DESC:
FIELD OF THE INVENTION
[0001] The present disclosure relates to Internet of Things (IoT), and more particularly, the present disclosure relates to a polynomial regression system and method that performs polynomial regression on captured parameters associated with one or more device to predict trends or future trends in real-time.

BACKGROUND OF THE INVENTION
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Internet of Things (IoT) is a network of uniquely-identifiable, purposed “things” that are enabled to communicate data pertaining thereto, there between, over a communications network whereby, the communicated data form a basis for manipulating the operation of the “things”. The “thing” in the “Internet of Things” could virtually be anything that fits into a common purpose thereof. For example, a “thing” could be a person with a heart rate monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert its driver when tire pressure is low, or the like, or any other natural or man-made entity that can be assigned a unique IP address and provided with the ability to transfer data over a communication network. Notably, if all the entities in an IoT are machines, then the IoT is referred to as a “Machine to Machine” (M2M) IoT or simply, as M2M IoT.
[0004] It is apparent from the aforementioned examples that an entity becomes a “thing” of an M2M IoT especially, when the entity is attached with one or more sensors capable of capturing one or more types of data pertaining thereto: segregating the data (if applicable); selectively communicating each segregation of data to one or more fellow “things”; receiving one or more control commands (or instructions) from one or more fellow “things” wherein, the one or more control commands is based on the data received by the one or more fellow “things”; and executing one or more commands resulting in the manipulation or “management” of the operation of the corresponding entity. Therefore, in an IoT-enabled system, the “things” basically manage themselves without any human intervention, thus drastically improving the efficiency thereof.
[0005] US Patent publication 2014/0336791 A1 discusses a predictive maintenance of industrial systems using big data analysis in a cloud platform. The service analyzes data gathered from multiple customers across different industries to identify operational trends as a function of industry type, application type, equipment in use, device configurations, and other such variables. Based on results of the analysis, the predictive maintenance service predicts anticipated device failures or system inefficiencies for individual customers. Notification services alert the customers of impending failures or inefficiencies before the issues become critical. The cloud-based notification services also notify appropriate technical support entities to facilitate proactive maintenance and device management.
[0006] U.S. Pat. No. 8,560,368 B1 discusses constraint-based scheduling, and in particular, constraint-based scheduling of one or more components for maintenance based on both, time-based maintenance information and condition-based maintenance information. An automated constraint-based scheduler that uses condition-based information is disclosed. A scheduler module generates a maintenance schedule for one or more components. The scheduler module receives time-based maintenance information associated with a component and condition-based maintenance information that identifies a determined condition of the component. A plurality of constraints associated with performing maintenance on the component is determined. The scheduler module uses a constraint-based scheduler to generate a maintenance schedule for the component based on the time-based maintenance information, the condition-based maintenance information, and the plurality of constraints.
[0007] U.S. Pat. No. 6,405,108 B1 discusses a system and process for developing diagnostic algorithms for predicting impending failures of the subsystems in a locomotive. A process and system for developing an algorithm for predicting failures in a system, such as a locomotive, having a plurality of subsystems is provided. The process allows for conducting a failure mode analysis for a respective subsystem so as to identify target failure modes of the subsystem and/or collecting expert data relative to the respective subsystem. The process includes a step for identifying, based on the identified failure modes and/or the collected expert data, one or more signals to be monitored for measuring performance of the respective subsystem. A generating step allows for generating, based on the monitored signals, a predicting signal indicative of the presence of the identified target failure modes in the respective subsystem
[0008] WIPO application WO2005086760 A2 discusses a method and system, for monitoring and maintaining equipment and machinery, as well as any other device or system that has discrete measuring points that can be gathered and analyzed to determine the status of the device or the system.
[0009] Visualization of analytical results or processed data from big data system poses several new challenges in terms of scalability, volume and velocity. Besides the results must be interpreted to the users who are technicians and not familiar with many of the advanced sensor data analytics. Therefore visualization of the predictive maintenance results must be auto-interpreted to factory technicians using simple normalized gauge scale concept. None of the prior art technologies emphasize on the visualization of the processed analytic data of predictive maintenance when obtained as a result of complex machine learning calculation.
