Claims:1. A system for a meter reading, wherein the system comprises:
an input unit operatively coupled to an image capturing device, wherein the input unit is configured to:
receive at least one image of a meter board at every pre-defined interval of time, captured by the image capturing device; and
receive one or more input parameters from corresponding one or more sources;
a processing unit comprising a processor operatively coupled to a memory, the memory storing instructions executable by the processor to:
retrieve the at least one image from the input unit via. a communication medium;
process the at least one image by a machine learning model, to retrieve one or more characters of the meter board from the corresponding at least one image using an image processing technique;
identify one or more parameters from at least one processed image based on a pre-defined set of instructions;
present at least one of the one or more characters, the one or more parameters, or a combination thereof in a pre-defined format;
validate the at least one of the one or more characters, the one or more parameters, or a combination thereof with the pre-defined set of instructions, to obtain meter readings; and
generate an electronic bill (e-bill) for the meter board based on the meter readings obtained.
2. The system as claimed in claim 1, wherein the meter comprises one of a digital meter board or an analog meter board.
3. The system as claimed in claim 1, wherein the one or more characters comprises at least one of date, time, maximum demand, a unique identification number, consumption of corresponding energy from the meter board, or a combination thereof.
4. The system as claimed in claim 1, wherein the one or more input parameters comprises at least one of a meter identification number, a due date for payment of a bill associated to the meter board, maximum demand, or a combination thereof.
5. The system as claimed in claim 1, wherein the communication medium comprises a wireless communication medium, wherein the wireless communication medium comprises one of a wireless fidelity (Wi-Fi) medium, a cellular network medium, a Bluetooth medium or a Bluetooth low energy (BLE) medium.
6. The system as claimed in claim 1, wherein the processor is further instructed to generate an alert notification if the one or more parameters identified by the corresponding at least one image do not match with the pre-defined set of instructions due to one or more conditions.
7. The system as claimed in claim 6, wherein the one or more conditions comprises at least one of abnormal image, blurred image, image captured in low light condition, failure in identifying at least one of the one or more characters, or a combination thereof.
8. The system as claimed in claim 1, wherein the processor is further instructed to generate a disconnect notification and transmit the disconnect notification to the meter board when at least one of a pre-defied set of conditions are not met.
9. A method for a meter reading, wherein the method comprises:
capturing, by an image capturing device, at least one image of a meter board at every pre-defined interval of time;
receiving one or more input parameters from corresponding one or more sources;
retrieving, by a processing unit, the at least one image via a communication medium;
processing, by the processing unit, the at least one image by a machine learning model, for retrieving one or more characters of the meter board from the corresponding at least one image using an image processing technique;
identifying, by the processing unit, one or more parameters from at least one processed image based on a pre-defined set of instructions;
presenting, by the processing unit, at least one of the one or more characters, the one or more parameters, or a combination thereof in a pre-defined format;
validating, by the processing unit, the at least one of the one or more characters, the one or more parameters, or a combination thereof with the pre-defined set of instructions, for obtaining meter readings;
generating, by the processing unit, an electronic bill (e-bill) for the meter board based on the meter readings obtained.
10. The method as claimed in claim 9, comprising generating, by the processing unit, an alert notification if the one or more parameters identified by the corresponding at least one image do not match with the pre-defined set of instructions due to one or more conditions.
, Description:TECHNICAL FIELD
[0001] The present disclosure generally relates to meter reading. More specifically, the present disclosure relates to a system and method for automating a process of meter reading.

BACKGROUND
[0002] Background description includes information that can 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] A meter is a device that measures an amount of energy consumed by an entity in or more ways. The meters can be electric meter, water meter, piped natural gas (PNG) meters, or the like which may be in the form of analog meters, digital meters or the like. Consumption of energy from such meters needs to be measured and bills has to be generated for the entities for the consumption of energy from these meter boards.
