Patent ID: 12241752

DETAILED DESCRIPTION

Certain aspects and examples of the present disclosure relate to adjusting a zoom level of a transaction map based on a familiarity score associated with a merchant. A zoom level may be a feature of a graphical user interface (GUI) for an electronically presented map. The zoom level may allow a customer to adjust between higher levels of zoom showing geographically smaller regions of a map with higher levels of detail and lower levels of zoom showing geographically larger regions of a map with lower levels of detail. A transaction map may be an electronically presented map marking locations associated with a merchant. In the case of a transaction map, a merchant may be any entity that has a recorded purchase, debt, credit, deposit, withdrawal, tax, or claim with a customer. Any entity which executes a financial transaction with a customer, or has a pending financial transaction with a customer, may be a merchant in the case of a transaction map. A familiarity score may be at least one variable meant to indicate the customer's likelihood of being geographically familiar with a merchant displayed on a transaction map.

Adjusting the zoom level of the transaction map with the familiarity score may save the customer the time and effort that may otherwise be required to make the transaction map useful to the customer. For example, a transaction map that does not adjust the zoom level based on a familiarity score may center a transaction map on a merchant location using a default zoom level. If the merchant location is in a location the customer took a short one-time trip to, the default zoom level may be too high and display an overly detailed view with street names and markings the customer may be unfamiliar with. Alternatively, the default zoom level may be too low and show the customer an overly large, abstracted view for a merchant location the customer may be more familiar with. By adjusting the zoom level for the customer with the familiarity score, the transaction map may be more useful to the customer and the customer may be more likely to use the transaction map.

The familiarity score may be based on several factors, including location data for a customer's mobile device, a history of banking transactions with the merchant marked on the transaction map, a history of the customer's adjustments of the zoom level or a history of other variables related to the customer's engagement with the transaction map. Other factors may also be considered. The weight of factors affecting the adjustment of the zoom level may be balanced by a machine-learning algorithm which may be trained to minimize the adjustments the customer makes to the zoom level after the transaction map has been displayed to the customer.

Illustrative examples are given to introduce the reader to the general subject matter discussed herein and are not intended to limit the scope of the disclosed concepts. The following sections describe various additional features and examples with reference to the drawings in which like numerals indicate like elements, and directional descriptions are used to describe the illustrative aspects, but, like the illustrative aspects, should not be used to limit the present disclosure.

FIG.1is a block diagram of a server100with instructions106for adjusting a transaction map107based on a familiarity score112, according to some aspects of the present disclosure. The server100may be in operable communication with a database130and a mobile device140. The server100includes a processor102that is communicatively coupled to a memory104. In some examples, the processor102and the memory104may be distributed from (e.g., remote to) one another. The processor102can include one processing device or multiple processing devices. Non-limiting examples of the processor102include a Field-Programmable Gate Array (FPGA), an application-specific integrated circuit (ASIC), a microprocessor, etc. The processor102can execute instructions106stored in the memory104to perform operations. In some examples, the instructions106can include processor-specific instructions generated by a compiler or an interpreter from code written in a suitable computer-programming language, such as C, C++, C #, etc.

The memory104can include one memory or multiple memories. The memory104can be non-volatile and may include any type of memory that retains stored information when powered off. Non-limiting examples of the memory104include electrically erasable and programmable read-only memory (EEPROM), flash memory, or any other type of non-volatile memory. At least some of the memory104can include a non-transitory, computer-readable medium form which the processor102can read instructions106. A computer-readable medium can include electronic, optical, magnetic, or other storage devices capable of providing the processor102with computer-readable instructions or other program code. Non-limiting examples of a computer-readable medium include magnetic disk(s), memory chip(s), ROM, random-access memory (RAM), an ASIC, a configured processor, optical storage, or any other medium from which the processor102can read the instructions106.

The memory104can also contain a transaction map107with a zoom level108of a graphical user interface (GUI) for the transaction map107. The memory104can also contain a merchant location110, a familiarity score112, a transaction history113, a machine-learning algorithm115, a map use history116, a merchant type118, a residence history120, customer preferences122, and location data114.

The zoom level108may be associated with a particular merchant location110, based on the familiarity score112. The merchant location110may be a location associated with any entity that has a recorded purchase, debt, credit, deposit, withdrawal, tax, or claim with a customer. Any entity which executes a financial transaction with a customer, or has a pending financial transaction with a customer, could be a merchant in the case of a transaction map107.