[0010] However, existing prior art technologies are limited to rule based engines. Mere rule based engines do not provide effective visualization of the equipment monitoring data which is critical for operational deployment of predictive maintenance systems. Further, mere rule based engines may not be sufficient to help operators in handling multiple organ failure in machines. Further, the above prior art technologies does not allow scalability in order to handle large volumes of data and therefore not capable of providing solution for IoT based predictive maintenance system. Furthermore, using machine learning with IoT devices is almost unheard of in the current market. This is because machine learning is regarded as something that requires a large data size, or a huge amount of processing power. Not all Machine learning algorithms require a high amount of processing power or very large data sets. Though tuning the Machine learning algorithm or regressor, is just as difficult. Additionally, there are mechanismming tools (Such as Matlab or Python libraries) available that help the user to implement the Polynomial regression on a given data. By using conventional mechanismming tools if the user wants to improve the predicted trend in a real-time manner, he has to write the mechanism by himself. Also linking the output of an IoT enabled sensor to this mechanism is quite difficult, and all prior implementations are neither scalable nor commercially available. Visualizing the output requires a different set of tools. Software such as Matlab and python libraries allows users to visualize the output, but it is often complicated. The correct usage of any Machine learning algorithm requires the user to tune the Algorithm parameters to the type of data set. Under tuning and over tuning tend to be very common problems, and without writing the code for a visualizer that enables the user to conclude that the algorithm is under tuned or overturned, is very difficult.
[0011] It is evident from the discussion of the aforementioned prior arts that none of the prior arts pave way for predictive maintenance of a machine through an IoT system based classification and providing effective visualization to a machine operator. Therefore, there exists a need in the art for a solution to the aforementioned problem. Further there is a need of a new, efficient and improved polynomial regression system and method that performs polynomial regression to predict trends in real-time for one or more device based on captured parameters.

SUMMARY
[0012] The present disclosure relates to Internet of Things (IoT), and more particularly, the present disclosure relates to a polynomial regression system and method that performs polynomial regression on captured parameters associated with one or more device to predict trends or future trends in real-time.
[0013] An aspect of the present disclosure relates to an Internet of Things (IoT) based polynomial regression system implemented for real time predictive maintenance. The system includes a plurality of sensors secured to a machine, the sensors being configured to capture one or more parameters associated with the machine in real-time; a polynomial regression visualizer configured to retrieve said captured parameters from the sensors to perform polynomial regression in real-time to predict trends for said machines based on said captured parameters; a parameter update unit configured to update one or more parameters selected from said captured parameters; and a control module including a hardware processor and a memory coupled to the hardware processor, the control module disposed in operative communication with a control panel of the machine, the control module receiving over a communications network control commands from the polynomial regression visualizer, and executing the control commands resulting in process control or predictive maintenance of the machine.
[0014] In an aspect, the polynomial regression visualize provides prediction history and prediction trend, and wherein the parameter update unit compares the prediction history with the prediction trend to monitor a variation in data points and update the one or more parameters selected from said captured parameters based upon the variation in the data points.
[0015] In an aspect, the system includes a user interface configured to display visual representation associated with working of said polynomial regression when said updated parameters are used.
[0016] In an aspect, the visual representation is a graphical representation of the prediction trend and the prediction history.
[0017] In an aspect, the polynomial regression visualizer includes a machine learning engine configured to classify the data captured from the sensors.
[0018] In an aspect, the machine learning engine is associated with at least one of physics based model, a rule based model and a vector classifier model.
[0019] In another aspect, the present disclosure relates to an Internet of Things (IoT) based polynomial regression method implemented for real time predictive maintenance. The method includes capturing, at a plurality of sensors, one or more parameters associated with one or more machines in real-time. The method further includes retrieving, at a polynomial regression visualizer, said captured parameters to perform, in real-time, polynomial regression to predict trends for said machines based on said captured parameters. The method further includes updating, at a parameter update unit, one or more parameters selected from said captured parameters. The method further includes receiving, at a control module, control commands from the polynomial regression visualizer, and executing the control commands resulting in process control or predictive maintenance of the machine.
[0020] In an aspect, the polynomial regression visualize provides prediction history and prediction trend, and wherein the parameter update unit compares the prediction history with the prediction trend to monitor a variation in data points and update the one or more parameters selected from said captured parameters based upon the variation in the data points.
