A-104N on a rental basis. The user is allowed to register their user devices with the remote processing unit 102 for accessing the remote processing unit 102. In one embodiment, each user devices includes a unique identification number. The one or more user devices 104A-104N connected to the remote processing unit 102 to automatically perform one or more functions using the remote processing unit 102. In an embodiment, the user is allowed to subscribe the remote processing unit 102 on monthly subscription packages. The user is also allowed to make payment immediately after the usage of the remote processing unit 102. The remote processing unit 102 receives one or more input data from the one or more user devices 104A-104N and process the input data and transmits one or more executable commands to the one or more user devices 104A-104N to perform the tasks assigned to the one or more user devices 104A-104N. In an embodiment, the tasks include executing certain function in the large machining devices such as CNC machines, automated Lathe machines, industrial product designing equipment's, bio medical instruments such as MRI scanner, CT scanner etc. In an embodiment, the tasks may also include one or more computing tasks performed by the computing devices such as mobile phone, personal computer, laptop, notebook, PDA etc. and the like. In an embodiment, the remote processing unit 102 is also capable of predicting the future processing requirement by analyzing the behavior of the one or more user devices 104A-104N. The remote processing unit 102 transmits necessary control commands to the one or more user devices 104A-104N based on the behavior of the one or more user devices 104A-104N. In one embodiment, the remote processing unit 102 analyzes the behavior of the one or more user devices 104A-104N and compares the behavior with the previous processing requirements and issues necessary executable commands to the one or more user devices 104A-104N to perform the expected tasks.

[0036] FIG. 2 illustrates an exploded view of a remote processing unit 102, according to an embodiment herein. The remote processing unit 102, includes a database 202, an input data receiving unit 204, An Artificial Intelligence Engine 206 (AI Engine), Executable commands transmitting unit 212 and machine feedback receiving unit 214. The Artificial Intelligence Engine 206 includes Machine behavior predicting unit 208 and Executable commands generating unit 210. The input data receiving unit 204 receives one or more input data from the one or more user devices 104A-104N and stores the input data in the data base 202. The input data includes identification number for the one or more user devices 104A-104N or request for accessing the remote processing unit 102 or request for registering the user devices into the remote processing unit 102. The input data further includes one or more performance data of a user device 104A. In one embodiment, while registering the user device 104 into the network, the input data receiving unit 204 receives one or more data's such as device specification, device parameters etc. from the one or more user devices 104A-104N and allocates a special identification number to the registered user devices 104. The one or more user devices 104A-104N uses the identification number (device ID) for accessing the remote processing unit 102 and/or associate with the remote processing unit 102 to perform the tasks assigned to the one or more user devices 104A-104N.

[0037] The Artificial Intelligence Engine 206 includes machine behavior predicting unit 208 for forecasting the future behavior of the one or more user devices 104A-104N. The machine behavior predicting unit 208 receives one or more machine feedback data from the Machine feedback receiving unit 214 and analyzes the present feedback data with the past behavioral data to forecast the future processing requirements of the plurality of user devices 104A-104N. In one embodiment, the Artificial Intelligence Engine 206 generates one or more executable commands based on the forecasted future processing requirements using the executable commands generating unit 210. The Executable commands transmitting unit 212 transmits the executable commands to the one or more user devices 104A-104N. In an embodiment, the remote processing unit 102 includes data tracking units for tracking the number of user devices accessing the remote processing unit 102. The data tracking unit estimates the usage cost for accessing the remote processing unit 102 by the one or more user devices. 104A- 104N. In one embodiment, the remote processing unit 102 assigned to the single user device 104 A to performs/execute specific task. In another embodiment, the remote processing unit 102 includes an identification code to pair the remote processing unit 102 with the user device 104 A through the network. [0038] In one embodiment, the remote processing unit 102 includes one or more configurations to perform one or more tasks. The configurations of the remote processing unit 102 is selected by the user or automatically chosen based on the specifications of the user device 104A.

