System, method, and computer program product for implementing a hybrid deep neural network model to determine a market strategy

Provided is a computer-implemented method for implementing a hybrid deep neural network. The method may include generating a first model comprising a generalized matrix factorization model, the generalized matrix factorization model configured to determine one or more latent factors based on receiving transaction data associated with one or more payment transactions; generating a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers; generating a combined model; and determining a rating for a payment account based on transaction data associated with a plurality of payment transactions, wherein the rating comprises an indication that the payment account will be used to conduct a plurality of payment transactions involving a merchant, and wherein the transaction data comprises merchant transaction data and user transaction data. A system and computer program product are also provided.

BACKGROUND

This disclosure relates generally to systems, devices, products, apparatus, and methods that are used for implementing a hybrid deep neural network model, and in one particular embodiment, to a system, product, and method for implementing a hybrid deep neural network model to determine a market strategy.

2. Technical Considerations

Machine learning may be a field of computer science that uses statistical techniques to provide a computer system with the ability to learn (e.g., to progressively improve performance of) a task with data without the computer system being explicitly programmed to perform the task. In some instances, a machine learning model may be developed based on a set of data so that the machine learning model may perform a task (e.g., a task associated with a prediction) with regard to the set of data.

In some instances, a machine learning model, such as a predictive machine learning model, may be used to make a prediction regarding a risk or an opportunity based on data. A predictive machine learning model may be used to analyze a relationship between the performance of a unit based on data associated with the unit and one or more known features of the unit. The objective of the predictive machine learning model may be to assess the likelihood that a similar unit will exhibit the performance of the unit. A predictive machine learning model may be used as a fraud detection model. For example, predictive machine learning models may perform calculations based on data associated with payment transactions to evaluate the risk or opportunity of a payment transaction involving a customer, in order to guide a decision of whether to authorize the payment transaction.

Multiple businesses (e.g., multiple merchants) may be involved in one or more transactions involving a customer associated with an account (e.g., a credit card account, a debit card account, and/or the like). The one or more transactions may each have similar features (e.g., the one or more transactions may be associated with a price, a time at which the transaction was initiated, and/or the like). However, a financial institution and/or a transaction service provider may be unable to accurately determine an alignment between the account and a subsequent transaction in regard to activity of the account by a customer involving one or more merchants. For example, the financial institution and/or the transaction service provider may be unable to accurately determine whether the user will conduct a payment transaction that involves a first merchant or a second merchant based on the account activity. Accordingly, the financial institution and/or the transaction service provider may transmit offers to the customer that are ineffective at encouraging the customer to conduct a payment transaction. By transmitting offers that are ineffective, network resources and/or processing resources may be wasted as compared to transmitting a smaller number of offers that are effective.

SUMMARY

Accordingly, disclosed are systems, devices, products, apparatus, and/or methods for implementing a hybrid deep neural network model to determine a market strategy.

According to a non-limiting aspect or embodiment, provided is a computer-implemented method for implementing a hybrid deep neural network model to determine a market strategy. The method may include generating, by at least one processor, a first model comprising a generalized matrix factorization model, the generalized matrix factorization model configured to determine one or more latent factors based on receiving transaction data associated with one or more payment transactions; generating, by at least one processor, a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers; generating, by at least one processor, a combined model, wherein the combined model comprises a combination of the first model, the second model, and a data input, wherein generating the combined model comprises concatenating the first model, the second model, and the data input; and determining, by at least one processor, a rating for a payment account based on transaction data associated with a plurality of payment transactions, wherein the rating comprises an indication that the payment account will be used to conduct a plurality of payment transactions involving a merchant, and wherein the transaction data comprises merchant transaction data and user transaction data.

According to a non-limiting aspect or embodiment, provided is a system for implementing a hybrid deep neural network model to determine a market strategy. The system may include at least one processor programmed or configured to: generate a first model comprising a generalized matrix factorization model, the generalized matrix factorization model configured to determine one or more latent factors based on receiving transaction data associated with one or more payment transactions; generate a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers, wherein the plurality of hidden layers of the second model comprise a first hidden layer configured to provide an output having a first amount of dimensions to a second hidden layer, the second hidden layer configured to provide an output having a second amount of dimensions less than the first amount of dimensions; generate a combined model, wherein the combined model comprises a combination of the first model, the second model, and feature data associated with one or more features, wherein generating the combined model comprises concatenating the first model, the second model, and the feature data; and determine a rating for a payment account based on transaction data associated with a plurality of payment transactions, wherein the rating comprises an indication that the payment account will be used to conduct a plurality of payment transactions involving a merchant, and wherein the transaction data comprises merchant transaction data and user transaction data

According to a non-limiting aspect or embodiment, provided is a computer program product for implementing a hybrid deep neural network model to determine a market strategy. In some non-limiting embodiments or aspects, the computer program product may comprise at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to: generate a first model comprising a generalized matrix factorization model, the generalized matrix factorization model configured to determine one or more latent factors based on receiving transaction data associated with one or more payment transactions; generate a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers, wherein the plurality of hidden layers of the second model comprise a first hidden layer configured to provide an output having a first amount of dimensions to a second hidden layer, the second hidden layer configured to provide an output having a second amount of dimensions less than the first amount of dimensions; and generate a combined model, wherein the combined model comprises a combination of the first model, the second model, and feature data associated with one or more features, wherein generating the combined model comprises concatenating the first model, the second model, and the feature data.

Further embodiments or aspects are set forth in the following numbered clauses:

Clause 1: A method for implementing a hybrid deep neural network model to determine a market strategy, comprising: generating, by at least one processor, a first model comprising a generalized matrix factorization model, the generalized matrix factorization model configured to determine one or more latent factors based on receiving transaction data associated with one or more payment transactions; generating, by at least one processor, a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers; generating, by at least one processor, a combined model, wherein the combined model comprises a combination of the first model, the second model, and a data input, wherein generating the combined model comprises concatenating the first model, the second model, and the data input; and determining, by at least one processor, a rating for a payment account based on transaction data associated with a plurality of payment transactions, wherein the rating comprises an indication that the payment account will be used to conduct a plurality of payment transactions involving a merchant, and wherein the transaction data comprises merchant transaction data and user transaction data.

