SYSTEM AND METHOD FOR ANALYZING AND EXECUTING INCOMING MULTI-CHANNEL NETWORK REQUESTS BASED ON PRE-GENERATED CHANNEL WEIGHTAGES

Embodiments of the present invention provide a system for analyzing and executing incoming multi-channel network requests based on pre-generated channel weightages. The system is configured for receiving a network request from at least one network channel of a plurality of network channels, determining that the network request is a first time request, determining a weightage inductor for the network request via a quantum machine learning model executed via a quantum machine learning optimizer, assigning the weightage inductor to the network request and store the weightage inductor in a data repository, and processing the network request based on the weightage inductor by initiating a first set of processes.

BACKGROUND

There exists a need for a system for analyzing and executing incoming multi-channel network requests based on pre-generated channel weightages.

BRIEF SUMMARY

Embodiments of the present invention address the above needs and/or achieve other advantages by providing apparatuses (e.g., a system, computer program product and/or other devices) and methods for analyzing and executing incoming multi-channel network requests based on pre-generated channel weightages. The system embodiments may comprise one or more memory devices having computer readable program code stored thereon, a communication device, and one or more processing devices operatively coupled to the one or more memory devices, wherein the one or more processing devices are configured to execute the computer readable program code to carry out the invention. In computer program product embodiments of the invention, the computer program product comprises at least one non-transitory computer readable medium comprising computer readable instructions for carrying out the invention. Computer implemented method embodiments of the invention may comprise providing a computing system comprising a computer processing device and a non-transitory computer readable medium, where the computer readable medium comprises configured computer program instruction code, such that when said instruction code is operated by said computer processing device, said computer processing device performs certain operations to carry out the invention.

In some embodiments, the present invention receives a network request from at least one network channel of a plurality of network channels, determines that the network request is a first time request, determines a weightage inductor for the network request via a quantum machine learning model executed via a quantum machine learning optimizer, assigns the weightage inductor to the network request and store the weightage inductor in a data repository, and processes the network request based on the weightage inductor by initiating a first set of processes.

In some embodiments, the present invention determines the weightage inductor based on one or more customizable parameters.

In some embodiments, the one or more customizable parameters comprise at least a type of the at least one network channel used to initiate the network request, type of the network request, type of a user computing system used to initiate the network request, type of software associated with the user computing system, type of hardware associated with the user computing system, type of data associated with the network request, amount of the data associated with the network request, historical data associated with the network channel, and type of users associated with the network request.

In some embodiments, the present invention receives a second network request from the at least one network channel, determines that the second network request is a repetitive request, wherein the second network request has same parameters as the network request, extracts the weightage inductor associated with the network request from the data repository, and processes the second network request based on the weightage inductor.

In some embodiments, processing the second network request based on the weightage inductor comprises bypassing at least one process from the first set of processes. In some embodiments, the present invention

In some embodiments, the present invention trains the machine learning models to calculate weightage inductors for incoming network requests.

In some embodiments, processing the network request based on the weightage inductor by initiating the first set of processes comprises processing the first set of processes in parallel, via the quantum machine learning optimizer.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

In accordance with embodiments of the invention, the term “entity” may include any organization that processes requests from multiple channels. Furthermore, embodiments of the present invention use the term “user.” It will be appreciated by someone with ordinary skill in the art that the user may be an employee of the entity. In an embodiment of the present invention, the entity may be a financial institution, the third-party entity may be a merchant, and a user may be an employee (e.g., application developer) of the merchant or the financial institution.

As used herein, a quantum computer is any computer that utilizes the principles of quantum physics to perform computational operations. Several variations of quantum computer design are known, including quantum computing, superconducting quantum computing, nuclear magnetic resonance quantum computing, and/or ion-trap quantum computing. Regardless of the particular type of quantum computer implementation, all quantum computers encode data onto qubits. Whereas classical computers encode bits into ones and zeros, quantum computers encode data by placing a qubit into one of two identifiable quantum states. Unlike conventional bits, however, qubits exhibit quantum behavior, allowing the quantum computer to process a vast number of calculations simultaneously.

A qubit can be formed by any two-state quantum mechanical system. For example, in some embodiments, a qubit may be the polarization of a single photon or the spin of an electron. Qubits are subject to quantum phenomena that cause them to behave much differently than classical bits. Quantum phenomena include superposition, entanglement, tunneling, superconductivity, and the like.