[0021] In an aspect, the method includes includes classifying, at a machine learning engine of the polynomial regression visualize, the data captured from the sensors.
[0022] In an aspect, the machine learning engine is associated with at least one of physics based model, a rule based model and a vector classifier model.
[0023] Various objects, features, aspects and advantages of the present disclosure will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like features.
[0024] Within the scope of this application it is expressly envisaged that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments, unless such features are incompatible.

BRIEF DESCRIPTION OF THE DRAWINGS
[0025] The novel features which are believed to be characteristic of the device according to the present disclosure, as to their structure, organization, use, and method of operation, together with further objectives and advantages thereof, will be better understood from the following drawings in which an embodiment of the disclosure will now be illustrated by way of example. It is expressly understood, however, that the drawings are for the purpose of illustration and description only, and are not intended as a definition of the limits of the invention.
[0026] FIG. 1 illustrates exemplary implementation architecture of the proposed system, in accordance with an embodiment of the present invention.
[0027] FIG. 2 illustrates exemplary functional modules, in accordance with an aspect of the present disclosure.
[0028] FIG. 3 illustrates an exemplary flow diagram of the present system, in accordance with an embodiment of the present disclosure.
[0029] FIGs. 4A-C illustrates an exemplary working of a proposed system, in accordance with an embodiment of the present disclosure.
[0030] FIG. 5 illustrates an exemplary computer system in which or using which aspects of the present disclosure can be implemented.

DETAILED DESCRIPTION
[0031] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[0032] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0033] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, and firmware and/or by human operators.
[0034] Various methods described herein may be practiced by combining one or more machine-readable storage media containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing various embodiments of the present invention may involve one or more computers (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, routines, subroutines, or subparts of a computer program product.
[0035] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0036] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0037] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. These exemplary embodiments are provided only for illustrative purposes and so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. The invention disclosed may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Various modifications will be readily apparent to persons skilled in the art. The general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure). Also, the terminology and phraseology used is for the purpose of describing exemplary embodiments and should not be considered limiting. Thus, the present invention is to be accorded the widest scope encompassing numerous alternatives, modifications and equivalents consistent with the principles and features disclosed. For purpose of clarity, details relating to technical material that is known in the technical fields related to the invention have not been described in detail so as not to unnecessarily obscure the present invention.
[0038] Thus, for example, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named element.
[0039] Embodiments of the present invention may be provided as a computer program product, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) to perform a process. The term “machine-readable storage medium” or “computer-readable storage medium” includes, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).A machine-readable medium may include a non-transitory medium in which data may be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory, memory or memory devices. A computer-program product may include code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.
[0040] Furthermore, embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a machine-readable medium. A processor(s) may perform the necessary tasks.
[0041] Systems depicted in some of the figures may be provided in various configurations. In some embodiments, the systems may be configured as a distributed system where one or more components of the system are distributed across one or more networks in a cloud computing system.
[0042] Each of the appended claims defines a separate invention, which for infringement purposes is recognized as including equivalents to the various elements or limitations specified in the claims. Depending on the context, all references below to the "invention" may in some cases refer to certain specific embodiments only. In other cases it will be recognized that references to the "invention" will refer to subject matter recited in one or more, but not necessarily all, of the claims.
[0043] All methods described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[0044] Various terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0045] Technical problems to be solved in the present invention are that: currently, in the prior-art technologies, usage of machine learning technologies for IoT devices is almost an unheard area of technology, since it is understood that machine learning is regarded as something that requires a large data size, or a huge amount of processing power. Further, if the user wants to improve the predicted trend in a real-time manner, the user himself has to write a new separate mechanism. Also, linking the output of an IoT enabled sensor to this mechanism is quite difficult, and all prior implementations are neither scalable nor commercially available.
[0046] To solve the above recited and other available technical problems in the prior-art, the present invention provides the following solution:
[0047] This invention provides a new, efficient, and improved a polynomial regression system and method that performs polynomial regression to predict trends in real-time for one or more device based on captured parameters.
[0048] Embodiments of the present disclosure provide an efficient, effective, reliable, improved system. Further, the present disclosure relates to a polynomial regression system and method that performs polynomial regression on captured parameters associated with one or more device to predict trends or future trends in real-time.