The familiarity score112may indicate the likelihood of a customer being geographically familiar with the merchant location110. The familiarity score112may be based on a transaction history113associated with the merchant location110. The transaction history113may be transmitted to the server100from the database130. The transaction history113may include a time-ordered history of financial transactions executed by the customer at the merchant location110.

The map use history116may be transmitted to the server100from the mobile device140. The machine-learning algorithm115may adjust the familiarity score112based on the map use history116, which may include data related to a customer's engagement with the transaction map107or a history of the customer's adjustments to the zoom level108. The machine-learning algorithm115may be trained to adjust the zoom level108to maximize variables related to engagement, such as how frequently the customer uses the transaction map107. The machine-learning algorithm115may be trained to adjust the zoom level108to minimize a number of adjustments a customer may make to the zoom level108after accessing the transaction map107. In addition to being trained by the customer's own engagement and zoom level108adjustment data, the machine-learning algorithm115may be trained with metadata, such as engagement data, zoom level108adjustment data, and customer tailored zoom levels from other customers. The metadata used to train the machine-learning algorithm115could be from all other customers or could be from customers of a similar demographic. For example, the machine-learning algorithm115could give more weight to engagement and zoom level108data of customers with commonalities in residence history120or commonalities in location data114. Training the machine-learning algorithm115to adjust the zoom level108with data from the customer or metadata of other customers can result in the mobile device140presenting a transaction map107with a zoom level108more likely to be useful to the customer.

Location data114may be transmitted from the mobile device140and may affect the zoom level108. The customer preferences122can also affect the zoom level108of the transaction map107. For example, a preference to display discounts may bring a map marker for a discount into the boundary of the transaction map107, which may affect the zoom level108.

The residence history120can be a history of addresses associated with the customer. The residence history120can include addresses from billing statements, addresses from loan applications, or addresses from background checks. The residence history120may be transmitted to the server100from the database130.

The merchant type118may determine whether a real marker is displayed on the transaction map107or a representative marker, which may not display a true geographic location of a merchant. A representative marker may be chosen for transactions related to utilities, loan payments, credit card payments, checking payments, and electronic commerce. For example, a payment to a power utility may place a representative marker at a customer's home, rather than the location of a power plant or a utility company's headquarters. In some examples, such as transactions related to peer-to-peer mobile payment services like Venmo®, CashApp®, and Google Pay®, a representative marker may be used instead of a real marker to protect the privacy of individuals associated with the transaction.

FIG.2is a block diagram of a mobile device140with instructions206for receiving a transaction map207based on a familiarity score212, according to some aspects of the present disclosure. The mobile device140may be in operable communication with the server100ofFIG.1. The mobile device140includes a processor202that is communicatively coupled to a memory204. In some examples, the processor202and the memory204may be distributed from (e.g., remote to) one another. The processor202can include one processing device or multiple processing devices. Non-limiting examples of the processor202include a Field-Programmable Gate Array (FPGA), an application-specific integrated circuit (ASIC), a microprocessor, etc. The processor202can execute instructions206stored in the memory204to perform operations, in some examples, the instructions206can include processor-specific instructions generated by a compiler or an interpreter from code written in a suitable computer-programming language, such as C, C++, C#, etc.

The memory204can include one memory or multiple memories. The memory204can be non-volatile and may include any type of memory that retains stored information when powered off. Non-limiting examples of the memory204include electrically erasable and programmable read-only memory (EEPROM), flash memory, or any other type of non-volatile memory. At least some of the memory204can include a non-transitory, computer-readable medium from which the processor202can read instructions206. A computer-readable medium can include electronic, optical, magnetic, or other storage devices capable of providing the processor202with a computer-readable instructions or other program code. Non-limiting examples of a computer-readable medium include magnetic disk(s), memory chip(s), ROM, random-access memory (RAM), an ASIC, a configured processor, optical storage, or any other medium from which a computer processor can read the instructions206.

The memory204may also include a transaction map207with an adjusted zoom level208of the graphical user interface (GUI) for the transaction map207. The memory204can also contain a merchant location210, an adjusted familiarity score212, a map use history216, a merchant type218, customer preferences222, and location data214.

The instructions206may cause the processor202to receive the adjusted familiarity score212, associated with a merchant location210, from the server100. The familiarity score212may be based on a transaction history113transmitted from the server100. Alternatively, the instructions206may cause the processor202to receive the transaction history113from the server100and calibrate the adjusted familiarity score212based on the received transaction history113. The adjusted familiarity score212may indicate the likelihood of the customer being geographically familiar with the merchant location210. The merchant location210may be a location associated with any entity that has a recorded purchase, debt, credit, deposit, withdrawal, tax, or claim with a customer. Any entity which executes a financial transaction with the customer, or has a pending financial transaction with the customer, could be a merchant in the case of the transaction map207.