[0039] FIG. 3 illustrates an exemplary view that depicts work station clustering to share works assigned to the user device 104 A using the remote processing unit 102 according to an embodiment herein. The exemplary view illustrates how a processing request is processed by the remote processing unit 102. The exemplary view of remote processing unit 102 includes one or more processing stations PS l-PSn connected in the network 106. The one or more user devices 104A-N requests the remote processing unit 102 to perform one or more tasks. The remote processing unit 102 receives the request, using input data receiving unit 204 and assigns the request to the plurality of processing stations PSl-PSn connected in the network. In one embodiment, the processing stations PS 1 -PSn supports one or more applications and one or more work environments.

[0040] The user device 104A with one or more functions, request the remote processing unit 102 for executing the one or more functions. The one or more functions may include performing certain calculation, or any arithmetical or logical functions etc. The remote processing unit 102 receives the request using input data receiving unit 204 and stores the data temporarily in the database 202. The remote processing unit 102 allocates the one or more functions to different processing stations that are connected to the remote processing unit 102. The processing Station PS 1 receives one or more functions and identifies the priority, the nature of the function and distributes the one or more functions to the one or more processing stations PS2-PSn connected in the network 106. The one or more processing stations PS2-PSn process the functions and transmits the processed function to the processing station PS 1. The processing station PS1 collects the processed functions from the one or more processing stations PSl-PSn and compile the processed functions. The processing station PS1 stores the combined processed functions in the database 202 of the remote processing unit 102. The remote processing unit 102 receives the combined processed functions and generates one or more executable commends for the user device 104 to perform certain tasks. In one embodiment, the remote processing unit 102 assigns the shared work to the one or more user devices 104A-N connected through the network 106. [0041] FIG. 4 illustrates a schematic diagram of the one or more user devices 104A-N are connected to the one or more processing stations PSl-PSn through one or more remote processing units 102A-N according to an embodiment herein. The schematic diagram includes the one or more remote processing units 102A-N, the one or more user devices 104A-N, the one or more processing stations PSl-PSn, a user 402 and a communication driver/interface 404. The one or more remote processing units 102A-N communicates with the one or more processing stations PSl-PSn. In one embodiment, the one or more remote processing units 102A-N communicates with each other to form a network cluster. The one or more remote processing units 102A-N communicates with (i) the one or more user devices 104A-N which is an interface device through a wired or wireless communication device, (ii) machines through the communication driver 404 and (ii) the user 402 through one or more wireless device.

[0042] FIG. 5 illustrates an architecture of the remote processing unit 102 A of FIG. 4 according to an embodiment herein. The architecture includes a processor 502, a first communication medium 504, I/O digital/analog data bus 506, a communication driver 508, a second communication medium 510, a peripheral control 512, a driver IC 514 and one or more appliances 516. The processor 502 communicates the processing requirements to a cloud using the first communication medium 504. The processor 502 receives input/output of one or more protocols in a digital /analog format. The processor 502 communicates the processing requirements to end device using wired/wireless communication via the communication driver 508 based on requirements of the end device. The processor 502 processes the processed data the executable commands and communicates to the user 402 via one or more protocols through wired or wireless communication based on at least one of (i) user request or (ii) automatic user requirement identification.

[0043] FIG. 6 is a flow diagram that illustrates a method for predicting the behavior of a user devices and transmitting the commands to the one or more user devices 104A-N according to an embodiment herein. At step 602, the user device 104A transmits a processing request to a remote processing unit 102 using a transmitting hardware. In one embodiment, the transmitting hardware is a modem. In one embodiment, the transmitting hardware connects the user device 104A and the remote processing unit 102 over the network 106. At step 604, the transmitting hardware transmits the processing requests to the remote processing unit 102 through a communication channel. At step 606, the AI engine in the remote processing unit 102, receives the processing request along with the input data and allocates the processing request to the one or more processing stations PSl-PSn connected in the network. At step 608, the AI engine 206 collects the processed functions from the one or more processing stations PSl-PSn and predict the behavior of the one or more the user devices 104A-104n. At step 610, the AI engine 206 generates one or more executable commands for the one or more user devices 104A-104N. At step 612, transmitting the one or more executable commands to the one or more user devices 104A-104N. At step 614, the one or more user devices 104A-104N performs one or more tasks by executing the one or more executable commands. In one embodiment, data transactions are made to occur only with paired clients and server with end to data encryption with algorithms like block chain and alike.