[0049] Accordingly, an aspect of the present disclosure relates to a polynomial regression system that include a plurality of sensors, a polynomial Regression Visualizer and a parameter update module. In an aspect, the sensors can capture/sense one or more parameters associated with one or more devices in real-time. In an aspect, the polynomial regression visualizer can retrieve said captured parameters to perform polynomial regression in real-time to predict trends for said one or more devices based on said captured parameters. In an aspect, the parameter update module can update one or more parameters selected from said captured parameters.
[0050] In an aspect, the polynomial regression system further can include a user interface configured to display visual representation associated with working of said polynomial regression when said updated parameters are used.
[0051] In an aspect, visual representation associated with working of said polynomial regression can be preferably represented in the form of graph.
[0052] In an aspect, the polynomial regression system can include a machine learning engine. In another aspect, the polynomial regression system can classify the sensor data through the machine learning engine.
[0053] In an aspect, the machine learning engine can be associated with at least one of a physics based model, a rule based model and a vector classifier model.
[0054] An aspect of the present disclosure relates to a method. The method include the steps of: capturing/sensing, at a plurality of sensors ,in real-time, one or more parameters associated with one or more devices and retrieving, at a polynomial regression visualizer, said captured parameters to perform, in real-time, polynomial regression to predict trends for said one or more devices based on said captured parameters.
[0055] FIG. 1 illustrates exemplary implementation architecture of the proposed system 104 in accordance with an embodiment of the present invention. In an embodiment, the proposed system 104 is a polynomial regression system 104. The present disclosure relates to Internet of Things (IoT), and more particularly, the present disclosure relates to a polynomial regression system and method to perform polynomial regression to predict trends in real-time for one or more device based on captured parameters.
[0056] In an implementation, said system 104 can be embedded with/incorporated with one or more Internet of Things (IoT) devices. In a typical network architecture of the present disclosure can include a plurality of network devices such as transmitter, receivers, and/or transceivers that may include one or more IoT devices.
[0057] As used herein, the IoT devices can be a device that includes sensing and/or control functionality as well as a WiFi™ transceiver radio or interface, a Bluetooth™ transceiver radio or interface, a Zigbee™ transceiver radio or interface, an Ultra-Wideband (UWB) transceiver radio or interface, a Wi-Fi-Direct transceiver radio or interface, a Bluetooth™ Low Energy (BLE) transceiver radio or interface, and/or any other wireless network transceiver radio or interface that allows the IoT device to communicate with a wide area network and with one or more other devices. In some embodiments, an IoT device does not include a cellular network transceiver radio or interface, and thus may not be configured to directly communicate with a cellular network. In some embodiments, an IoT device may include a cellular transceiver radio, and may be configured to communicate with a cellular network using the cellular network transceiver radio.
[0058] IoT devices may include home or industrial automation network devices that allow a user to access, control, and/or configure various home or industrial appliances located within the user's home (e.g., a television, radio, light, fan, humidifier, sensor, microwave, iron, and/or the like), or outside of the user's home (e.g., exterior motion sensors, exterior lighting, garage door openers, sprinkler systems, or the like), or within a user’s industry or factory (e.g., boiler, heater, condenser, HVAC, etc). Network device may include a automation switch that may be coupled with a home or industrial appliance. In some embodiments, network devices may be used in other environments, such as a business, a school, an establishment, a park, or any place that can support a local area network to enable communication with network devices. For example, a network device can allow a user to access, control, and/or configure devices, such as office-related devices (e.g., copy machine, printer, fax machine, or the like), audio and/or video related devices (e.g., a receiver, a speaker, a projector, a DVD player, a television, or the like), media-playback devices (e.g., a compact disc player, a CD player, or the like), computing devices (e.g., a home computer, a laptop computer, a tablet, a personal digital assistant (PDA), a computing device, a wearable device, or the like), lighting devices (e.g., a lamp, recessed lighting, or the like), devices associated with a security system, devices associated with an alarm system, devices that can be operated in an automobile (e.g., radio devices, navigation devices), and/or the like.
