METHOD AND SYSTEM FOR TRACKING SOLICITATIONS FOR CONTRIBUTORY PAYMENTS FOR COMMERCIAL TRANSACTIONS

Method and systems for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique are provided. The method includes: receiving a notification that a transaction has been executed by a user; obtaining information that relates to the transaction; generating, based on the received information, a recommendation for soliciting contributions from potential participants with respect to the transaction; receiving information that relates to the potential participants; and receiving a confirmation of the recommendation. The generation of the recommendation may be implemented by applying a machine learning algorithm that is trained by using historical transaction data and/or data that relates to a merchant and/or a type of merchandise involved in the transaction.

BACKGROUND

1. Field of the Disclosure

This technology generally relates to methods and systems for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique.

2. Background Information

For some types of commercial transactions, there may a circumstance by which a single bill or invoice is generated but multiple parties intend to pay a portion of the total amount. For example, when a group of two or more persons enjoys a meal together at a restaurant, typically there will be a single bill, but often separate parties will contribute toward payment of the bill.

There are several conventional ways to make payments and/or contribute toward payment of a bill, such as, for example, the use of cash or the use of a payment card, such as a charge card, a credit card, or a debit card. However, many find it more convenient to use a smart phone application to make payments electronically with the need for cash or a payment card. In addition, in a circumstance in which there is a single bill with multiple parties contributing payments, there is also an issue regarding how to divide the total payment amount equitably among the parties.

Accordingly, there is a need for a method for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique.

SUMMARY

The present disclosure, through one or more of its various aspects, embodiments, and/or specific features or sub-components, provides, inter alia, various systems, servers, devices, methods, media, programs, and platforms for methods and systems for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique.

According to an aspect of the present disclosure, a method for tracking a solicitation of contributory payments from multiple parties in connection with a single transaction is provided. The method is implemented by at least one processor. The method includes: receiving, by the at least one processor, a notification that the single transaction has been executed by a user; obtaining, by the at least one processor, first information that relates to the transaction; generating, by the at least one processor based on the first information, a recommendation for soliciting contributions from a plurality of potential participants with respect to the transaction; transmitting, by the at least one processor to the user, the recommendation; receiving, by the at least one processor from the user, second information that relates to the plurality of potential participants; and receiving, by the at least one processor, a confirmation of the recommendation.

The first information may include at least one from among a total payment amount of the transaction and an identification of a merchant that relates to the transaction.

The second information may include at least one from among a number of potential participants, an identification of at least one of the potential participants, and an instruction that relates to a respective requested payment amount for each respective one of the potential participants.

The instruction that relates to the respective requested payment amount may include an indication that each respective requested payment amount is equal.

Alternatively, the instruction that relates to the respective requested payment amount may include an indication that the respective requested payment amounts are customized so as to vary among the potential participants.

The method may further include: retrieving historical transaction data that relates to the user; using the retrieved historical transaction data to train a machine learning algorithm to be used for generating the recommendation; and generating the recommendation by applying the machine learning algorithm to the received first information.

When the identification of the merchant includes a restaurant, the generating of the recommendation may further include using historical information that relates to the restaurant as an input to the machine learning algorithm.

The historical information that relates to the restaurant may include at least one from among first information that relates to coffee, second information that relates to hamburgers, third information that relates to sandwiches, fourth information that relates to chicken, fifth information that relates to pizza, sixth information that relates to Asian cuisine, seventh information that relates to beverages, eighth information that relates to Mexican cuisine, ninth information that relates to Mediterranean cuisine, tenth information that relates to American cuisine, eleventh information that relates to Brazilian cuisine, and twelfth information that relates to high-end cuisine.

The historical information that relates to the restaurant may further include at least one Gaussian curve that relates to an expected cost of an outing at the restaurant.

According to another exemplary embodiment, a computing apparatus for tracking a solicitation of contributory payments from multiple parties in connection with a single transaction is provided. The computing apparatus includes a processor; a memory; and a communication interface coupled to each of the processor and the memory. The computing apparatus may further include a display that is also coupled to the communication interface. The processor is configured to: receive, via the communication interface, a notification that the single transaction has been executed by a user; obtain first information that relates to the transaction; generate, based on the first information, a recommendation for soliciting contributions from a plurality of potential participants with respect to the transaction; transmit, to the user via the communication interface, the recommendation; receive, from the user via the communication interface, second information that relates to the plurality of potential participants; and receive, via the communication interface, a confirmation of the recommendation.