Clause 2: The method of clause 1, wherein the plurality of hidden layers of the second model comprise a first hidden layer configured to provide an output having a first amount of dimensions to a second hidden layer, the second hidden layer configured to provide an output having a second amount of dimensions less than the first amount of dimensions.

Clause 3: The method of clauses 1 or 2, wherein the generalized matrix factorization model is configured to determine 16 latent factors.

Clause 4: The method of any of clauses 1-3, wherein the data input comprises feature data associated with one or more features to be provided to a data input layer of a neural network model.

Clause 5: The method of any of clauses 1-4, wherein the transaction data is first transaction data, and wherein generating the first model comprises: training the first model based on second transaction data; and wherein generating the second model comprises: training the second model based on the second transaction data.

Clause 6: The method of any of clauses 1-5, wherein the merchant transaction data comprises: merchant category data associated with a merchant category of a payment transaction; occurrence data associated with an occurrence of a payment transaction involving a merchant; and country identification data associated with an identifier of a country.

Clause 7: The method of any of clauses 1-6, further comprising: applying, with at least one processor, an activation function that is a rectifier to an output of the combined model.

Clause 8: The method of any of clauses 1-7, wherein generating the first model comprises: applying, with at least one processor, a regularization function that is a dropout function to data associated with an input to be provided to an input layer of the first model, wherein the dropout function has a predetermined probability.

Clause 9: The method of any of clauses 1-8, wherein generating the second model comprises: applying, with at least one processor, a regularization function that is a dropout function to data associated with an input to be provided to an input layer of the second model, wherein the dropout function has a predetermined probability.

Clause 10: A system for implementing a hybrid deep neural network model to determine a market strategy, comprising: at least one processor programmed or configured to: generate a first model comprising a generalized matrix factorization model, the generalized matrix factorization model configured to determine one or more latent factors based on receiving transaction data associated with one or more payment transactions; generate a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers, wherein the plurality of hidden layers of the second model comprise a first hidden layer configured to provide an output having a first amount of dimensions to a second hidden layer, the second hidden layer configured to provide an output having a second amount of dimensions less than the first amount of dimensions; generate a combined model, wherein the combined model comprises a combination of the first model, the second model, and feature data associated with one or more features, wherein generating the combined model comprises concatenating the first model, the second model, and the feature data; and determine a rating for a payment account based on transaction data associated with a plurality of payment transactions, wherein the rating comprises an indication that the payment account will be used to conduct a plurality of payment transactions involving a merchant, and wherein the transaction data comprises merchant transaction data and user transaction data.

Clause 11: The system of clause 10, wherein the transaction data is first transaction data, wherein, when generating the first model, the at least one processor is programmed or configured to: train the first model based on second transaction data; and wherein, when generating the second model, the at least one processor is programmed or configured to: train the second model based on the second transaction data.

Clause 12: The system of clauses 10 or 11, wherein the merchant transaction data comprises: merchant category data associated with a merchant category of a payment transaction; occurrence data associated with an occurrence of a payment transaction involving a merchant; and country identification data associated with an identifier of a country.

Clause 13: The system of any of clauses 10-12, wherein the at least one processor is further programmed or configured to: apply an activation function that is a rectifier to an output of the combined model.

Clause 14: The system of any of clauses 10-13, wherein, when generating the first model, the at least one processor is programmed or configured to: apply a regularization function that is a dropout function to data associated with an input to be provided to an input layer of the first model, wherein the dropout function has a predetermined probability.

Clause 15: The system of any of clauses 10-14, wherein, when generating the second model, the at least one processor is programmed or configured to: apply a regularization function that is a dropout function to data associated with an input to be provided to an input layer of the second model, wherein the dropout function has a predetermined probability.

Clause 16: A computer program product for implementing a hybrid deep neural network model to determine a market strategy, the computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to: generate a first model comprising a generalized matrix factorization model, the generalized matrix factorization model configured to determine one or more latent factors based on receiving transaction data associated with one or more payment transactions; generate a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers, wherein the plurality of hidden layers of the second model comprise a first hidden layer configured to provide an output having a first amount of dimensions to a second hidden layer, the second hidden layer configured to provide an output having a second amount of dimensions less than the first amount of dimensions; and generate a combined model, wherein the combined model comprises a combination of the first model, the second model, and feature data associated with one or more features, wherein generating the combined model comprises concatenating the first model, the second model, and the feature data.

Clause 17: The computer program product of clause 16, wherein the one or more instructions further cause the at least one processor to: determine a rating for a payment account based on transaction data associated with a plurality of payment transactions, wherein the rating comprises an indication that the payment account will be used to conduct a plurality of payment transactions involving a merchant, and wherein the transaction data comprises merchant transaction data and user transaction data

Clause 18: The computer program product of clauses 16 or 17, wherein the transaction data is first transaction data, wherein, the one or more instructions that cause the at least one processor to generate the first model cause the at least one processor to: train the first model based on second transaction data; and wherein the one or more instructions that cause the at least one processor to generate the second model cause the at least one processor to: train the second model based on the second transaction data.

Clause 19: The computer program product of any of clauses 16-18, wherein the one or more instructions that further cause the at least one processor to: apply an activation function that is a rectifier to an output of the combined model.

Clause 20: The computer program product of any of clauses 16-19, wherein the one or more instructions that cause the at least one processor to generate the first model further cause the at least one processor to: apply a regularization function that is a dropout function to data associated with an input to be provided to an input layer of the first model, wherein the dropout function has a predetermined probability.

DESCRIPTION

As used herein, the terms “issuer,” “issuer institution,” “issuer bank,” or “payment device issuer” may refer to one or more entities that provide accounts to individuals (e.g., users, customers, and/or the like) for conducting payment transactions such as credit payment transactions and/or debit payment transactions. For example, an issuer institution may provide an account identifier, such as a primary account number (PAN), to a customer that uniquely identifies one or more accounts associated with that customer. In some non-limiting embodiments or aspects, an issuer may be associated with a bank identification number (BIN) that uniquely identifies the issuer institution. As used herein, the term “issuer system” may refer to one or more computer systems operated by or on behalf of an issuer, such as a server executing one or more software applications. For example, an issuer system may include one or more authorization servers for authorizing a transaction.