Two quantum phenomena are especially important to the behavior of qubits in a quantum computer: superposition and entanglement. Superposition refers to the ability of a quantum particle to be in multiple states at the same time. Entanglement refers to the correlation between two quantum particles that forces the particles to behave in the same way even if they are separated by great distances. Together, these two principles allow a quantum computer to process a vast number of calculations simultaneously.

In a quantum computer with n qubits, the quantum computer can be in a superposition of up to 2″ states simultaneously. By comparison, a classical computer can only be in one of the 2″ states at a single time. As such, a quantum computer can perform vastly more calculations in a given time period than its classical counterpart. For example, a quantum computer with two qubits can store the information of four classical bits. This is because the two qubits will be a superposition of all four possible combinations of two classical bits (00, 01, 10, or 11). Similarly, a three qubit system can store the information of eight classical bits, four qubits can store the information of sixteen classical bits, and so on. A quantum computer with three hundred qubits could possess the processing power equivalent to the number of atoms in the known universe.

Despite the seemingly limitless possibilities of quantum computers, present quantum computers are not yet substitutes for general purpose computers. Instead, quantum computers can outperform classical computers in a specialized set of computational problems. Principally, quantum computers have demonstrated superiority in solving optimization problems. Generally speaking, the term “optimization problem” as used throughout this application describe a problem of finding the best solution from a set of all feasible solutions. In accordance with some embodiments of the present invention, quantum computers as described herein are designed to perform adiabatic quantum computation and/or quantum annealing. Quantum computers designed to perform adiabatic quantum computation and/or quantum annealing are able to solve optimization problems as contemplated herein in real time or near real time.

Embodiments of the present invention make use of quantum ability of optimization by utilizing a quantum computer in conjunction with a classical computer. Such a configuration enables the present invention to take advantage of quantum speedup in solving optimization problems, while avoiding the drawbacks and difficulty of implementing quantum computing to perform non-optimization calculations. Examples of quantum computers that can be used to solve optimization problems parallel to a classic system are described in, for example, U.S. Pat. Nos. 9,400,499, 9,207,672, each of which is incorporated herein by reference in its entirety.

Typically, an entity may utilize many applications and services to implement organizational activities associated with the entity, where these applications comprise upstream applications that pass on data to other downstream applications to process incoming network requests. Applications may receive these multiple network requests every minute from multiple network channels, where more than one of the network requests may have issues due to invalid exposure controls, wrong validations, regression issues, or the like, thereby causing applications to fail and creating a huge backlog of network requests which in turn results in consumption of processing power of multiple computing systems. As such, there exists a need for a system that overcomes these technical problems and processes the network requests efficiently.

FIG.1provides a block diagram illustrating a system environment100for analyzing and executing incoming multi-channel network requests based on pre-generated channel weightages, in accordance with an embodiment of the invention. As illustrated inFIG.1, the environment100includes an multi-channel network request execution system300, entity system200, a quantum optimizer500, and a computing device system400. One or more users110may be included in the system environment100, where the users110interact with the other entities of the system environment100via a user interface of the computing device system400. In some embodiments, the one or more user(s)110of the system environment100may be employees of an entity associated with the entity system200.

The entity system(s)200may be any system owned or otherwise controlled by an entity to support or perform one or more process steps described herein. In some embodiments, the entity is a financial institution. In some embodiments, the entity is a non-financial institution.

The multi-channel network request execution system300is a system of the present invention for performing one or more process steps described herein. In some embodiments, the multi-channel network request execution system300may be an independent system. In some embodiments, the multi-channel network request execution system300may be a part of the entity system200.

The multi-channel network request execution system300, the entity system200, the computing device system400, and the quantum optimizer500may be in network communication across the system environment100through the network150. The network150may include a local area network (LAN), a wide area network (WAN), and/or a global area network (GAN). The network150may provide for wireline, wireless, or a combination of wireline and wireless communication between devices in the network. In one embodiment, the network150includes the Internet. In general, the multi-channel network request execution system300is configured to communicate information or instructions with the entity system200, the quantum optimizer500, and the computing device system400across the network150.