[0004] In a conventional approach, where most of analog and digital meters are being used, a manpower is required to visit in person, to a location where such meters are installed and take the readings of meters at every pre-defined interval of time. The readings taken by the user is fed to the database manually; due to such manual intervention there are higher chances of errors in the reading and discrepancy in the generated bills. In addition, due to the manual process, tampering of the bills is higher. Such limitations cause revenue loss for service providers and also the government. Moreover, at times during natural calamities, national emergency situations, or the like, it becomes difficult for the manpower to visit the locations in person; in such situations, energy distributors are forced to generate predictive bills for the consumption of energy based on previously generated bills. Due to such predictive bills, the customer complaints increase. Due to such limitation, the conventional approach is less reliable and less efficient.
[0005] In comparison with the conventional approach, a newer approach is being used smart meters are being used, where existing meters are to be completely replaced with smart meters which involves service interruption, higher investment on smart meters. However, there are complaints from smart meter users that the electricity bills have increased since inception of smart meters. Some smart meter involves manual transmission of meter readings by Touch technology, drive-by or walk-by methods, where an authorised user is expected to visit a location of meter to collect the reading, and share the collected reading to the database. Some smart meter system uses ports such as RS485, RS232, USB, Ethernet port, or the like to collect reading from distributed meter units and transmit the collected readings to a central gateway. Central gateway then further transmits the readings to a central database server. Such smart meters involve multiple equipment like central gateway for group of meters, which is an extra overhead. In addition, the newer approach is limited to only those meters which may include the above-mentioned communication ports, thereby making the approach less reliable and less efficient.
[0006] In another newer approach, the meters use automatic meter reading (AMR) technology. In some of the AMR devices, such as a touch-based AMR device, a manpower is still required to visit meter location with handheld computer or reading collection device. The collection device collects meter readings of meters in close proximity using one or more communication means. In another AMR devices such as Radio Frequency (RF) based AMR devices, there are limitations of regulation in terms of using frequency bands. Like in India, ISM frequency bands are not free and 2.4 GHz band can be used for such applications. Such limitations make the RF based AMR devices more expensive. In yet another AMR device such as Handheld or Mobile AMR devices, manpower is still required, where a manpower needs to visit the meter location. Meter reader needs to walk-by or drive-by for collection of meter reading in nearby proximity using any of the communication mediums. All the said limitations, makes the newer approach less reliable and less efficient.
[0007] Therefore, there is a need in the art to provide an improved system and method for automating a process of meter reading to address the aforementioned issue(s).

OBJECTS OF THE PRESENT DISCLOSURE
[0008] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0009] It is an object of the present disclosure to provide an automated system and method for meter reading.
[0010] It is another object of the present disclosure to provide a contactless, self-validated system for meter reading using the ML and the AI models.
[0011] It is another object of the present disclosure to provide a simple and cost-effective system and method for meter reading.
[0012] It is another object of the present disclosure to provide a reliable and efficient system and method for automatic meter reading.

SUMMARY
[0013] The present disclosure generally relates to meter reading. More specifically, the present disclosure relates to a system and method for automating a process of meter reading.
[0014] This summary is provided to introduce simplified concepts of a system for time bound availability check of an entity, which are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended for use in determining/limiting the scope of the claimed subject matter.
[0015] An aspect of this present disclosure pertains to a system for meter reading. The system includes an input unit configured to receive at least one image of a meter board at every pre-defined interval of time, captured by the image capturing device; and to receive one or more input parameters from corresponding one or more sources. The system also includes a processing unit configured to retrieve the at least one image from the input unit via. a communication medium; process the at least one image by a machine learning model, to retrieve one or more characters of the meter board from the corresponding at least one image using an image processing technique; identify one or more parameters from at least one processed image based on a pre-defined set of instructions; present at least one of the one or more characters, the one or more parameters, or a combination thereof in a pre-defined format; validate the at least one of the one or more characters, the one or more parameters, or a combination thereof with the pre-defined set of instructions, to obtain meter readings; and to generate an electronic bill (e-bill) for the meter board based on the meter readings obtained.