As used herein, the term “account identifier” may include one or more types of identifiers associated with an account (e.g., a PAN associated with an account, a card number associated with an account, a payment card number associated with an account, a token associated with an account, and/or the like). In some non-limiting embodiments or aspects, an issuer may provide an account identifier (e.g., a PAN, a token, and/or the like) to a user (e.g., an account holder) that uniquely identifies one or more accounts associated with that user. The account identifier may be embodied on a payment device (e.g., a physical instrument used for conducting payment transactions, such as a payment card, a credit card, a debit card, a gift card, and/or the like) and/or may be electronic information communicated to the user that the user may use for electronic payment transactions. In some non-limiting embodiments or aspects, the account identifier may be an original account identifier, where the original account identifier was provided to a user at the creation of the account associated with the account identifier. In some non-limiting embodiments or aspects, the account identifier may be a supplemental account identifier, which may include an account identifier that is provided to a user after the original account identifier was provided to the user. For example, if the original account identifier is forgotten, stolen, and/or the like, a supplemental account identifier may be provided to the user. In some non-limiting embodiments or aspects, an account identifier may be directly or indirectly associated with an issuer institution such that an account identifier may be a token that maps to a PAN or other type of account identifier. Account identifiers may be alphanumeric, any combination of characters and/or symbols, and/or the like.

As used herein, the term “token” may refer to an account identifier of an account that is used as a substitute or replacement for another account identifier, such as a PAN. Tokens may be associated with a PAN or other original account identifier in one or more data structures (e.g., one or more databases) such that they may be used to conduct a payment transaction without directly using an original account identifier. In some non-limiting embodiments or aspects, an original account identifier, such as a PAN, may be associated with a plurality of tokens for different individuals or purposes. In some non-limiting embodiments or aspects, tokens may be associated with a PAN or other account identifiers in one or more data structures such that they can be used to conduct a transaction without directly using the PAN or the other account identifiers. In some examples, an account identifier, such as a PAN, may be associated with a plurality of tokens for different uses or different purposes.

As used herein, the term “merchant” may refer to one or more entities (e.g., operators of retail businesses) that provide goods, services, and/or access to goods and/or services, to a user (e.g., a customer, a customer of a merchant, a consumer, and/or the like) based on a transaction such as a payment transaction. As used herein, the term “merchant system” may refer to one or more computer systems operated by or on behalf of a merchant, such as a server executing one or more software applications. As used herein, the term “product” may refer to one or more goods and/or services offered by a merchant.

As used herein, the term “point-of-sale (POS) device” may refer to one or more electronic devices, which may be used by a merchant to conduct a transaction (e.g., a payment transaction) and/or process a transaction. Additionally or alternatively, a POS device may include peripheral devices, card readers, scanning devices (e.g., code scanners and/or the like), Bluetooth® communication receivers, near-field communication (NFC) receivers, radio frequency identification (RFID) receivers, and/or other contactless transceivers or receivers, contact-based receivers, payment terminals, and/or the like.

As used herein, the term “point-of-sale (POS) system” may refer to one or more client devices and/or peripheral devices used by a merchant to conduct a transaction. For example, a POS system may include one or more POS devices and/or other like devices that may be used to conduct a payment transaction. In some non-limiting embodiments or aspects, a POS system (e.g., a merchant POS system) may include one or more server computers programmed or configured to process online payment transactions through webpages, mobile applications, and/or the like.

As used herein, the term “transaction service provider” may refer to an entity that receives transaction authorization requests from merchants or other entities and provides guarantees of payment, in some cases through an agreement between the transaction service provider and an issuer institution. In some non-limiting embodiments or aspects, a transaction service provider may include a credit card company, a debit card company, a payment network such as Visa®, MasterCard®, AmericanExpress®, or any other entity that processes transaction. As used herein, the term “transaction service provider system” may refer to one or more computer systems operated by or on behalf of a transaction service provider, such as a transaction service provider system executing one or more software applications. A transaction service provider system may include one or more processors and, in some non-limiting embodiments or aspects, may be operated by or on behalf of a transaction service provider.

As used herein, the term “payment device” may refer to a payment card (e.g., a credit or debit card), a gift card, a smart card (e.g., a chip card, an integrated circuit card, and/or the like), smart media, a payroll card, a healthcare card, a wristband, a machine-readable medium containing account information, a keychain device or fob, an RFID transponder, a retailer discount or loyalty card, and/or the like. The payment device may include a volatile or a non-volatile memory to store information (e.g., an account identifier, a name of the account holder, and/or the like).

As used herein, the term “computing device” may refer to one or more electronic devices that are configured to directly or indirectly communicate with or over one or more networks. In some non-limiting embodiments or aspects, a computing device may include a mobile device. A mobile device may include a smartphone, a portable computer, a wearable device (e.g., watches, glasses, lenses, clothing, and/or the like), a personal digital assistant (PDA), and/or other like devices. In some non-limiting embodiments or aspects, a computing device may include a server, a desktop computer, and/or the like.

As used herein, the terms “client” and “client device” may refer to one or more computing devices, such as processors, storage devices, and/or similar computer components, that access a service made available by a server. In some non-limiting embodiments or aspects, a “client device” may refer to one or more devices that facilitate payment transactions, such as one or more POS devices used by a merchant. In some non-limiting embodiments or aspects, a client device may include a computing device configured to communicate with one or more networks and/or facilitate payment transactions such as, but not limited to, one or more desktop computers, one or more mobile devices, and/or other like devices. Moreover, a “client” may also refer to an entity, such as a merchant, that owns, utilizes, and/or operates a client device for facilitating payment transactions with a transaction service provider.

As used herein, the term “server” may refer to one or more computing devices, such as processors, storage devices, and/or similar computer components that communicate with client devices and/or other computing devices over a network, such as the Internet or private networks and, in some examples, facilitate communication among other servers and/or clients.