The first information may include at least one from among a total payment amount of the transaction and an identification of a merchant that relates to the transaction.

The second information may include at least one from among a number of potential participants, an identification of at least one of the potential participants, and an instruction that relates to a respective requested payment amount for each respective one of the potential participants.

The instruction that relates to the respective requested payment amount may include an indication that each respective requested payment amount is equal.

Alternatively, the instruction that relates to the respective requested payment amount may include an indication that the respective requested payment amounts are customized so as to vary among the potential participants.

The processor may be further configured to: retrieve historical transaction data that relates to the user; use the retrieved historical transaction data to train a machine learning algorithm to be used for generating the recommendation; and generate the recommendation by applying the machine learning algorithm to the received first information.

When the identification of the merchant includes a restaurant, the processor may be further configured to generate the recommendation by using historical information that relates to the restaurant as an input to the machine learning algorithm.

The historical information that relates to the restaurant may include at least one from among first information that relates to coffee, second information that relates to hamburgers, third information that relates to sandwiches, fourth information that relates to chicken, fifth information that relates to pizza, sixth information that relates to Asian cuisine, seventh information that relates to beverages, eighth information that relates to Mexican cuisine, ninth information that relates to Mediterranean cuisine, tenth information that relates to American cuisine, eleventh information that relates to Brazilian cuisine, and twelfth information that relates to high-end cuisine.

The historical information that relates to the restaurant may further include at least one Gaussian curve that relates to an expected cost of an outing at the restaurant.

According to yet another exemplary embodiment, a non-transitory computer readable storage medium storing instructions for tracking a solicitation of contributory payments from multiple parties in connection with a single transaction is provided. The storage medium includes executable code which, when executed by a processor, causes the processor to: receive a notification that the single transaction has been executed by a user; obtain first information that relates to the transaction; generate, based on the first information, a recommendation for soliciting contributions from a plurality of potential participants with respect to the transaction; transmit, to the user, the recommendation; receive, from the user, second information that relates to the plurality of potential participants; and receive a confirmation of the recommendation.

The first information may include at least one from among a total payment amount of the transaction and an identification of a merchant that relates to the transaction.

DETAILED DESCRIPTION

As described herein, various embodiments provide optimized methods and systems for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique.

Referring toFIG.2, a schematic of an exemplary network environment200for implementing a method for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique is illustrated. In an exemplary embodiment, the method is executable on any networked computer platform, such as, for example, a personal computer (PC), a tablet computer, or a smart phone.

The method for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique may be implemented by a Bill Splitter device202. The Bill Splitter device202may be the same or similar to the computer system102as described with respect toFIG.1. The Bill Splitter device202may store one or more applications that can include executable instructions that, when executed by the Bill Splitter device202, cause the Bill Splitter device202to perform actions, such as to transmit, receive, or otherwise process network messages, for example, and to perform other actions described and illustrated below with reference to the figures. The application(s) may be implemented as modules or components of other applications. Further, the application(s) can be implemented as operating system extensions, modules, plugins, or the like.

The communication network(s)210may be the same or similar to the network122as described with respect toFIG.1, although the Bill Splitter device202, the server devices204(1)-204(n), and/or the client devices208(1)-208(n) may be coupled together via other topologies. Additionally, the network environment200may include other network devices such as one or more routers and/or switches, for example, which are well known in the art and thus will not be described herein. This technology provides a number of advantages including methods, non-transitory computer readable media, and Bill Splitter devices that efficiently implement a method for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique.

The Bill Splitter device202may be a standalone device or integrated with one or more other devices or apparatuses, such as one or more of the server devices204(1)-204(n), for example. In one particular example, the Bill Splitter device202may include or be hosted by one of the server devices204(1)-204(n), and other arrangements are also possible. Moreover, one or more of the devices of the Bill Splitter device202may be in a same or a different communication network including one or more public, private, or cloud networks, for example.

The server devices204(1)-204(n) may be hardware or software or may represent a system with multiple servers in a pool, which may include internal or external networks. The server devices204(1)-204(n) hosts the databases206(1)-206(n) that are configured to store data that relates to individual payor accounts and transaction histories and information that relates to categories of commercial entities, such as food and drink establishments.