[0044] In an exemplary embodiment, the method for predicting the present and future processing requirement of the user device 104A with a plurality of sensors connected in the network 106. The user device 104A includes a plurality of sensors connected in the network 106 for monitoring the present and future processing requirement of the user device 104A. The Artificial Intelligence Engine 206 in the remote processing unit 102 access a particular set of sensors for predicting the future processing requirement of the user device 104A. In one embodiment, the remote processing unit 102, at certain instances, may access the remaining sensors to validate the predicted processing requirement the user device 104A.

[0045] FIG. 7 is a flow diagram that illustrates a method for accessing the remote processing unit by the one or more user devices 104A-N in specific to consumer gadgets according to an embodiment herein. The one or more user devices 104A-N receive one or more input data from the user and transmits the processing request to the remote processing unit 102. At step 702, user manually enters the login details in the user interface of the user device by entering the device Id provided to the user device at the time of registering. The network is also capable of automatically identifying the user devices when they are connected to the network 106. At step 704, the user interface displays an entry screen for the user to enter the required configuration. At step 706, the user enters the required configuration on the user interface. The user device automatically displays the required configuration by analyzing the previous subscription made by the user. At step 708, the user device automatically estimates the cost per usage based on the usage. At step 710, the user device automatically generates necessary functions based on the usage or the user is allowed to customize the configuration based on the user preference. In one embodiment, all data transactions are made to occur only with the paired clients and the server with end to data encryption with the algorithms like the block chain and the alike.

[0046] FIG. 8 is a flow diagram that illustrates a method for the remote processing unit 102 receiving processing requirements from a cloud according to an embodiment herein. At step 802, data is requested from appropriate server. At step 804, an encrypted data or the executable commands are received and performed error rectification. At step 806, an information is decoded or in turn connected with one or more servers which provides Iaas, Paas, Daas and Saas using the remote processing device 102. At step 808, the processed data the executable commands are sent to the user 402 via one or more protocols through wired or wireless communication based on at least one of (i) user request or (ii) automatic user requirement identification.

[0047] FIG. 9 is a flow diagram that illustrates a method for the remote processing unit 102 transmits the processing requirements to the cloud according to an embodiment herein. At step 902, the user or machine requirements are received from at least one of (i) IoT devices with user interface (UI) or (b) communication via the one or more protocols. At step 904, nature of processing to be done and CPU load required are identified. At step 906, the processing requirements are distributed to one or more servers paired with the user 402 via multiple protocols support directly or through the routing device.

[0048] FIG. 10 is a flow diagram that illustrates a method for transmission done when the remote processing unit 102A is not within a final service device and local processor send processing requirements to the remote processing unit 102A according to an embodiment herein. At step 1002, end device requirements are received via communication channels via multiple protocol. At step 1004, the communication driver 404 are communicated with the one or more remote processing units 102A-N using the wired/wireless communication. At step 1006, the user or machine requirements are received from at least one of (i) IoT devices with user interface (UI) or (b) communication via the one or more protocols. At step 1008, the nature of processing to be done and CPU load required are identified. At step 1010, the processing requirements are distributed to one or more servers paired with the user 402 via multiple protocols support directly or through the routing device.

[0049] FIG. 11 is a flow diagram that illustrates method for accessing the remote processing unit by the user devices in a subscription manner in specific to machines and devices according to an embodiment herein. At step 1102, the user device 104 A is identified by the remote processing unit 102 by identifying the device id provided to the user device 104 A. At step 1104, the remote processing unit 102 automatically identifies processing requirement of the user device 104 A by identifying the device id. At step 1106, the remote processing unit 102 searches for any other user devices program/requirements for using the device id and working as a part of other user requirements. At step 1108, the remote processing unitl02 estimates the cost of usage by based on the time or based on the processing load accessed by the user device 104A. At step 1110, the remote processing unit 102 displays the amount of usage and asks the user to make payment or to select monthly subscription package. At step 1112, the remote processing unit 102 receives the processing request and sends the processing request to the one or more the processing stations through the network 106 or through any other network owned by the third- party network. In one embodiment, all data transactions are made to occur only with the paired clients and the server with end to data encryption with the algorithms like the block chain and the alike.