As used herein, the term “system” may refer to one or more computing devices or combinations of computing devices such as, but not limited to, processors, servers, client devices, software applications, and/or other like components. In addition, reference to “a server” or “a processor,” as used herein, may refer to a previously-recited server and/or processor that is recited as performing a previous step or function, a different server and/or processor, and/or a combination of servers and/or processors. For example, as used in the specification and the claims, a first server and/or a first processor that is recited as performing a first step or function may refer to the same or different server and/or a processor recited as performing a second step or function.

In some non-limiting embodiments or aspects, computer-implemented methods, systems, and computer program products for implementing a hybrid deep neural network model to determine a market strategy using a machine learning model architecture are disclosed. For example, a computer-implemented method may include generating, by at least one processor, a first model comprising a generalized matrix factorization model, the generalized matrix factorization model configured to determine one or more latent factors based on receiving transaction data associated with one or more payment transactions; generating, by at least one processor, a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers; generating, by at least one processor, a combined model, wherein the combined model comprises a combination of the first model, the second model, and a data input, wherein generating the combined model comprises concatenating the first model, the second model, and the data input; and determining, by at least one processor, a rating for a payment account based on transaction data associated with a plurality of payment transactions, wherein the rating comprises an indication that the payment account will be used to conduct a plurality of payment transactions involving a merchant, and wherein the transaction data comprises merchant transaction data and user transaction data.

In this way, non-limiting embodiments of the present disclosure may accurately determine an alignment between an account and one or more merchants, where a user of the account is more likely to initiate a transaction with the one or more merchants than another one or more merchants. Accordingly, a financial institution and/or a transaction service provider may be able to transmit offers to the customer that are effective at encouraging the customer to conduct a payment transaction involving the one or more merchants. In particular, the disclosed systems, methods, and computer program products identify latent features that are encompassed by one or more payment transactions, thereby importing contextual data into one or more predictions (e.g., one or more predictions as to what offers would be effective if provided to a customer to cause the customer to initiate a payment transaction with a merchant). In this way, network resources and/or processing resources may be conserved as compared to transmitting a larger number of offers that are ineffective. Additionally, the described machine learning model architectures can be divided into sub-architectures (e.g., a first model architecture associated with a first machine learning model, a second model architecture associated with a second machine learning model, and a third model architecture associated with a third machine learning model, and/or the like), enabling the machine learning model architecture to divide the processing performed by the machine learning model across multiple systems (e.g., multiple computing devices), including one or more graphical processing units (GPUs), allowing for parallel processing of the data provided as input to the machine learning model. By processing the data provided as input in parallel, the computation time and resources needed to process the input data (e.g., when training one or more machine learning models) may be reduced (e.g., from 10 days to 3 days).

Referring now toFIG.1,FIG.1is a diagram of an example environment100in which systems, devices, products, apparatus, and/or methods described herein may be implemented. As shown inFIG.1, environment100includes merchant system106, transaction service provider system102, acquirer system110, issuer system108, and user device104. In some non-limiting embodiments or aspects, merchant system106, transaction service provider system102, acquirer system110, issuer system108, and user device104may interconnect (e.g., establish a connection to communicate, and/or the like) via wired connections, wireless connections, or a combination of wired and wireless connections.

Transaction service provider system102may include one or more devices capable of being in communication with merchant system106, acquirer system110, issuer system108, and/or user device104via communication network112. For example, transaction service provider system102may include a server (e.g., a transaction processing server), a group of servers (e.g., a group of transaction processing servers), and/or other like devices. In some non-limiting embodiments or aspects, transaction service provider system102may be associated with a transaction service provider, as described herein.

User device104may include one or more devices capable of being in communication with merchant system106, transaction service provider system102, acquirer system110, and/or issuer system108via communication network112. For example, user device104may include one or more computing devices, such as one or more mobile devices, one or more smartphones, one or more wearable devices, one or more servers, and/or the like. In some non-limiting embodiments or aspects, user device104may communicate via a short-range wireless communication connection. In some non-limiting embodiments or aspects, user device104may be associated with a customer as described herein.

Merchant system106may include one or more devices capable of being in communication with transaction service provider system102, acquirer system110, issuer system108, and user device104via communication network112. For example, merchant system106may include one or more payment devices, one or more computing devices, such as one or more mobile devices, one or more smartphones, one or more wearable devices (e.g., watches, glasses, lenses, clothing, and/or the like), one or more PDAs, one or more servers, and/or the like. In some non-limiting embodiments or aspects, merchant system106may communicate via a short-range wireless communication connection (e.g., a wireless communication connection for communicating information in a range between 2 to 3 centimeters to 5 to 6 meters, such as an NFC communication connection, an RFID communication connection, a Bluetooth® communication connection, and/or the like). In some non-limiting embodiments or aspects, merchant system106may be associated with a merchant, as described herein.

Issuer system108may include one or more devices capable of being in communication with merchant system106, transaction service provider system102, acquirer system110, and/or user device104via communication network112. For example, issuer system108may include one or more computing devices, such as one or more servers, and/or other like devices. In some non-limiting embodiments or aspects, issuer system108may be associated with an issuer institution that issued a payment account and/or instrument (e.g., a credit account, a debit account, a credit card, a debit card, and/or the like) to a customer.

Acquirer system110may include one or more devices capable of being in communication with merchant system106, transaction service provider system102, issuer system108, and/or user device104via communication network112. For example, acquirer system110may include one or more computing devices, such as one or more servers, and/or other like devices. In some non-limiting embodiments or aspects, acquirer system110may be associated with an acquirer, as described herein.

The number and arrangement of systems and/or devices shown inFIG.1are provided as an example. There may be additional systems and/or devices, fewer systems and/or devices, different systems and/or devices, or differently arranged systems and/or devices than those shown inFIG.1. Furthermore, two or more systems and/or devices shown inFIG.1may be implemented within a single system or a single device, or a single system or a single device shown inFIG.1may be implemented as multiple, distributed systems or devices. Additionally or alternatively, a set of systems or a set of devices (e.g., one or more systems, one or more devices) of environment100may perform one or more functions described as being performed by another set of systems or another set of devices of environment100.