[0059] A user may communicate with the network devices using an access device that may include any human-to-machine interface with network connection capability that allows access to a network. For example, the access device may include a stand-alone interface (e.g., a cellular telephone, a smartphone, a home computer, a laptop computer, a tablet, a personal digital assistant (PDA), a computing device, a wearable device such as a smart watch, a wall panel, a keypad, or the like), an interface that is built into an appliance or other device e.g., a television, a refrigerator, a security system, a game console, a browser, or the like), a speech or gesture interface (e.g., a Kinect™ sensor, a Wiimote™, or the like), an IoT device interface (e.g., an Internet enabled device such as a wall switch, a control interface, or other suitable interface), or the like. In some embodiments, the access device may include a cellular or other broadband network transceiver radio or interface, and may be configured to communicate with a cellular or other broadband network using the cellular or broadband network transceiver radio. In some embodiments, the access device may not include a cellular network transceiver radio or interface.
[0060] User may interact with the network devices using an application, a web browser, a proprietary mechanism, or any other mechanism executed and operated by the access device. In some embodiments, the access device may communicate directly with the network devices (e.g., communication signal). For example, the access device may communicate directly with network devices using Zigbee™ signals, Bluetooth™ signals, WiFi™ signals, infrared (IR) signals, UWB signals, WiFi-Direct signals, BLE signals, sound frequency signals, or the like. In some embodiments, the access device may communicate with the network devices via the gateways and/or a cloud network.
[0061] Local area network may include a wireless network, a wired network, or a combination of a wired and wireless network. A wireless network may include any wireless interface or combination of wireless interfaces (e.g., Zigbee™, Bluetooth™, WiFi™, IR, UWB, WiFi-Direct, BLE, cellular, Long-Term Evolution (LTE), WiMax™, or the like). A wired network may include any wired interface (e.g., fiber, Ethernet, powerline, Ethernet over coaxial cable, digital signal line (DSL), or the like). The wired and/or wireless networks may be implemented using various routers, access points, bridges, gateways, or the like, to connect devices in the local area network. For example, the local area network may include gateway and gateway. Gateway can provide communication capabilities to network devices and/or access device via radio signals in order to provide communication, location, and/or other services to the devices. The gateway is directly connected to the external network and may provide other gateways and devices in the local area network with access to the external network. The gateway may be designated as a primary gateway.
[0062] The network access provided by gateway may be of any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols. For example, gateways may provide wireless communication capabilities for the local area network 100 using particular communications protocols, such as WiFi™ (e.g., IEEE 802.11 family standards, or other wireless communication technologies, or any combination thereof). Using the communications protocol(s), the gateways may provide radio frequencies on which wireless enabled devices in the local area network can communicate. A gateway may also be referred to as a base station, an access point, Node B, Evolved Node B (eNodeB), access point base station, a Femtocell, home base station, home Node B, home eNodeB, or the like.
[0063] Gateways may include a router, a modem, a range extending device, and/or any other device that provides network access among one or more computing devices and/or external networks. For example, gateway may include a router or access point or a range extending device. Examples of range extending devices may include a wireless range extender, a wireless repeater, or the like.
[0064] A router gateway may include access point and router functionality, and may further include an Ethernet switch and/or a modem. For example, a router gateway may receive and forward data packets among different networks. When a data packet is received, the router gateway may read identification information (e.g., a media access control (MAC) address) in the packet to determine the intended destination for the packet. The router gateway may then access information in a routing table or routing policy, and may direct the packet to the next network or device in the transmission path of the packet. The data packet may be forwarded from one gateway to another through the computer networks until the packet is received at the intended destination.
[0065] Referring now to FIG. 1, in an embodiment, FIG. 1 indicates a network implementation 100 of the polynomial regression system 104. Although the present subject matter is explained considering that the polynomial regression system 104 is implemented as an application on a server 102, it may be understood that the polynomial regression system 104 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a server, a network server, a cloud-based environment and the like. It would be appreciated that the proposed polynomial regression system 104 may be accessed by multiple users 110-1, 110-2…110-N (collectively referred to as users 110 and individually referred to as the user 110 hereinafter), through one or more computing devices 108-1, 108-2…108-N (collectively referred to as computing devices 108 hereinafter), or applications residing on the computing devices 108. In an aspect, the proposed polynomial regression system 104 can be operatively coupled to a website and so be operable from any Internet enabled computing device 108. Examples of the computing devices 108 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The computing devices 108 are communicatively coupled to the proposed polynomial regression system 104 through a network 106. It may be also understood that the proposed polynomial regression system 104 is a wearable device to be worn by a child and having wearable technology, wearable, fashionable technology, wearable devices, tech togs, or fashion electronics embedded in them. The proposed polynomial regression system 104 can be smart electronic devices (electronic device with microcontrollers) that can be worn on the body as implant or accessories.