[0050] FIG. 12 illustrates an exploded view of the receiver employed in the user devices 104A-N according to an embodiment herein. The receiver includes a memory 1202 having a set of instructions, a bus 1204, a display 1206, a speaker 1208, and a processor 1210 capable of processing the set of instructions to perform any one or more of the methodologies herein, according to an embodiment herein. The processor 1210 may also enable digital content to be consumed in the form of video for output via one or more displays 1206 or audio for output via speaker and/or earphones 1208. The processor 1210 may also carry out the methods described herein and in accordance with the embodiments herein

[0051] Digital content may also be stored in the memory 1202 for future processing or consumption. The memory 1202 may also store program specific information and/or service information (PSI/SI), including information about digital content (e.g., the detected information bits) available in the future or stored from the past. A user of the receiver may view this stored information on display 1206 and select an item of for viewing, listening, or other uses via input, which may take the form of keypad, scroll, or other input device(s) or combinations thereof. When digital content is selected, the processor 1210 may pass information. The content and PSI/SI may be passed among functions within the receiver using the bus 1204. [0052] The techniques provided by the embodiments herein may be implemented on an integrated circuit chip (not shown). The chip design is created in a graphical computer programming language, and stored in a computer storage medium (such as a disk, tape, physical hard drive, or virtual hard drive such as in a storage access network). If the designer does not fabricate chips or the photolithographic masks used to fabricate chips, the designer transmits the resulting design by physical means (e.g., by providing a copy of the storage medium storing the design) or electronically (e.g., through the Internet) to such entities, directly or indirectly.

[0053] The stored design is then converted into the appropriate format (e.g., GDSII) for the fabrication of photolithographic masks, which typically include multiple copies of the chip design in question that are to be formed on a wafer. The photolithographic masks are utilized to define areas of the wafer (and/or the layers thereon) to be etched or otherwise processed.

[0054] The resulting integrated circuit chips can be distributed by the fabricator in raw wafer form (that is, as a single wafer that has multiple unpackaged chips), as a bare die, or in a packaged form. In the latter case the chip is mounted in a single chip package (such as a plastic carrier, with leads that are affixed to a motherboard or other higher level carrier) or in a multichip package (such as a ceramic carrier that has either or both surface interconnections or buried interconnections). In any case the chip is then integrated with other chips, discrete circuit elements, and/or other signal processing devices as part of either (a) an intermediate product, such as a motherboard, or (b) an end product. The end product can be any product that includes integrated circuit chips, ranging from toys and other low-end applications to advanced computer products having a display, a keyboard or other input device, and a central processor.

[0055] The embodiments herein can take the form of, an entirely hardware embodiment, an entirely software embodiment or an embodiment including both hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. Furthermore, the embodiments herein can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0056] The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random-access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk - read only memory (CD-ROM), compact disk - read/write (CD-R/W) and DVD.

[0057] A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.

[0058] Input/output (I/O) devices (including but not limited to keyboards, displays, pointing devices, remote controls, etc.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.

[0059] A representative hardware environment for practicing the embodiments herein is depicted in FIG. 13. This schematic drawing illustrates a hardware configuration of a computer architecture/computer system (e.g. the remote processing unit 102) in accordance with the embodiments herein. The system comprises at least one processor or central processing unit (CPU) 10. The CPUs 10 are interconnected via system bus 12 to various devices such as a random access memory (RAM) 14, read-only memory (ROM) 16, and an input/output (I/O) adapter 18. The I/O adapter 18 can connect to peripheral devices, such as disk units 11 and tape drives 13, or other program storage devices that are readable by the system. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein.

[0060] The system further includes a user interface adapter 19 that connects a keyboard 15, mouse 17, speaker 24, microphone 22, and/or other user interface devices such as a touch screen device (not shown) or a remote control to the bus 12 to gather user input. Additionally, a communication adapter 20 connects the bus 12 to a data processing network 25, and a display adapter 21 connects the bus 12 to a display device 23 which may be embodied as an output device such as a monitor, printer, or transmitter, for example.

[0061] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the appended claims.