The computing device system400may be a computing device of the user11. In general, the computing device system400communicates with the user110via a user interface of the computing device system400, and in turn is configured to communicate information or instructions with the multi-channel network request execution system300, the quantum optimizer500, and the entity system200across the network150.

FIG.2provides a block diagram illustrating the entity system200, in greater detail, in accordance with embodiments of the invention. As illustrated inFIG.2, in one embodiment of the invention, the entity system200includes one or more processing devices220operatively coupled to a network communication interface210and a memory device230. In some embodiments, the entity system200may be operated by any entity that develops software applications. In certain embodiments, the entity system200is operated by a first entity, such as a financial institution, while in other embodiments, the entity system200is operated by an entity other than a financial institution.

It should be understood that the memory device230may include one or more databases or other data structures/repositories. The memory device230also includes computer-executable program code that instructs the processing device220to operate the network communication interface210to perform certain communication functions of the entity system200described herein. For example, in one embodiment of the entity system200, the memory device230includes, but is not limited to, a network server application240, an multi-channel network request execution application250, one or more entity applications260, and a data repository280. The computer-executable program code of the network server application240, the multi-channel network request execution application250, and the one or more entity applications260to perform certain logic, data-extraction, and data-storing functions of the entity system200described herein, as well as communication functions of the entity system200.

The network server application240, the multi-channel network request execution application250and the one or more entity applications260are configured to store data in the data repository280or to use the data stored in the data repository280when communicating through the network communication interface210with the multi-channel network request execution system300, the quantum optimizer500, and the computing device system400to perform one or more process steps described herein. In some embodiments, the entity system200may receive instructions from the multi-channel network request execution system300via the multi-channel network request execution application250to perform certain operations. The multi-channel network request execution application250may be provided by the multi-channel network request execution system300.

FIG.3provides a block diagram illustrating the multi-channel network request execution system300in greater detail, in accordance with embodiments of the invention. As illustrated inFIG.3, in one embodiment of the invention, the multi-channel network request execution system300includes one or more processing devices320operatively coupled to a network communication interface310and a memory device330. In certain embodiments, the multi-channel network request execution system300is operated by a first entity, such as a financial institution, while in other embodiments, the multi-channel network request execution system300is operated by an entity other than a financial institution. In some embodiments, the multi-channel network request execution system300is owned or operated by the entity of the entity system200. In some embodiments, the multi-channel network request execution system300may be an independent system. In alternate embodiments, the multi-channel network request execution system300may be a part of the entity system200.

It should be understood that the memory device330may include one or more databases or other data structures/repositories. The memory device330also includes computer-executable program code that instructs the processing device320to operate the network communication interface310to perform certain communication functions of the multi-channel network request execution system300described herein. For example, in one embodiment of the multi-channel network request execution system300, the memory device330includes, but is not limited to, a network provisioning application340, a middle ware application350, a request controller360, a prediction and optimization application370, a dynamic weightage processing application380, a weightage inductor385, and a data repository390comprising data processed or accessed by one or more applications in the memory device330. The computer-executable program code of the network provisioning application340, the middle ware application350, the request controller360, the prediction and optimization application370, the dynamic weightage processing application380, and the weightage inductor385may instruct the processing device320to perform certain logic, data-processing, and data-storing functions of the multi-channel network request execution system300described herein, as well as communication functions of the multi-channel network request execution system300.

The network provisioning application340, the middle ware application350, the request controller360, the prediction and optimization application370, the dynamic weightage processing application380, and the weightage inductor385are configured to invoke or use the data in the data repository390when communicating through the network communication interface310with the entity system200, the quantum optimizer500, and the computing device system400. In some embodiments, the network provisioning application340, the middle ware application350, the request controller360, the prediction and optimization application370, the dynamic weightage processing application380, and the weightage inductor385may store the data extracted or received from the entity system200, the quantum optimizer500, and the computing device system400in the data repository390. In some embodiments, the network provisioning application340, the middle ware application350, the request controller360, the prediction and optimization application370, the dynamic weightage processing application380, and the weightage inductor385may be a part of a single application. One or more processes performed by the middle ware application350, the request controller360, the prediction and optimization application370, the dynamic weightage processing application380, and the weightage inductor385are described inFIG.5.