Referring now toFIG.2, illustrated is a diagram of example components of device200. Device200may correspond to one or more devices of transaction service provider system102, one or more devices of merchant system106(e.g., one or more devices of a device of merchant system106), one or more devices of acquirer system110, one or more devices of issuer system108, and/or one or more devices of user device104. In some non-limiting aspects or embodiments, one or more devices of transaction service provider system102, one or more devices of merchant system106, one or more devices of acquirer system110, one or more devices of issuer system108, and/or one or more devices of user device104may include at least one device200and/or at least one component of device200. As shown inFIG.2, device200may include bus202, processor204, memory206, storage component208, input component210, output component212, and communication interface214.

Bus202may include a component that permits communication among the components of device200. In some non-limiting aspects or embodiments, processor204may be implemented in hardware, software, or a combination of hardware and software. For example, processor204may include a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), and/or the like), a microprocessor, a digital signal processor (DSP), and/or any processing component (e.g., a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), and/or the like) that can be programmed to perform a function. Memory206may include random access memory (RAM), read-only memory (ROM), and/or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, optical memory, and/or the like) that stores information and/or instructions for use by processor204.

Input component210may include a component that permits device200to receive information, such as via user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, a camera, and/or the like). Additionally or alternatively, input component210may include a sensor for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, an actuator, and/or the like). Output component212may include a component that provides output information from device200(e.g., a display, a speaker, one or more light-emitting diodes (LEDs), and/or the like).

Communication interface214may include a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, and/or the like) that enables device200to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communication interface214may permit device200to receive information from another device and/or provide information to another device. For example, communication interface214may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi® interface, a cellular network interface, a Bluetooth® interface, a Zigbee® interface, and/or the like.

Device200may perform one or more processes described herein. Device200may perform these processes based on processor204executing software instructions stored by a computer-readable medium, such as memory206and/or storage component208. A computer-readable medium (e.g., a non-transitory computer-readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes memory space located inside of a single physical storage device or memory space spread across multiple physical storage devices.

Software instructions may be read into memory206and/or storage component208from another computer-readable medium or from another device via communication interface214. When executed, software instructions stored in memory206and/or storage component208may cause processor204to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments or aspects described herein are not limited to any specific combination of hardware circuitry and software.

Memory206and/or storage component208may include data storage or one or more data structures (e.g., a database, and/or the like). Device200may be capable of retrieving information from, storing information in, or searching information stored in the data storage or one or more data structures in memory206and/or storage component208. For example, the information may include encryption data, input data, output data, transaction data, account data, or any combination thereof.

Referring now toFIG.3, illustrated is a flowchart of a non-limiting embodiment of a process300for implementing a hybrid deep neural network model to determine a market strategy. In some non-limiting aspects or embodiments, one or more of the functions described with respect to process300may be performed (e.g., completely, partially, and/or the like) by transaction service provider system102. In some non-limiting embodiments or aspects, one or more of the steps of process300may be performed (e.g., completely, partially, and/or the like) by another device or a group of devices separate from and/or including transaction service provider system102such as, for example, user device104, merchant system106, issuer system108, and/or acquirer system110.

As shown inFIG.3, at step302, process300may include generating a first model. For example, transaction service provider system102may generate a first model. In such an example, the first model may be the same as and/or similar to a collaborative filtering structure (e.g., a filtering structure that determines predictions based on combinations of preferences of a user). In some non-limiting embodiments or aspects, transaction service provider system102may generate the first model based on transaction data associated with one or more payment transactions (e.g., first transaction data associated with one or more payment transactions). For example, transaction service provider system102may generate the first model based on the first transaction data associated with one or more payment transactions, where the first transaction data includes merchant data associated with a merchant (e.g., a merchant associated with merchant system106) and/or account data associated with an account of one or more users (e.g., users associated with one or more user devices104-1-104-n, collectively referred to as user of user device104). In some non-limiting embodiments or aspects, transaction service provider system102may generate the first model by training the first model using transaction data associated with the one or more payment transactions. For example, transaction service provider system102may generate the first model by training the first model using the first transaction data associated with one or more payment transactions.

In some non-limiting embodiments or aspects, transaction service provider system102may generate the first model, where the first model includes a generalized factorization model. Transaction service provider system102may configure (e.g., configure to be used) the generalized factorization model to determine one or more latent factors (e.g., 16 latent factors) of one or more payment transactions. For example, transaction service provider system102may configure the generalized factorization model to determine one or more latent factors of the one or more payment transactions based on transaction service provider system102providing first transaction data associated with one or more payment transactions as input to the generalized factorization model. In such an example, transaction service provider system102may provide the first transaction data associated with the one or more payment transactions.

In some non-limiting embodiments or aspects, transaction service provider system102may generate the first model by applying a regularization function to data provided as input to an input layer of the first model. For example, transaction service provider system102may generate the first model by applying the regularization function to data provided as input to an input layer of the first model, where the regularization function is a dropout function. In such an example, transaction service provider system102may apply the dropout function to the data provided as input to the input layer of the first model to regularize the data provided as input, thereby reducing the likelihood that the first model will be overfit when generated by transaction service provider system102. In some non-limiting embodiments or aspects, the dropout function may have a predetermined probability.

As shown inFIG.3, at step304, process300may include generating a second model. For example, transaction service provider system102may generate a second model. In some non-limiting embodiments or aspects, transaction service provider system102may generate the second model based on first transaction data associated with one or more payment transactions. For example, transaction service provider system102may generate the second model based on first transaction data that may include merchant data associated with a merchant (e.g., a merchant associated with merchant system106) and/or account data associated with an account of one or more users (e.g., a user associated with user device104). In some non-limiting embodiments or aspects, transaction service provider system102may generate the second model by training the second model based on transaction data associated with one or more payment transactions. For example, transaction service provider system102may generate the second model by training the second model based on the first transaction data associated with the one or more payment transactions.

In some non-limiting embodiments or aspects, transaction service provider system102may generate the second model, where the second model includes a deep neural network model. For example, transaction service provider system102may generate the second model, where the second model includes a deep neural network model, the deep neural network model including one or more hidden layers. In such an example, the one or more hidden layers of the deep neural network model may include a first hidden layer configured to (e.g., configured to be used to) provide an output having a first amount of dimensions to a second hidden layer, the second hidden layer configured to provide an output having a second amount of dimensions that are less than the first amount of dimensions to a third hidden layer. In some non-limiting embodiments or aspects, the deep neural network model may have more or fewer hidden layers that are configured to provide outputs having amounts of dimensions that are greater than, less than, or equal to the amount of dimensions of any other hidden layer. In some non-limiting embodiments or aspects, transaction service provider system102may generate the second model, where the second model includes a deep neural network model, the deep neural network model including one or more hidden layers, where each successive hidden layer is configured to receive input having a number of dimensions that is greater than or equal to an amount of dimensions of an output provided by the successive hidden layer.