[0016] Another aspect of this present disclosure pertains to a method for meter reading. The method includes capturing at least one image of a meter board at every pre-defined interval of time; receiving one or more input parameters from corresponding one or more sources; retrieving the at least one image via a communication medium; processing the at least one image by a machine learning model, for retrieving one or more characters of the meter board from the corresponding at least one image using an image processing technique; identifying one or more parameters from at least one processed image based on a pre-defined set of instructions; presenting at least one of the one or more characters, the one or more parameters, or a combination thereof in a pre-defined format; validating the at least one of the one or more characters, the one or more parameters, or a combination thereof with the pre-defined set of instructions, for obtaining meter readings; and generating an electronic bill (e-bill) for the meter board based on the meter readings obtained.
[0017] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The diagrams are for illustration only, which thus is not a limitation of the present disclosure, and wherein:
[0019] FIG. 1 illustrates a block diagram of an embodiment of a system for meter reading, in accordance with an embodiment of the present disclosure;
[0020] FIG. 2 illustrates a block diagram of an exemplary embodiment of the system for a meter reading using an image capturing device, input unit, communication medium and processing unit of FIG. 1, in accordance with an embodiment of the present disclosure;
[0021] FIG. 3 illustrates a flowchart of a method for meter reading, in accordance with an embodiment of the present disclosure;
[0022] FIG. 4 illustrates a flowchart of an exemplary embodiment of method for Automatic capturing and uploading process of images of FIG. 3, in accordance with an embodiment of the present disclosure;
[0023] FIG. 5a and FIG. 5b illustrates flowchart of another exemplary embodiment of a method for processing of captured images of FIG. 3, in accordance with an embodiment of the present disclosure;
[0024] FIG. 6 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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).
[0029] The present disclosure generally relates to meter reading. More specifically, the present disclosure relates to a system and method for automating a process of meter reading.
[0030] FIG. 1 illustrates a block diagram of an embodiment of a system 100 for meter reading, in accordance with an embodiment of the present disclosure. As used herein, the meter reading may be performed for a corresponding meter board. In one embodiment, the meter board may be one of a digital meter board or an analog meter board. In such embodiment, the meter board may be one of an electric meter board, a water meter board, a piped natural gas (PNG) meter board, or the like. The system 100 includes an input unit 102 including an image capturing device 104. The image capturing device 104 is configured to capture at least one image of a meter board at every pre-defined interval of time. In some embodiments, the image capturing device 104 may be an image capturing device. In one specific embodiment, the image capturing device may be a camera module which may be a ESP32 Cam which may be placed in front of meter display board and is connected to microcontroller. In one exemplary embodiment, the pre-defined interval of time may include one of few hours, few days, few months, or the like, which may be defined by an authorised user.
[0031] The input unit 102 is also configured to receive one or more input parameters from corresponding one or more sources. In one embodiment, the one or more input parameters may include, but not limited to, at least one of a meter identification number, a due date for payment of a bill associated to the meter board, maximum demand, or a combination thereof.
[0032] Furthermore, the system 100 includes a processing unit 106 comprising a processor 108 operatively coupled to a memory 110. In one exemplary embodiment, the memory may include EPROM or other form of internal memory of ESP32 Cam chipset.
[0033] The memory 110 storing instructions executable by the processor 108 to retrieve the at least one image from the input unit 102 from a communication medium 112. In one exemplary embodiment, the communication medium 112 may include a wireless communication medium. In such embodiment, the wireless communication medium may include, but not limited to, one of a wireless fidelity (Wi-Fi) medium, a cellular network medium, a Bluetooth medium or a Bluetooth low energy (BLE) medium, or the like.
[0034] The processor 108 is further instructed to process the at least one image by a machine learning (ML) model, to retrieve one or more characters of the meter board from the corresponding at least one image using an image processing technique. More specifically, the ML model which may be created using a machine learning technique. As used herein, the term ‘machine learning technique’ is defined a study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Also, the term ‘artificial intelligence’ is defined as an intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. In one exemplary embodiment, the ML models may be trained using Millions of sample images so that the output reading from the snapshot is most accurate. In one exemplary embodiment, the one or more characters may include, but not limited to, at least one of date, time, maximum demand, a unique identification number, consumption of corresponding energy from the meter board, or a combination thereof. In such embodiment, the ML model may be used to convert the content in the corresponding at least one image into corresponding numerical values, wherein the one or more characters may be in the form of the numerical values.