[0066] In one implementation, the network 106 can be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. Further, the network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 can include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[0067] As discussed, the computing device 108 (which may include multiple devices in communication in a hard-wired or wireless format) may include at least one of the following: a mobile wireless device, a smartphone, a mobile computing device, a wireless device, a hard-wired device, a network device, a docking device, a personal computer, a laptop computer, a pad computer, a personal digital assistant, a wearable device, a remote computing device, a server, a functional computing device, or any combination thereof. While, in one preferred and non-limiting embodiment, the primary computing device 108 is a smartphone (which may include the appropriate hardware and software components to implement the various described functions), it is also envisioned that the computing device 108 be any suitable computing device configured, mechanism, or adapted to perform one or more of the functions of the described system.
[0068] FIG. 2 illustrates exemplary functional modules in accordance with an aspect of the present disclosure. In one embodiment, the proposed polynomial regression system 104 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 206. The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the proposed receipt management system 104 to interact with a user directly or through the client devices 108. Further, the I/O interface 204 may enable the proposed system 104 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[0069] The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable mechanism ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 210 may include modules, routines, mechanisms, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the memory 206 may include one or more sensors 208, polynomial regression visualizer 210 and parameter update module 212.
[0070] In an embodiment, the sensor can capture/sense, in real-time, one or more parameters associated with one or more devices. In an exemplary embodiment, the sensor can be selected from any or combination of light dependent resistor (LDR) sensor, light sensor, temperature sensor, photo diode sensor, electrical power sensor. Alternatively, the sensors may not be considered limited to the aforementioned sensors, and may alternatively include any proximity, motion, thermal, imaging, electrical, mechanical, or the like sensors as known to a person skilled in the art.
[0071] In an embodiment, the polynomial Regression Visualizer 210 can retrieve said captured parameters to perform polynomial regression to predict trends in real-time for said one or more devices based on said captured parameters. In another embodiment, the polynomial Regression Visualizer 210 can include a machine learning engine 212. The polynomial regression system can classify the sensor data through the machine learning engine 212.
[0072] In an embodiment, the parameter update module 214 can update one or more parameters selected from said captured parameters.
[0073] In an embodiment, the polynomial regression visualizer 210 can perform polynomial regression on the data collected from user’s product. The user may perform the following things to use the Polynomial Regression Visualizer. The following steps are exemplary in nature and should not be construed as a limitation. In other examples, all necessary setup steps as known to a person skilled in the art may be employed with respect to the present subject matter.
Exemplary Setup Steps:
i. Create an account on the cloud.
ii. Login to the app on Android or iPhone.
iii. Power up the module/system and method according to the present invention (implemented in the form of application).
iv. Follow the instructions on the App to connect the module to a WiFi AP with internet access.
v. Log into the Cloud/Server, and go to the products section.
vi. Click on “create new product” button.
vii. Select input, and specify whether the data will be collected by the module via its GPIO or UART.
viii. Select the newly created product and click on Configure.
ix. Specify which GPIOs to monitor, or how many parameters will be received over UART.
x. Ensure to name each parameter.
xi. Go to the code section and write the required visualizer code. Save the product configurations.
xii. Link the device to the product.
xiii. In the Products tab, click on “View Device” button next to user device information.
Exemplary Setup Steps specific to Polynomial Regression Visualizer:
i. Go to the product configurations for the product with which user want to use the Polynomial Regression Visualizer.
ii. Enter the following code. Do ensure that the correct parameter name has been entered.
iii. setChartLibrary('google-chart');
setChartType('predictionGraph');
setAxisName('X-axis Name','Y-axis Name');
plotChart('time_stamp','your_variable_name');
iv. Save the product configurations.
v. Go to the products tab, and click on View device next to user device information.
vi. Polynomial Regression Visualizer usage: Once the user clicks on the View device button, a new tab is created which holds the Visualizer.