FIG.4provides a block diagram illustrating a computing device system400ofFIG.1in more detail, in accordance with embodiments of the invention. However, it should be understood that the computing device system400is merely illustrative of one type of computing device system that may benefit from, employ, or otherwise be involved with embodiments of the present invention and, therefore, should not be taken to limit the scope of embodiments of the present invention. The computing devices may include any one of portable digital assistants (PDAs), pagers, mobile televisions, mobile phone, entertainment devices, desktop computers, workstations, laptop computers, cameras, video recorders, audio/video player, radio, GPS devices, wearable devices, Internet-of-things devices, augmented reality devices, virtual reality devices, automated teller machine devices, electronic kiosk devices, or any combination of the aforementioned.

Some embodiments of the computing device system400include a processor410communicably coupled to such devices as a memory420, user output devices436, user input devices440, a network interface460, a power source415, a clock or other timer450, a camera480, and a positioning system device475. The processor410, and other processors described herein, generally include circuitry for implementing communication and/or logic functions of the computing device system400. For example, the processor410may include a digital signal processor device, a microprocessor device, and various analog to digital converters, digital to analog converters, and/or other support circuits. Control and signal processing functions of the computing device system400are allocated between these devices according to their respective capabilities. The processor410thus may also include the functionality to encode and interleave messages and data prior to modulation and transmission. The processor410can additionally include an internal data modem. Further, the processor410may include functionality to operate one or more software programs, which may be stored in the memory420. For example, the processor410may be capable of operating a connectivity program, such as a web browser application422. The web browser application422may then allow the computing device system400to transmit and receive web content, such as, for example, location-based content and/or other web page content, according to a Wireless Application Protocol (WAP), Hypertext Transfer Protocol (HTTP), and/or the like.

The processor410is configured to use the network interface460to communicate with one or more other devices on the network150. In this regard, the network interface460includes an antenna476operatively coupled to a transmitter474and a receiver472(together a “transceiver”). The processor410is configured to provide signals to and receive signals from the transmitter474and receiver472, respectively. The signals may include signaling information in accordance with the air interface standard of the applicable cellular system of the wireless network152. In this regard, the computing device system400may be configured to operate with one or more air interface standards, communication protocols, modulation types, and access types. By way of illustration, the computing device system400may be configured to operate in accordance with any of a number of first, second, third, and/or fourth-generation communication protocols and/or the like. For example, the computing device system400may be configured to operate in accordance with second-generation (2G) wireless communication protocols IS-136 (time division multiple access (TDMA)), GSM (global system for mobile communication), and/or IS-95 (code division multiple access (CDMA)), or with third-generation (3G) wireless communication protocols, such as Universal Mobile Telecommunications System (UMTS), CDMA2000, wideband CDMA (WCDMA) and/or time division-synchronous CDMA (TD-SCDMA), with fourth-generation (4G) wireless communication protocols, with LTE protocols, with 4GPP protocols and/or the like. The computing device system400may also be configured to operate in accordance with non-cellular communication mechanisms, such as via a wireless local area network (WLAN) or other communication/data networks.

As described above, the computing device system400has a user interface that is, like other user interfaces described herein, made up of user output devices436and/or user input devices440. The user output devices436include a display430(e.g., a liquid crystal display or the like) and a speaker432or other audio device, which are operatively coupled to the processor410.

The user input devices440, which allow the computing device system400to receive data from a user such as the user110may include any of a number of devices allowing the computing device system400to receive data from the user110, such as a keypad, keyboard, touch-screen, touchpad, microphone, mouse, joystick, other pointer device, button, soft key, and/or other input device(s). The user interface may also include a camera480, such as a digital camera.

The computing device system400may also include a positioning system device475that is configured to be used by a positioning system to determine a location of the computing device system400. For example, the positioning system device475may include a GPS transceiver. In some embodiments, the positioning system device475is at least partially made up of the antenna476, transmitter474, and receiver472described above. For example, in one embodiment, triangulation of cellular signals may be used to identify the approximate or exact geographical location of the computing device system400. In other embodiments, the positioning system device475includes a proximity sensor or transmitter, such as an RFID tag, that can sense or be sensed by devices known to be located proximate a merchant or other location to determine that the computing device system400is located proximate these known devices.