[0035] The processor 108 is also instructed to identify one or more parameters from at least one processed image based on a pre-defined set of instructions. In one embodiment, the one or more parameters may include, but not limited to, at least one of the date, the time, maximum demand, the unique identification number, consumption of corresponding energy from the meter board, or a combination thereof.
[0036] Furthermore, the processor 108 is instructed to present at least one of the one or more characters, the one or more parameters, or a combination thereof in a pre-defined format. In one embodiment, the pre-defined format may be set by the authorised entities to generate one or more bill for the energy consumed by the corresponding users as indicated in the meter board. The pre-defined format may be standard within a corresponding area or location.
[0037] The processor 108 is also instructed to validate the at least one of the one or more characters, the one or more parameters, or a combination thereof with the pre-defined set of instructions, to obtain meter readings. In one embodiment, the validation may happen upon comparing at least one of the one or more characters, the one or more parameters, or a combination thereof with pre-set values associated to corresponding at least one of the one or more characters, the one or more parameters, or a combination thereof.
[0038] In one exemplary embodiment, the processor 108 may be further instructed to generate an alert notification if the one or more parameters identified by the corresponding at least one image do not match with the pre-defined set of instructions due to one or more conditions. In such embodiment, the one or more conditions may be at least one of abnormal image, blurred image, image captured in low light condition, failure in identifying at least one of the one or more characters, or a combination thereof. In one embodiment, the ML model may extract the one or more undefined conditions to enhance the processing, validation and presentation of the one or more characters associated with the corresponding at least one abnormal image.
[0039] Furthermore, in another exemplary embodiment, the processor 108 may be configured to generate a disconnect notification and transmit the disconnect notification to the meter board when at least one of a pre-defied set of conditions are not met.
[0040] The processor 108 is also instructed to generate an electronic bill (e-bill) for the meter board based on the meter readings obtained. In one embodiment, the e-bill may be further transmitted to a computing device associated to the corresponding entity. In one exemplary embodiment, the entity may include a user using energy from the meter board, a building using the energy from the meter board, or the like.
[0041] In operation, a meter reading device may be installed at user’s place in front of the energy meter board. After installation, an SSID and passwords of 3 Wi-Fi connections (say for example) may be auto configured in the device via a cellular connection. Also, an authorised person may configure current meter consumption unit from a meter board screen, position of the meter reading screen and maximum demand screen after reset screen into the backend AI and ML processing server. Further, the processor 108 automatically goes into configuration mode which takes high frequency images and once the automated algorithm deciphers the number of images and duration, the backend process automatically programs the meter reading device to take the images until energy consumed is captured.
[0042] Further, the images captured may be uploaded to the backend image processing server automatically along with device ID in order to identify from which device the images has been uploaded. Internet communication media such as Wi-Fi (from broadband connection or hot-spot of mobile device), mobile internet (GSM or LTE) may be used for uploading the images. In case internet connection is not available due to any reason, the images may be stored in a local storage temporarily and may be uploaded to server once internet connection is available again.
[0043] Subsequently, the images may be uploaded by the input unit may be fed to the ML model. Wherein the ML model may be developed and trained in such a way, that the images uploaded to server gets transformed into numerical readings. Millions of sample images may be used to train the ML model, so that the output reading from the corresponding image is most accurate. Machine learning model may extract the reading from abnormal images such as blurred image, images captured in low light condition, half number detection in case of analog meters, or the like. Numerical readings extracted as an output of machine learning model may be processed in order to eradicate abnormal readings in case any, as well as process of auto configuration of the image capturing device to capture load irrespective of display sequence and timing of digital meter boards. Consequently, the processed meter readings may be validated for accuracy in order to have accurate billing. Generated bill amount along with due date and MD (maximum demand) may be displayed on the display attached to the meter reading device.