In some non-limiting embodiments or aspects, transaction service provider system102may generate the second model by applying a regularization function to data provided as input to an input layer of the second model. For example, transaction service provider system102may generate the second model by applying the regularization function to data provided as input to an input layer of the second model, where the regularization function is a dropout function. In such an example, transaction service provider system102may apply the dropout function to the data provided as input to the input layer of the second model to regularize the data provided as input, thereby reducing the likelihood that the first model will be overfit when generated by transaction service provider system102. In some non-limiting embodiments or aspects, the dropout function may have a predetermined probability.

As shown inFIG.3, at step306, process300may include generating a combined model. For example, transaction service provider system102may generate a combined model. In some non-limiting embodiments or aspects, transaction service provider system102may generate the combined model by combining the first model, the second model, and feature data associated with one or more features. For example, transaction service provider system102may generate the combined model by concatenating the first model, the second model, and the feature data associated with the one or more features.

In some non-limiting embodiments or aspects, the feature data associated with the one or more features may be provided to a data input layer of a neural network model. For example, transaction service provider system102may provide the feature data associated with the one or more features to a first data input layer of a neural network. In some non-limiting embodiments or aspects, transaction service provider system102may additionally provide an output of the first model and/or an output of the second model to the first data input layer of the neural network.

In some non-limiting embodiments or aspects, transaction service provider system102may provide an output of the first layer of the neural network to a second layer of the neural network. In such an example, the second layer of the neural network may provide, as an output, a combined model output. In some non-limiting embodiments or aspects, transaction service provider system102may provide the combined model output as input to an activation function. For example, transaction service provider system102may provide the combined model output as input to an activation function, where the activation function is a rectifier to the combined model output.

In some non-limiting embodiments or aspects, the combined model output may include transaction data associated with one or more payment transactions. For example, the combined model output may include transaction data associated with one or more payment transactions, where at least one payment transaction is a predicted payment transaction involving a user of an account and a merchant associated with a merchant identifier. In another example, the combined model output may include transaction data associated with a plurality of payment transactions. In some non-limiting embodiments or aspects, the combined model output may include feature data associated with one or more features of one or more payment transactions.

As shown inFIG.3, at step308, process300may include determining a rating for a payment account. For example, transaction service provider system102may determine a rating for a payment account. In some non-limiting embodiments or aspects, transaction service provider system102may determine a rating for a payment account based on transaction data associated with one or more payment transactions. For example, transaction service provider system102may determine a rating for a payment account based on providing the transaction data associated with the one or more payment transactions (e.g., one or more payment transactions involving accounts of one or more users and one or more merchants) to the combined model. In such an example, transaction service provider system102may receive a combined model output based on providing the transaction data associated with the one or more transactions to the combined model. In some non-limiting embodiments or aspects, the combined model output may include the transaction data associated with the one or more payment transactions that were provided by transaction service provider system102to the combined model and/or predicted transaction data associated with one or more predicted payment transactions. Transaction service provider system102may then determine the rating for the payment account based on the combined model output.

In some non-limiting embodiments or aspects, the rating for the payment account may include an indication that the payment account will be used to conduct one or more predicted payment transactions. For example, the rating for the payment account may include an indication that the payment account will be used to conduct one or more payment transactions involving a user of an account and a merchant. In some non-limiting embodiments or aspects, transaction data associated with one or more payment transactions may include merchant transaction data and user transaction data.

In some non-limiting embodiments or aspects, transaction service provider system102may determine merchant transaction data associated with a future transaction. For example, transaction service provider system102may determine the merchant transaction data based on determining the predicted payment transaction and/or based on the combined model output. In some non-limiting embodiments or aspects, merchant transaction data associated with a future transaction may include merchant category data associated with a merchant category (e.g., a merchant category of a payment transaction), occurrence data associated with an occurrence of a payment transaction (e.g., a payment transaction involving a merchant and/or one or more users), and/or country identification data associated with an identifier of one or more countries.

Referring now toFIGS.4A-4D, illustrated is a diagram of an implementation400of a process (e.g., process300) for implementing a hybrid deep neural network model to determine a market strategy. As illustrated inFIGS.4A-4D, implementation400may include first machine learning model400awhich may include account data410, embedding layer412, merchant data414, embedding layer416, multiply layer418, first dense layer420, and first model output422. Implementation400may also include second machine learning model400bwhich may include account data410, embedding layer412, merchant data414, embedding layer416, concatenate layer424, first dense layer426, second dense layer428, third dense layer430, fourth dense layer432, and second model output434. Implementation400may also include third machine learning model400cwhich may include feature data436(as shown inFIG.4D), first dense layer438, second dense layer440, and combined model output442. In some non-limiting embodiments or aspects, machine learning model architecture400may be included in transaction service provider system102.