The instructions206may cause the processor202to receive the adjusted familiarity score212associated with the merchant location210, from a server100. The adjusted familiarity score212may be based on location data214related to the mobile device140. The location data214may be transmitted to the server100for consideration in arriving at the adjusted familiarity score212. Alternatively, the instructions206may cause the processor202to use the location data214to calibrate the adjusted familiarity score212based on the location data214.

The instructions206may also cause the processor202to receive an adjusted zoom level208for a graphical user interface (GUI) of the transaction map207, which may be displayed on the mobile device140. Alternatively, the instructions206may cause the processor202to adjust the zoom level208based on the received, adjusted familiarity score212. The adjusted zoom level208may be based on the adjusted familiarity score212. The instructions206may also cause the processor202to transmit map use history216to the server100. The map use history216may include data related to a customer's engagement with the transaction map207or a history of the customer's adjustments to the adjusted zoom level208.

The instructions206may cause the processor202to display the merchant location210on the transaction map207with either a real marker, displaying the true geographic location of the merchant location210, or a representative marker, which may not display the true geographic location of the merchant location210. Data related to real marker and representative marker selection may be determined by a merchant type218, which may be transmitted to the mobile device140by the server100. The merchant type218may be based on whether displaying the true geographic location or a representative location may be more useful to the customer. For example, the merchant type218may be for a real location if the merchant location210is a grocery store. Alternatively, the merchant type218may be for a representative location if the merchant location210is associated with electronic commerce, such as an Amazon® purchase.

The instructions206may cause the processor202to transmit customer preferences222to the server100. The customer preferences222may be selected by the customer to cause the server100to consider factors other than a familiarity score212for determining the adjusted zoom level208that may be ultimately received by the mobile device140. For example, the customer preferences222may indicate a preference to display discounts, which may adjust the boundary of the transaction map207to include map markers for discounts.

FIG.3is a flowchart300of an example of a process for adjusting a transaction map107based on a familiarity score112, according to some aspects of the present disclosure. Some examples may include more steps, fewer steps, different steps, or a different combination of steps than is shown inFIG.3. The steps ofFIG.3are described below with reference to the components ofFIG.1described above.

In block302, the server100may adjust the familiarity score112associated with the merchant location110based on the transaction history113associated with the merchant location110. The familiarity score112may indicate how likely a customer is to be geographically familiar with the merchant location110. The transaction history113, which may include a time-ordered history of financial transactions executed by the customer at the merchant location110, may also affect the familiarity score112. The magnitude of the adjustment of the familiarity score112may be mediated by the machine-learning algorithm115, which may be trained to raise or maintain a customer engagement score as part of a transaction map use history116.

In block304, the server100may adjust the familiarity score112associated with the merchant location110based on location data114from a mobile device of the customer. The location data114may include a time-ordered, duration-specific history of the customer relative to the merchant location110. The magnitude of the adjustment to the zoom level108may also be mediated by the machine-learning algorithm115to raise or maintain the customer engagement score.

In some examples, the server100may use the location data114to adjust the familiarity score112based on a transaction history with an alternate merchant location of the same franchise as the as the primary merchant location110. For example, a customer may frequent a coffeehouse close to their home address, but on one occasion buy coffee at a coffeehouse of the same franchise at a different location, 30 miles away from their home. When the customer views the transaction map107associated with the transaction at this different location, the zoom level108may be higher, and the transaction map107have more detail of a smaller geographical area, than if the customer was viewing a transaction map107for a transaction with a merchant location and franchise they rarely frequent that is also 30 miles away from their home. In some such examples, the machine-learning algorithm115may be trained to adjust the familiarity score112to raise or maintain the customer's engagement score.

In some examples, the server100may use the location data114to adjust the familiarity score112if the merchant location110is a proximal merchant location with an existing familiarity score112. For example, if a customer frequents a coffeehouse with a high familiarity score112, and the customer buys a wrench at a hardware store next door to the coffeehouse, but the customer has never shopped at this hardware store before, when the customer views the transaction map107associated with the transaction at the hardware store, the zoom level108may be similar to the zoom level for the coffeehouse with the high familiarity score112. In some such examples, the machine-learning algorithm115may be trained to adjust the familiarity score112to raise or maintain the customer's engagement score.