[0074] In an embodiment, a prediction point depicts the visualizer how many future data points need to be predicted. By default, the Visualizer spaces the points with the data collection time in the hardware configuration of the product. So if user set the product to collect data every 5 minutes, and select 6 prediction points, the visualizer can predict the trend and show 6 points up to 30 minutes into the future. The polynomial Visualizer processes the given input time-dependent data, and outputs number polynomial coefficients of the function of the form: This most closely resembles the trend in the input data. The number polynomial coefficients depicts the Visualizer how many elements should be present in the function i.e. the value of n. Frame size are the number of previous data points the Visualizer can use to predict the trend of the data. For example, if you set this value to 5, the Visualizer can use the previous 5 points to predict the trend. When user click the 'Predict' button, the prediction history (Red line) and the next predicted trend (Yellow line) are added to the graph. The prediction history is a graph of points the Visualizer would have predicted, at the time with the data before that point in time, using the current settings. Changing the configurations mentioned earlier will change the prediction history and the next predicted trend. The closer the prediction history matches the logged data, the better the visualizer predicts the trend in the data. When a new data point is received, the data will be updated to the graph, along with prediction history and the next predicted trend.
[0075] In one or more embodiments, the machine learning engine may be associated with a machine learning algorithm. The machine learning engine may be associated with one or more of one of a physics based model, a rule based model and a vector classifier model.
[0076] FIG. 3 illustrates exemplary flow diagrams of the present system in accordance with an embodiment of the present disclosure. In an aspect, the proposed methods may be described in general context of computer executable instructions. Generally, computer executable instructions can include routines, mechanisms, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The methods may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[0077] The order in which the methods are described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method or alternate methods. Additionally, individual blocks may be deleted from the method without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method may be considered to be implemented in the above described proposed system.
[0078] At step 302, plurality of sensors can capture/ sense one or more parameters associated with one or more devices in real-time.
[0079] At step 304, the polynomial Regression Visualizer can retrieve said captured parameters to perform polynomial regression to predict trends for said one or more devices based on said captured parameters in real-time.
[0080] FIG. 4A-C illustrates an exemplary working of a proposed system, in accordance with an embodiment of the present disclosure. The exemplary embodiment provides a general implementation of the proposed system, and should not be construed as a limitation.
[0081] As shown in FIG. 4A, the proposed system adequately predict how the intensity of light will change within a room. FIG.4A depicts LDR circuit with WI-FI module. Once the connections are done, the WiFi module can connect with a server or cloud. The LDR sensor can measure the intensity of the light for particular room or particular light. The proposed system can perform polynomial regression to predict trends. The proposed system can monitor and plot the analog readings read by the A0 pin of the WiFi module. FIG. 4B depicts Analog value on A0 pin plotted against time. FIG. 4C shows the Polynomial fit working for LDR circuit.
[0082] FIG. 5 illustrates an exemplary computer system in which or using which aspects of the present disclosure can be implemented. Computer system 500 includes a bus 520 or other communication mechanism for communicating information, and a processor 570 coupled with bus 520 for processing information. Computer system 500 can also include a main memory 530 or other non-transitory computer-readable medium, such as a random-access memory (RAM) or other dynamic storage device, which can then be coupled to bus 520 for storing information and instructions to be executed by processor 570. Main memory 530 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 570. Computer system 500 may further include a read only memory (ROM) 540 or other static storage device coupled to bus 520 for storing static information and instructions for processor 570. A data/external storage device 510, such as a magnetic disk or optical disk, is provided and coupled to bus 520 for storing information and instructions.
[0083] Computer system 500 may be coupled via bus 520 to a display (not shown), such as a cathode ray tube (CRT), for displaying information to a user. An input device (not shown), including alphanumeric and other keys, can be coupled to bus 520 for communicating information and command selections to processor 570. Another type of user input device can be cursor control, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 570 and for controlling cursor movement on display. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.
[0084] The invention is related to the use of computer system 500 for creation and management of BOMs as elaborated above. According to some embodiments of the invention, such use may be provided by computer system 500 in response to processor 570 executing one or more sequences of one or more instructions contained in the main memory 530. Such instructions may be read into main memory 530 from another computer-readable medium, such as storage device 550. Execution of the sequences of instructions contained in main memory 530 causes processor 570 to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in main memory 530. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the invention. Thus, embodiments of the invention are not limited to any specific combination of hardware circuitry and software.