One or more of the devices depicted in the network environment200, such as the Bill Splitter device202, the server devices204(1)-204(n), or the client devices208(1)-208(n), for example, may be configured to operate as virtual instances on the same physical machine. In other words, one or more of the Bill Splitter device202, the server devices204(1)-204(n), or the client devices208(1)-208(n) may operate on the same physical device rather than as separate devices communicating through communication network(s)210. Additionally, there may be more or fewer Bill Splitter devices202, server devices204(1)-204(n), or client devices208(1)-208(n) than illustrated inFIG.2.

The Bill Splitter device202is described and illustrated inFIG.3as including a bill splitting module302, although it may include other rules, policies, modules, databases, or applications, for example. As will be described below, the bill splitting module302is configured to implement a method for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique.

An exemplary process300for implementing a mechanism for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique by utilizing the network environment ofFIG.2is illustrated as being executed inFIG.3. Specifically, a first client device208(1) and a second client device208(2) are illustrated as being in communication with Bill Splitter device202. In this regard, the first client device208(1) and the second client device208(2) may be “clients” of the Bill Splitter device202and are described herein as such. Nevertheless, it is to be known and understood that the first client device208(1) and/or the second client device208(2) need not necessarily be “clients” of the Bill Splitter device202, or any entity described in association therewith herein. Any additional or alternative relationship may exist between either or both of the first client device208(1) and the second client device208(2) and the Bill Splitter device202, or no relationship may exist.

Further, Bill Splitter device202is illustrated as being able to access an individual accounts and transaction history data repository206(1) and a commercial entity categories database206(2). The bill splitting module302may be configured to access these databases for implementing a method for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique.

Upon being started, the bill splitting module302executes a process for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique. An exemplary process for tracking a solicitation of contributory payments from multiple parties for a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique is generally indicated at flowchart400inFIG.4.

In process400ofFIG.4, at step S402, the bill splitting module302receives a notification that a commercial transaction has been executed by a user, i.e., a payor. In an exemplary embodiment, the payor may desire to obtain contributions from other potential payors. In this aspect, an opportunity for conducting such a transaction may occur when a group visits a restaurant to dine out together. The user may be an account holder with a financial institution, such as a bank, that is operating the bill splitting module302.

At step S404, the bill splitting module302obtains transaction-specific information. In an exemplary embodiment, the transaction-specific information may include a total payment amount of the transaction and an identification of a merchant that is participating in the transaction.

At step S406, the bill splitting module302generates a recommendation regarding splitting the bill among a group of potential participants, and then transmits the recommendation to the user. In an exemplary embodiment, the bill splitting module302applies a machine learning algorithm to the received information items (i.e., the transaction-specific information obtained in step S404) in order to generate the recommendation. The machine learning algorithm may be trained by using historical transaction data that pertains to the account that is associated with the user, which data may be retrieved from the individual accounts and transaction history data repository206(1).

The recommendation may also be partly based on information that is retrieved from the commercial entity categories database206(2). For example, when the merchant is a restaurant, the commercial entity categories database206(2) may include data for a food-and-drink category that includes various genres of food and drink, such as any one or more of the following: coffee; burgers; sandwiches; chicken; pizza; Asian cuisine; drinks; Mexican cuisine; Mediterranean cuisine; American cuisine; Brazilian cuisine; and high-end cuisine. In addition, the commercial entity categories database206(2) may also include a set of Gaussian curves (i.e., “bell” curves or normal distributions) that relate to average amounts that an individual diner may expect to pay for an outing at such an establishment. Thus, the machine learning algorithm may use this data as an input for generating the recommendation. As another example, the merchant may include an airline that offers travel-related services, a hotel or other establishment that offers lodging services, and/or any other suitable type of merchant for which splitting a cost of services provided to more than one customer may be contemplated.

At step S408, the bill splitting module302may receive information that relates to the potential participants. In an exemplary embodiment, the bill splitting module302may prompt the user to provide information about the potential participants, and the user may respond by entering information as a user input. The information may include, for example, a number of potential participants, an identification of one or more of the potential participants, and an instruction that relates to a respective requested payment amount for each respective potential participant. In an exemplary embodiment, the instruction for the payment amounts may indicate an equal split of the bill, or customized amounts that vary from individual to individual.