The computing device system400further includes a power source415, such as a battery, for powering various circuits and other devices that are used to operate the computing device system400. Embodiments of the computing device system400may also include a clock or other timer450configured to determine and, in some cases, communicate actual or relative time to the processor410or one or more other devices.

The computing device system400also includes a memory420operatively coupled to the processor410. As used herein, memory includes any computer readable medium (as defined herein below) configured to store data, code, or other information. The memory420may include volatile memory, such as volatile Random Access Memory (RAM) including a cache area for the temporary storage of data. The memory420may also include non-volatile memory, which can be embedded and/or may be removable. The non-volatile memory can additionally or alternatively include an electrically erasable programmable read-only memory (EEPROM), flash memory or the like.

The memory420can store any of a number of applications which comprise computer-executable instructions/code executed by the processor410to implement the functions of the computing device system400and/or one or more of the process/method steps described herein. For example, the memory420may include such applications as a conventional web browser application422, a multi-channel network request execution application421, an entity application424, or the like. These applications also typically instructions to a graphical user interface (GUI) on the display430that allows the user110to interact with the entity system200, the quantum optimizer500, the multi-channel network request execution system300, and/or other devices or systems. The memory420of the computing device system400may comprise a Short Message Service (SMS) application423configured to send, receive, and store data, information, communications, alerts, and the like via the wireless telephone network152.

The memory420can also store any of a number of pieces of information, and data, used by the computing device system400and the applications and devices that make up the computing device system400or are in communication with the computing device system400to implement the functions of the computing device system400and/or the other systems described herein.

FIG.5presents a block diagram illustrating the quantum optimizer500ofFIG.1, in accordance with embodiments of the present invention.FIG.5is a schematic diagram of an exemplary Quantum Optimizer500that can be used in parallel with a classical computer to solve optimization problems. The Quantum Optimizer500is comprised of a Data Extraction Subsystem504, a Quantum Computing Subsystem501, and an Action Subsystem505. As used herein, the term “subsystem” generally refers to components, modules, hardware, software, communication links, and the like of particular components of the system. Subsystems as contemplated in embodiments of the present invention are configured to perform tasks within the system as a whole.

As depicted inFIG.5, the Data Extraction Subsystem504communicates with the system of the invention to extract data for optimization. It will be understood that any method of communication between the Data Extraction Subsystem504and the network includes, but is not limited to wired communication, Radiofrequency (RF) communication, Bluetooth®, WiFi, and the like. The Data Extraction Subsystem504then formats the data for optimization in the Quantum Computing Subsystem.

As further depicted inFIG.5, the Quantum Computing Subsystem501comprises a Quantum Computing Infrastructure523, a Quantum Memory522, and a Quantum Processor521. The Quantum Computing Infrastructure523comprises physical components for housing the Quantum Processor521and the Quantum Memory522. The Quantum Computing Infrastructure523further comprises a cryogenic refrigeration system to keep the Quantum Computing Subsystem501at the desired operating conditions. In general, the Quantum Processor521is designed to perform adiabatic quantum computation and/or quantum annealing to optimize data received from the Data Extraction Subsystem504. The Quantum Memory522is comprised of a plurality of qubits used for storing data during operation of the Quantum Computing Subsystem501. In general, qubits are any two-state quantum mechanical system.

The Action Subsystem502communicates the optimized data from the Quantum Computing Subsystem501back to the system of the invention. It will be understood that any method of communication between the Data Extraction Subsystem504and the network includes, but is not limited to wired communication, Radiofrequency (RF) communication, Bluetooth®, WiFi, and the like.

In accordance with the present systems and methods, an on-board quantum optimizer may be employed to perform real-time optimizations to hyper parameters of machine learning models more quickly and more reliably than a digital computing system. Because a quantum computing device inherently performs optimization in its natural evolution, quantum optimizer is particularly well-suited to solve optimization problems.

FIG.6provides a process flow for analyzing and executing incoming multi-channel network requests based on pre-generated channel weightages, in accordance with an embodiment of the invention. As shown in block605, the system receives a network request from at least one network channel of a plurality of network channels. The network request may be any request initiated via an entity's network associated with an entity by a user (e.g., an employee), via a user computing system (e.g., computing device system400). The network request may be processed by ‘n’ number of entity applications and/or entity services. The plurality of network channels may be specific to the use case that the present invention is utilized for. In some embodiments, the plurality of network channels may comprise a web application-based channel, a mobile application based channel, and/or the like. In one example, the system may receive a login request from a web-based channel, where the login request is processed by authentication applications and authorization services associated with the entity. In another example, the system may receive a payment processing request from a mobile application based channel, where the payment processing request is processed by payment processing application and services associated with the entity.