As shown by reference number402inFIG.4A, transaction service provider system102may generate a first machine learning model400a. For example, transaction service provider system102may generate a first model, the first model including account data410, embedding layer412, merchant data414, embedding layer416, multiply layer418, and first dense layer420. In some non-limiting embodiments or aspects, account data410may include account data associated with an account. For example, account data410may include account data associated with an account, the account being associated with one or more account identifiers (e.g., a credit card number, an account number, and/or the like). For example, account data associated with one or more accounts may include data associated with one or more payment transactions that include an account identifier, a merchant identifier, and/or rating data associated with a rating (e.g., card 123x may assign a rating of “1” to a merchant “A”; card 123x may assign a rating of “1” to a merchant “B”, card 123x may assign a rating of “3” to a merchant “C”, card 456x may assign a rating of “1” to the merchant “A”, and/or the like, where the ratings represent a percent of spending involving the user of the account and the merchant involved in the payment transaction as compared to a total spending amount of the user). In some non-limiting embodiments or aspects, the account data associated with the account may include feature data associated with one or more features, the one or more features associated with one or more market segments. For example, the one or more market segments may include a market segment associated with a telecommunication industry, a market segment associated with an insurance industry, a market segment associated with a food and/or grocery industry, a market segment associated with a transportation industry, a market segment associated with a fuel industry, a market segment associated with a restaurant industry, a market segment associated with a quick service restaurant industry, a market segment associated with an entertainment industry, a market segment associated with a professional services industry, a market segment associated with a retail goods industry, a market segment associated with an apparel and accessories industry, a market segment associated with an education and/or a government industry, a market segment associated with a department store industry, a market segment associated with a retail services industry, a market segment associated with an electronics industry, a market segment associated with a health care industry, a market segment associated with a drug store and/or pharmacy industry, a market segment associated with a discount store industry, a market segment associated with a direct marketing industry, a market segment associated with a lodging industry, a market segment associated with a business to business industry, a market segment associated with a home improvement and/or supply industry, a market segment associated with a travel service industry, a market segment associated with an automotive industry, a market segment associated with a vehicle rental industry, and/or the like. In some non-limiting embodiments or aspects, embedding layer412may be configured to receive account data410as an input to embedding layer412. In some non-limiting embodiments or aspects, embedding layer412may be configured to determine an embedding (e.g., a vector including one or more dimensions, each dimension further including a value). For example, embedding layer412may be configured to determine an embedding including 64 dimensions based on (e.g., in response to) embedding layer412receiving account data410, with each dimension of the embedding including a value. In some non-limiting embodiments or aspects, embedding layer412may be configured to provide an output (e.g., the embedding determined based on the account data410) of embedding layer412to multiply layer418. In some non-limiting embodiments or aspects, embedding layer412may be a neural network including one or more hidden layers, including one or more embedding layers.

As shown byFIG.4A, merchant data414may include merchant data associated with a merchant. For example, merchant data414may include merchant data associated with a merchant, the merchant associated with a merchant category code (MCC) and/or a merchant identifier (e.g., a unique number that identifies the merchant). In some non-limiting embodiments or aspects, merchant data414may be the same as, or similar to, account data410. In some non-limiting embodiments or aspects, embedding layer416may be configured to receive merchant data414as an input to embedding layer416. In some non-limiting embodiments or aspects, embedding layer416may be configured to determine an embedding. For example, embedding layer416may be configured to determine an embedding including 64 dimensions based on (e.g., in response to) receiving merchant data414, with each dimension of the embedding including a value. In some non-limiting embodiments or aspects, embedding layer416may be configured to provide an output (e.g., the embedding determined based on merchant data414) of embedding layer416to multiply layer418. In some non-limiting embodiments or aspects, embedding layer416may be a neural network including one or more hidden layers, including one or more embedding layers. In some non-limiting embodiments or aspects, the output of embedding layer416may include one or more values that collectively define a relationship between one or more users of one or more accounts and one or more merchants.

In some non-limiting embodiments or aspects, multiply layer418may be configured to receive the output from embedding layer412and embedding layer416. For example, multiply layer418may be configured to receive the output from embedding layer412and embedding layer416as inputs to multiply layer418. In some non-limiting embodiments or aspects, multiply layer418may be configured to multiply (e.g., via an element-wise multiplication operation) the output from embedding layer412and embedding layer416received as input at multiply layer418. For example, multiply layer418may be configured to multiply based on (e.g., in response to) receiving the output from embedding layer412and embedding layer416. In some non-limiting embodiments or aspects, multiply layer418may be configured to provide an output of multiply layer418to first dense layer420. For example, multiply layer418may be configured to provide an output of multiply layer418, where the output is a predicted rating for a future payment transaction.

In some non-limiting embodiments or aspects, first dense layer420(e.g., a first layer of first machine learning model400aincluding 128 nodes) may be configured to receive the output from multiply layer418. For example, first dense layer420may be configured to receive the output from multiply layer418as inputs to first dense layer420. In some non-limiting embodiments or aspects, first dense layer420may be configured to determine an output based on receiving the output from multiply layer418as input. For example, first dense layer420may be configured to determine an output based on receiving the output from multiply layer418as input based on (e.g., in response to) receiving the output from multiply layer418. In some non-limiting embodiments or aspects, first dense layer420may be configured to provide first model output422of first dense layer420. For example, first dense layer420may be configured to provide first model output422to first dense layer438of third machine learning model400c(seeFIG.4C). In some non-limiting embodiments or aspects, first dense layer420may be configured to receive input via one or more nodes and first dense layer420may be configured to provide output via 1 node (e.g., a scalar value).

As shown by reference number404inFIG.4B, transaction service provider system102may generate a second model. For example, transaction service provider system102may generate a second machine learning model400b, where second machine learning model400bincludes account data410, embedding layer412, merchant data414, embedding layer416, concatenate layer424, first dense layer426, second dense layer428, third dense layer430, and fourth dense layer432. In some non-limiting embodiments or aspects, embedding layer412may be configured to receive account data410as an input to embedding layer412. In some non-limiting embodiments or aspects, embedding layer412may be configured to provide an output of embedding layer412to concatenate layer424. Embedding layer416may be configured to receive merchant data414as an input to embedding layer416. In some non-limiting embodiments or aspects, embedding layer416may be configured to provide an output of embedding layer416to concatenate layer424.

In some non-limiting embodiments or aspects, concatenate layer424may be configured to receive the output from embedding layer412and embedding layer416. For example, concatenate layer424may be configured to receive the output from embedding layer412and embedding layer416as inputs to concatenate layer424. In some non-limiting embodiments or aspects, concatenate layer424may be configured to concatenate (e.g., append and/or link) the output of embedding layer412and embedding layer416. For example, concatenate layer424may be configured to concatenate the output of embedding layer412and embedding layer416based on (e.g., in response to) receiving the output from embedding layer412and embedding layer416. In some non-limiting embodiments or aspects, concatenate layer424may be configured to provide an output (e.g., an embedding including 64 dimensions) of concatenate layer424to first dense layer426.