[0044] FIG. 2 illustrates a block diagram of an exemplary embodiment of the system 200 for a meter reading using an image capturing device, input unit, communication medium (Wi-Fi or cellular medium) and processing unit of FIG. 1, in accordance with an embodiment of the present disclosure. A GPRS module 202 and Wi-fi module 208 of a user computing device, which is communicatively coupled to a microcontroller 204 and uploads data to backend processing server 222 over an internet medium 206. Power from a main electric power pole 214 is fed into either the digital meter board or the analog meter board 210. The power is further supplied to at least one load 216 via the relay 212. The supply of the electric power is controlled by a power module 218 which is operatively coupled to the microcontroller 204. The microcontroller 204 is further coupled to a display unit 220 which is configured to display monthly electricity bill which is extracted via images by the microcontroller 204 for further processing, validation and e-bill generation. The extracted data is transmitted to a backend server 222 via the internet medium 206 for generation of the e-bill.
[0045] FIG. 3 illustrates a flowchart of a method 300 for meter reading, in accordance with an embodiment of the present disclosure. The method 300 includes capturing at least one image of a meter board at every pre-defined interval of time in step 302. In one embodiment, capturing the at least one image may include capturing the at least one image by an image capturing device.
[0046] The method 300 also includes receiving one or more input parameters from corresponding one or more sources in step 304. In one embodiment, receiving the one or more input parameters may include receiving at least one of a meter identification number, a due date for payment of a bill associated to the meter board, maximum demand, or a combination thereof.
[0047] The method 300 also includes retrieving the at least one image via a communication medium in step 306. In one embodiment, retrieving the at least one image may include retrieving the at least one image by a processing unit. In one exemplary embodiment, retrieving the at least one image may include retrieving the at least one image via a wireless communication medium, wherein the wireless communication medium comprises one of a wireless fidelity (Wi-Fi) medium, cellular network medium, a Bluetooth medium or a Bluetooth low energy (BLE) medium.
[0048] Furthermore, the method 300 includes processing the at least one image by a machine learning model, for retrieving one or more characters of the meter board from the corresponding at least one image using an image processing technique in step 308. In one embodiment, processing the at least one image may include processing the at least one image by the processing unit.
[0049] The method 300 also includes identifying one or more parameters from at least one processed image based on a pre-defined set of instructions 310. In one embodiment, identifying the one or more parameters may include identifying the one or more parameters. In some embodiments, identifying the one or more parameters may include identifying at least one of the meter identification number, the due date for payment of the bill associated to the meter board, the maximum demand, or a combination thereof.
[0050] The method 300 also includes presenting at least one of the one or more characters, the one or more parameters, or a combination thereof in a pre-defined format in step 312. In one embodiment, presenting may include presenting by the processing unit. The method 300 further includes validating the at least one of the one or more characters, the one or more parameters, or a combination thereof with the pre-defined set of instructions, for obtaining meter readings in step 314. In one embodiment, validating may include validating by the processing unit.
[0051] The method 300 further includes generating an electronic bill (e-bill) for the meter board based on the meter readings obtained in step 316. In one embodiment, generating the e-bill may include generating the e-bill by the processing unit. In one exemplary embodiment, the method 300 may further include generating an alert notification if the one or more parameters identified by the corresponding at least one image do not match with the pre-defined set of instructions due to one or more conditions.
[0052] In one specific embodiment, the method 300 may further include generating a disconnect notification and transmit the disconnect notification to the meter board when at least one of a pre-defied set of conditions are not met.
[0053] It should be noted that all the embodiments described in FIG. 1 holds good to the method steps described in FIG. 3.
[0054] FIG. 4 illustrates a flowchart of an exemplary embodiment of method 400 for Automatic capturing and uploading process of images of FIG. 3, in accordance with an embodiment of the present disclosure. The credentials stored in SPIFFs are chosen in step 402. Using the credentials, the device is connected to the Wi-Fi medium in step 404. Upon successful connection in step 406, the at least one image are initiated to be captured in step 408. The captured images are transmitted to the server in step 410 and fetching the credentials in step 412. If new credentials are found, the SPIFFs are updated and the ESP is reset in step 414, 415 and 416. If there are no new credentials found, the loop is ended 418.