At step S410, the bill splitting module302receives a confirmation from the user with respect to the recommendation.

FIGS.5-12are screen shots500,600,700,800,900,1000,1100,1200,1300,1400,1500, and1600of a user interface for a smart phone application that executes a method for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, according to an exemplary embodiment.

When a user desires to conduct a single transaction that involves obtaining payments from multiple parties, the user may use his/her smart phone to access a smart phone application (hereinafter referred to as “app”) that executes a method for tracking a solicitation of contributory payments from multiple parties in connnection a commercial transaction, such as a restaurant bill, according to an exemplary embodiment. Referring toFIG.5, in such a scenario, the user may initially see a user interface that includes a first screen shot500, which identifies the app as a “Split the Bill” app and also provides a heading “Transaction Details”, a total amount for the proposed transaction, and an identification of a place and/or a commercial establishment that identifies the proposed recipient of the total payment for the transaction.

Referring toFIG.6, a second screen shot600includes a message that prompts the user to indicate whether he/she wishes to split the bill, as displayed on the user interface of the smart phone. Then, when the user responds affirmatively, referring toFIG.7, a third screen shot700displays, on the user interface, an account header, an account balance, and buttons that enable the user to view transactions and/or to view pending transaction splits.

Referring toFIG.8, a fourth screen shot800includes a list of transactions that is displayed on the user interface when the user clicks on the “View Transactions” button shown inFIG.7. The list of transactions includes a description of each listed transaction and, in some cases, a button labeled “Split” that invites the user to initiate a split of the corresponding transaction. Referring toFIG.9, a fifth screen shot900includes a list of split transactions that is displayed on the user interface when the user clicks on the “View Pending Splits” button shown inFIG.7. The list of split transactions includes a list of pending split transactions and a list of completed split transactions, and in both lists, each transaction is accompanied by a button labeled “View Split Details” that invites the user to view specific details that relate to the corresponding split transaction.

Referring toFIG.10, a sixth screen shot1000shows a status page that includes various details for a particular split transaction, which may be displayed on the user interface in response to the user having clicked on a “View Split Details” button as illustrated inFIG.9. The displayed information includes a number of participants (i.e., “Split: 3 People”; a total amount with gratuity (i.e., “Total Amount (w/Tip): $100)”; a description of the transaction; a date of the transaction; an amount of the user's share of the total amount; a requested amount from other participants; a received money tracker that indicates an amount that has been received from other participants thus far; and a breakdown of each participant that indicates a respective amount for each and a status re whether each amount has been received already or whether each amount has been requested but not yet received.

Referring toFIG.11, a seventh screen shot1100includes a heading labeled “Create Request Page” and includes a drop-down menu labeled “How To Split” that includes at least two clickable options, “Equal Split” and “Custom Split” by which the total amount may be either split equally among the participants or may be split according to a customized division therebetween, respectively. There is also a button labeled “Add People” that enables the user to add participants to the splitting process for executing the transaction, accompanied by a list of previously requested participants and a button for submitting the request.

Referring toFIG.12, an eighth screen shot1200is displayed on the user interface in response to the user having clicked on the “Add People” button shown inFIG.11, and includes a “Search Bar” that enables the user to select from a list of persons. A button labeled “Add Selected People” enables the user to effectuate the requested addition of transaction participants.

Referring toFIG.13, a ninth screen shot1300is a modified version of the “Create Request Page” shown inFIG.11. As shown inFIG.13, the user interface may also display information that relates to sharing rewards among the transaction participants. Referring toFIG.14, a tenth screen shot1400includes a text message that is displayed on the user interface and that indicates an amount of reward points received by the user as a result of the transaction. Referring toFIG.15, an eleventh screen shot1500invites the user to “Get your Points” and prompts the user to enter a username, a password, and a Zelle number by which rewards points may be accessed. Referring toFIG.16, a twelfth screen shot1600includes a “Rewards Page” that is displayed on the user interface and includes a balance of rewards points that are associated with the user's account.

Accordingly, with this technology, an optimized process for tracking a solicitation of contributory payments from multiple parties in connection with a commercial transaction, such as a restaurant bill, by using an application that is trained by using a machine learning technique is provided.