In some non-limiting embodiments or aspects, first dense layer426may be configured to receive the output from concatenate layer424. For example, first dense layer426may be configured to receive the output from concatenate layer424as inputs to first dense layer426. In some non-limiting embodiments or aspects, first dense layer426may be configured to determine an output (e.g., values output via one or more nodes of first dense layer426) based on receiving the output from concatenate layer424as input. For example, first dense layer426may be configured to determine an output based on (e.g., in response to) receiving the output from concatenate layer424. In some non-limiting embodiments or aspects, first dense layer426may be configured to provide an output of first dense layer426to second dense layer428. In some non-limiting embodiments or aspects, first dense layer426may be configured to receive input via 64-128 nodes and first dense layer426may be configured to provide output via 32-64 nodes.

In some non-limiting embodiments or aspects, second dense layer428may be configured to receive the output from first dense layer426. For example, second dense layer428may be configured to receive the output from first dense layer426as inputs to second dense layer428. In some non-limiting embodiments or aspects, second dense layer428may be configured to determine an output. For example, second dense layer428may be configured to determine an output based on (e.g., in response to) receiving the output from first dense layer426. In some non-limiting embodiments or aspects, second dense layer428may be configured to provide an output of second dense layer428to third dense layer430. In some non-limiting embodiments or aspects, second dense layer428may be configured to receive input via 32-64 nodes and second dense layer428may be configured to provide output via 16-32 nodes.

In some non-limiting embodiments or aspects, third dense layer430may be configured to receive the output from second dense layer428. For example, third dense layer430may be configured to receive the output from second dense layer428as inputs to third dense layer430. In some non-limiting embodiments or aspects, third dense layer430may be configured to determine an output. For example, third dense layer430may be configured to determine an output based on (e.g., in response to) receiving the output from second dense layer428. In some non-limiting embodiments or aspects, third dense layer430may be configured to provide an output of third dense layer430to fourth dense layer432. In some non-limiting embodiments or aspects, third dense layer430may be configured to receive input via 16-32 nodes and third dense layer430may be configured to provide output via 8-16 nodes.

In some non-limiting embodiments or aspects, fourth dense layer432may be configured to receive the output from third dense layer430. For example, fourth dense layer432may be configured to receive the output from third dense layer430as inputs to fourth dense layer432. In some non-limiting embodiments or aspects, fourth dense layer432may be configured to determine an output. For example, fourth dense layer432may be configured to determine an output based on (e.g., in response to) receiving the output from third dense layer430. In some non-limiting embodiments or aspects, fourth dense layer432may be configured to provide second model output434as an output from fourth dense layer432. For example, fourth dense layer432may be configured to provide second model output434as an output from fourth dense layer432to first dense layer438of third machine learning model400c(seeFIG.4C). In some non-limiting embodiments or aspects, fourth dense layer432may be configured to receive input via 16-32 nodes and fourth dense layer432may be configured to provide output via 8-16 nodes.

With continued reference toFIG.4C, first dense layer438may be configured to receive first model output422(seeFIG.4A), second model output434(seeFIG.4B) and feature data436(seeFIG.4D) that is associated with one or more features (e.g., values of one or more features). For example, feature data may be associated with one or more features, the one or more features having values derived from data associated with the one or more features (e.g., account data associated with an account of a user, merchant data associated with a merchant involved in a transaction, rating data associated with a predicted rating that a future transaction involving a user and a merchant will be initiated, insurance spending data associated with annual insurance spending of the user and/or a merchant, electronics spending data associated with annual electronics spending of the user and/or a merchant, electronic transaction data associated with an amount of electronic transactions initiated by the user and/or a merchant, and/or the like). In some non-limiting embodiments or aspects, first dense layer438may be configured to receive first model output422, second model output434and feature data436as inputs to first dense layer438. In some non-limiting embodiments or aspects, first dense layer438may be configured to determine an output. For example, first dense layer438may be configured to determine an output based on (e.g., in response to) receiving first model output422, second model output434, and/or feature data436. In some non-limiting embodiments or aspects, first dense layer438may be configured to provide an output of first dense layer438to second dense layer440. For example, first dense layer438may be configured to provide an output of first dense layer438to second dense layer440. In some non-limiting embodiments or aspects, first dense layer426may be configured to receive input via one or more nodes and first dense layer426may be configured to provide output via one or more different nodes. For example, first dense layer426may be configured to receive input via one or more nodes and first dense layer426may be configured to provide output via one or more different nodes, where a first set of nodes are associated with first model output422, a second set of nodes are associated with second model output434, and a third set of nodes are associated with feature data436. In some non-limiting embodiments or aspects, the first set of nodes, second set of nodes, and third set of nodes may be independent of each other (e.g., may include nodes that are not included in the other nodes, respectively).

As shown by reference number406inFIG.4C, transaction service provider system102may generate a combined model. For example, transaction service provider system102may generate a combined model including first model output422, second model output434, feature data436, first dense layer438, and second dense layer440. In some non-limiting embodiments or aspects, second dense layer440may be configured to receive the output from first dense layer438. For example, second dense layer440may be configured to receive the output from first dense layer438as inputs to second dense layer440. In some non-limiting embodiments or aspects, second dense layer440may be configured to determine an output. For example, second dense layer440may be configured to determine an output based on (e.g., in response to) receiving the first dense layer438as input to second dense layer440. In some non-limiting embodiments or aspects, second dense layer440may be configured to provide combined model output442as an output of second dense layer440. In some non-limiting embodiments or aspects, second dense layer428may be configured to receive input via one or more nodes and second dense layer428may be configured to provide output via one node. For example, second dense layer428may be configured to receive input via one or more nodes and second dense layer428may be configured to provide output via one node, where the output provided by second dense layer440is combined model output442.

As shown by reference number408inFIG.4C, transaction service provider system102may determine a rating. For example, transaction service provider system102may determine a rating based on combined model output442. In some non-limiting embodiments or aspects, combined model output442may include a prediction (e.g., transaction data associated with a predicted transaction, transaction data associated with a plurality of payment transactions where the plurality of payment transactions includes a predicted payment transaction, and/or the like). In some non-limiting embodiments or aspects, transaction service provider system102may determine a rating for a future payment transaction based on the combined model output.