As shown in block610, the system determines that the network request is a first time request. The system determines that the network request is a first time request based on determining that the network request is initiated by a new user, a new channel, a new user computing system, and/or the like.

As shown in block615, the system determines a weightage inductor for the network request via a quantum machine learning model executed via a quantum machine learning optimizer. The system may determine the weightage inductor based on one or more customizable parameters. In some embodiments, the one or more customizable parameters comprise at least a type of the at least one network channel used to initiate the network request (e.g., email, web, mobile, and/or the like), type of the network request (e.g., authentication request, payment request, and/or the like), type of a user computing system used to initiate the network request (e.g., mobile device, desktop computer, external device, internal device associated with the entity), type of software associated with the user computing system (e.g., security applications, type of application used to initiate the network request), type of hardware associated with the user computing system, type of data associated with the network request, amount of the data associated with the network request, historical data associated with the network channel (e.g., historical data that resulted in outages of entity applications or entity services), and type of users (e.g., internal users, external users, or the like) associated with the network request.

As shown in block620, the system assigns the weightage inductor to the network request and store the weightage inductor in a data repository. The weightage inductor defines whether the incoming network request is reliable and whether it causes any stability issues in the entity applications and entity services that are processing the network request. In some embodiments, value of the weightage inductor may be positive. In some embodiments, value of the weightage inductor may be negative. In some embodiments, the weightage inductor may comprise information about priority of executing the network request compared to the other incoming network requests. In some embodiments, the system may calculate weightage inductor value for every incoming request unless the incoming requests are similar in all parameters to previous requests.

As shown in block625, the system processes the network request based on the weightage inductor by initiating a first set of processes. In some embodiment, the weightage inductor may store additional data associated with processing the network request, where the additional data may comprise information about executing the first set of processes, type of processing associated with first set of processes (e.g., sequential processing, parallel processing, combination of parallel and sequential processing), number of mandatory processes to be executed from the first set of processes, and/or the like.

As shown in block630, the system receives a second network request from the at least one network channel. As shown in block635, the system determines that the second network request is a repetitive request, wherein the second network request has same parameters as the network request. For example, the system may determine that the second network request is initiated by the same user that initiated the network request and from the same channel as described in block605and may determine that the network request is a repetitive request. In some embodiments, if any of the parameters associated with the second network request do not match the previous network requests, the system will calculate a new weightage inductor for the second network request. Continuing with the previous example, if the second network request is initiated by the same user that initiated the network request, but from a different user computing system, the system will calculate a new weightage inductor for the second network request.

As shown in block640, the system extracts the weightage inductor associated with the network request from the data repository. As shown in block645, the system processes the second network request based on the weightage inductor. Processing the second network request based on the weightage inductor may comprise bypassing at least one process from the first set of processes. For example, the system may determine that the second network request is repetitive and may skip non-crucial processes from the first set of processes.

FIG.7provides a block diagram for analyzing and executing incoming multi-channel network requests based on pre-generated channel weightages, in accordance with an embodiment of the invention. As shown, multiple network requests may be initiated from the one or more channels710, where the network requests are directed to a middleware application350and the request controller360for initial processing and sorting, where these applications may determine whether all data that is required for processing the network request exists or not and whether the network request is a first time request. The prediction optimization application370determines one or more parameters that are associated with processing the network request and may optimize the quantum machine learning models utilized by the system. If the network request is a first time request, the network request is directed to dynamic weightage processing application380, where the weightage is calculated for the network request and is stored in the data repository390. The weightage inductor385may extract the weightage for the network request from the data repository390and may process the network request based on the weightage.

Computer-executable program code for carrying out operations of embodiments of the present invention may be written in an object oriented, scripted or unscripted programming language. However, the computer program code for carrying out operations of embodiments of the present invention may also be written in conventional procedural programming languages, such as the “C” programming language or similar programming languages.