[0085] The term “computer-readable medium” as used herein refers to any medium that participates in providing instructions to processor 570 for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 550. Volatile media includes dynamic memory, such as main memory 530. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 520. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.
[0086] Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.
[0087] Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to processor 570 for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 500 can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to bus 520 can receive the data carried in the infrared signal and place the data on bus 520. Bus 520 carries the data to main memory 530, from which processor 570 retrieves and executes the instructions. The instructions received by main memory 530 may optionally be stored on storage device 550 either before or after execution by processor 570.
[0088] Computer system 500 also includes a communication interface 560 coupled to bus 520. Communication interface 560 can provide a two-way data communication coupling to a network link (not shown) that can be connected to a local network (not shown). For example, communication interface 560 may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 560 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface 560 sends and receives electrical, electromagnetic or optical signals that carry data streams representing various types of information.
[0089] As used herein, and unless the context dictates otherwise, the term “coupled to” is intended to include both direct coupling; in which two elements that are coupled to each other contact each other, and indirect coupling; in which at least one additional element is located between the two elements. Therefore, the terms “coupled to” and “coupled with” are used synonymously. Within the context of this document terms “coupled to” and “coupled with” are also used euphemistically to mean “communicatively coupled with” over a network, where two or more devices are able to exchange data with each other over the network, possibly via one or more intermediary device.
[0090] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C … and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc. 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.
[0091] While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.

ADVANTAGES OF THE INVENTION
[0092] The present disclosure provides a polynomial regression system and method performs polynomial regression to predict trends in real-time for one or more device based on captured parameters.
[0093] The present disclosure provides a polynomial regression system and method to give a visual representation of how the regression performs with the captured parameters.

,CLAIMS:
1. An Internet of Things (IoT) based polynomial regression system implemented for real time predictive maintenance, the system comprising:
a plurality of sensors secured to a machine, the sensors being configured to capture one or more parameters associated with the machine in real-time;
a polynomial regression visualizer configured to retrieve said captured parameters from the sensors to perform polynomial regression in real-time to predict trends for said machines based on said captured parameters;
a parameter update unit configured to update one or more parameters selected from said captured parameters; and
a control module including a hardware processor and a memory coupled to the hardware processor, the control module disposed in operative communication with a control panel of the machine, the control module receiving over a communications network control commands from the polynomial regression visualizer, and executing the control commands resulting in process control or predictive maintenance of the machine.
2. The system as claimed in claim 1, wherein the polynomial regression visualize provides prediction history and prediction trend, and wherein the parameter update unit compares the prediction history with the prediction trend to monitor a variation in data points and update the one or more parameters selected from said captured parameters based upon the variation in the data points.
3. The system as claimed in claim 1 includes a user interface configured to display visual representation associated with working of said polynomial regression when said updated parameters are used.
4. The system as claimed in claim 2, wherein the visual representation is a graphical representation of the prediction trend and the prediction history.
5. The system as claimed in claim 1, wherein the polynomial regression visualizer includes a machine learning engine configured to classify the data captured from the sensors.
6. The system as claimed in claim 5, wherein the machine learning engine is associated with at least one of physics based model, a rule based model and a vector classifier model.
7. An Internet of Things (IoT) based polynomial regression method implemented for real time predictive maintenance, the method comprising:
capturing, at a plurality of sensors, one or more parameters associated with one or more machines in real-time;
retrieving, at a polynomial regression visualizer, said captured parameters to perform, in real-time, polynomial regression to predict trends for said machines based on said captured parameters;
updating, at a parameter update unit, one or more parameters selected from said captured parameters; and
receiving, at a control module, control commands from the polynomial regression visualizer, and executing the control commands resulting in process control or predictive maintenance of the machine.
8. The method as claimed in claim 7, wherein the polynomial regression visualize provides prediction history and prediction trend, and wherein the parameter update unit compares the prediction history with the prediction trend to monitor a variation in data points and update the one or more parameters selected from said captured parameters based upon the variation in the data points.
9. The method as claimed in claim 7 includes classifying, at a machine learning engine of the polynomial regression visualize, the data captured from the sensors.
10. The method as claimed in claim 9, wherein the machine learning engine is associated with at least one of physics based model, a rule based model and a vector classifier model.