In some examples, the server100may use metadata related to the relationships between other customer's familiarity scores and customer tailored zoom levels to determine the zoom level108for the transaction map107. For example, a customer may have a familiarity score112of67for a particular merchant location110. Other customers with similar familiarity scores for the same merchant location110may prefer a zoom level108that results in a boundary of the transaction map107to be 15 miles across. As a result, the server100may adjust the zoom level108to be closer to the 15 miles across figure. In some such examples, the machine-learning algorithm115may be trained to adjust the familiarity score112to raise or maintain the customer's engagement score.

In block306, the server100may select a zoom level108, based on the familiarity score112, for the transaction map107containing the merchant location110. The transaction map107may be a navigational map displaying at least one merchant location110as it relates to at least one financial transaction relevant to the customer or one pending financial transaction relevant to the customer.

FIG.4is an example of a first transaction map408displayed on a first web browser window402, in which a zoom level is based on a familiarity score associated with a first merchant location407, according to some aspects of the present disclosure. The first web browser window402displays a first web address404, a first transaction statement406, and a first transaction map408. The first transaction map408has map zoom adjustment controls illustrated as a plus (+), which may increase the zoom level, and a minus (−), which may decrease the zoom level. Increasing the zoom level may adjust the map to display a geographically smaller region with a higher level of detail. Decreasing the zoom level may adjust the map to display a geographically larger region with a lower level of detail.

The first transaction map408marks a first merchant location407and marks a customer location409. The first merchant location407may be associated with a transaction selected from the first transaction statement406. In the first transaction statement406, the transaction “Yee Haw Brewing—$42.41” may be illustrated in bold on the first transaction statement406to indicate selection by the customer. The selection may occur by touchscreen input, keyboard input, mouse click, scrolling, voice command, or any other suitable graphical user interface (GUI) input.

FIG.5is an example of a second transaction map516displayed on a second web browser window510, in which a zoom level is based on a familiarity score associated with a second merchant location517, according to some aspects of the present disclosure. The second transaction map516marks a second merchant location517and the location of a discount518. The second merchant location517may be associated with a transaction selected from the second transaction statement514. In the second transaction statement514, the transaction “Walgreens P. Hvn—$13.19” may be illustrated in bold on the second transaction statement514to indicate selection by the customer. The selection may occur by touchscreen input, keyboard input, mouse click, scrolling, voice command, or any other suitable graphical user interface (GUI) input.

The zoom level illustrated in the second transaction map516ofFIG.5is higher than the zoom level illustrated in the first transaction map408ofFIG.4. As a result, the second transaction map516illustrates a higher level of detail for a smaller geographical area than the first transaction map408. The difference in zoom levels between the second transaction map516and the first transaction map408can be because the familiarity score of the merchant associated with the first merchant location407ofFIG.4can be lower than the familiarity score of the merchant associated with the second merchant location517ofFIG.5. The first transaction map408also differs from the second transaction map416by displaying a customer location409. The customer location409may be displayed or omitted from a transaction map based on the familiarity score associated with a merchant location.

Other map markings, such as the discount518ofFIG.5, may be displayed or omitted from a transaction map based on the zoom level, the familiarity score of the merchant associated with the discount518, and customer preferences. In some examples, customer preferences may adjust the zoom level to expand or reduce the geographic area covered by the second transaction map516to display the discount518that may not be visible if the zoom level of the second transaction map516were to be adjusted only by a familiarity score.

In one example, a server may track a transaction history of a customer who frequently shops at a particular grocery store as a merchant location. The server may associate the grocery store with location data from a customer's mobile device. The server may have recorded, in a map use history, at least one instance of the customer opening a transaction map marking the grocery store. The customer may have opened the map marking the grocery store by selecting a transaction related to the grocery store from their transaction history. The customer may not have adjusted the zoom level upon opening the transaction map marking the grocery store. As a result, the server may have recorded that the zoom level for the transaction map marking the grocery store was set to a satisfactory zoom level. The customer may have started a membership at a gym adjacent to the grocery store. The customer may select the entry for the gym from their transaction history, which may open a transaction map marking the gym. The transaction map marking the gym may open with a zoom level similar to the zoom level for the transaction map marking the grocery store. The zoom level for the transaction map marking the gym may be slightly different than the zoom level for the transaction map marking the grocery store based on inferences from a machine learning algorithm which may have concluded, based on metadata related to other customers, that the boundary of a transaction map should be extended to display a proximal merchant location with a high familiarity score when a transaction map marks a merchant location with a low familiarity.

The foregoing description of certain examples, including illustrated examples, has been presented only for the purpose of illustration and description and is not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Numerous modifications, adaptations, and uses thereof will be apparent to those skilled in the art without departing from the scope of the disclosure.