[0055] Consequently, if there was a failure of connection for the Wi-Fi medium (as shown in step 404), the meter board tries to connect to GPRS or the LTE connection in step 420, upon successful connection, a switch status is verified in step 422 and 424. If the switch is on, load is connected and the initiation to capture the at least one image is initiated in step 426, 425 and 408, and the process continues as described above. If the switch is not on (as shown in step 424), the load is disconnected in step 428. On the other hand, of the connection to GPRS or the LTE fails (as shown in step 420), the connection to Wi-Fi is again initiated (as shown in step 404).
[0056] FIG. 5a and FIG. 5b illustrates flowchart of another exemplary embodiment of a method 500 for processing of captured images of FIG. 3, in accordance with an embodiment of the present disclosure. The method 500 is initiated upon receiving the at least one image which may be captured by the image capturing device in step 502. On receiving the same, the processing is initiated in step 504, else the system waits for the image to be received in step 506. Further, if the image indicates a reset screen in step 508, counter is incremented by value 1, in step 510, else the image is discarded in step 512 and the step 506 is repeated.
[0057] Consequently, on receiving a second image in step 514, the counter is again incremented by value 1, in step 516. If the image is not received, step 506 is repeated. Further, as the process continues, and if the counter gets equal to value 4, in step 518, the image is processed and the validation is performed for the kilowatt hour (KWh) reading in step 520. If the reading is valid and is greater than a previous reading, the reading is saved in step 522, 524 and 526. If the reading is not valid or if the current reading is not greater than the previous reading, step 512 is performed.
[0058] Furthermore, the process continues for receiving a next image in step 528 and the step 510 is repeated. Further, the image is processed and is validated for the MD value in step 530, if the MD value is found less than or equal to 50 in step 532, MD value is saved in step 534, else the image is discarded in step 512. The process is continued to receive the next image in step 536, the step 510 is repeated. If the value of counter is found equal to either a value 6 or a value 7 in step 538, the image is discarded in step 540 and the counter is reset in step 542 and is directed to the initial step of 502; else the process for waiting for the next image continues in step 544, and upon receiving the same, the process as described is carried out.
[0059] FIG. 6 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure. The computer system 600 can include an external storage device 602, a bus 604, a main memory 606, a read only memory 608, a mass storage device 610, communication port 612, and a processor 614. A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. Examples of processor 614 include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on chip processors, GPU (graphics processing unit) or other future processors. Processor 614 may include various modules associated with embodiments of the present invention. Communication port 612 can be any of an RS-232 port for use with a modem based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. Communication port 612 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects.
[0060] Memory 606 can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory 408 can be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for processor 614. Mass storage 610 may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate Barracuda 7102 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.
[0061] Bus 604 communicatively couples the processor(s) 614 with the other memory, storage and communication blocks. Bus 404 can be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects processor 614 to software system.
[0062] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to bus 604 to support direct operator interaction with a computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port 612. The external storage device 602 can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc-Re-Writable (CD-RW), Digital Video Disk-Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0063] Embodiments of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc.) or combining software and hardware implementation that may all generally be referred to herein as a “circuit,” “module,” “component,” or “system.” Furthermore, aspects of the present disclosure may take the form of a computer program product comprising one or more computer readable media having computer readable program code embodied thereon.
[0064] Thus, 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.
[0065] 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.
[0066] 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.
[0067] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0068] The present disclosure provides for a system and method for content learning through multimedia interaction and ML and AI based processing;
[0069] The present disclosure provides an automated system and method for meter reading.
[0070] The present disclosure provides a contactless, self-validated system for meter reading using the ML and the AI models.
[0071] The present disclosure provides a simple and cost-effective system and method for meter reading.
[0072] The present disclosure provides a reliable and efficient system and method for automatic meter reading.