PERSONALIZED POINT-OF-SALE GRAPHICAL USER INTERFACE

According to one embodiment, a method, computer system, and computer program product for a personalized, point-of-sale graphical user interface is provided. The embodiment may include detecting an object for purchase that requires additional user input on a point-of-sale (POS) device. The embodiment may also include generating a custom user interface display on the POS device based, at least in part, on the detected object and historical purchase information associated with a user purchasing the detected object.

BACKGROUND

The present invention relates generally to the field of computing, and more particularly to point-of-sale systems.

Point-of-sale relates to relates to the time and place at which a transaction, typically a retail transaction, is completed. At the point-of-sale, an amount owed by a purchasing party to a selling party is calculated and payment is tendered and exchanged from the purchasing party to the selling party thus completing the transaction. Traditionally, point-of-sale systems, or devices utilized to assist in executing transactions at the point-of-sale, were operated by representatives of the selling party, such as cashiers for a retail establishment. Many point-of-sale systems were highly specialized computing devices that needed adequate training to properly operate. However, due to more customer-friendly innovations, many point-of-sale systems now allow a purchasing party to operate the point-of-sale system (e.g., a self-checkout) while a representative for the selling party may be nearby to offer assistance when needed.

SUMMARY

According to one embodiment, a method, computer system, and computer program product for a personalized, point-of-sale graphical user interface is provided. The embodiment may include detecting an object for purchase that requires additional user input on a point-of-sale (POS) device. The embodiment may also include generating a custom user interface display on the POS device based, at least in part, on the detected object and historical purchase information associated with a user purchasing the detected object.

DETAILED DESCRIPTION

Embodiments of the present invention relate to the field of computing, and more particularly to point-of-sale systems. The following described exemplary embodiments provide a system, method, and program product to, among other things, personalize a point-of-sale system using recurrent user trends mined from historical, user-specific data. Therefore, the present embodiment has the capacity to improve the technical field of point-of-sale systems by improving a point-of-sale graphical user interface through personalization that increases efficiencies throughout user utilization of a point-of-sale device and aides in the identification of items detrimental to or avoided by the user. Furthermore, a reduction in consumed resources may be also be observed through a reduction in the time per point-of-sale transaction.

As previously described, point-of-sale relates to relates to the time and place at which a transaction, typically a retail transaction, is completed. At the point-of-sale, an amount owed by a purchasing party to a selling party is calculated and payment is tendered and exchanged from the purchasing party to the selling party thus completing the transaction. Traditionally, point-of-sale systems, or devices utilized to assist in executing transactions at the point-of-sale, were operated by representatives of the selling party, such as cashiers for a retail establishment. Many point-of-sale systems were highly specialized computing devices that needed adequate training to properly operate. However, due to more customer-friendly innovations, many point-of-sale systems now allow a purchasing party to operate the point-of-sale system (e.g., a self-checkout) while a representative for the selling party may be nearby to offer assistance when needed.

During the point-of-sale process at a user-operated point-of-sale device, a user often needs to report on items not associated with a barcode, such as produce. At times, these items can number in the dozens, which may require weighing or counting of each individual item thus lengthening the self-checkout process. Furthermore, user errors or frustrations with a lack of ease of use with a point-of-sale system may result in inventory shrinkage. Inventory shrinkage relates to any loss of an item by retails due to factors other than sales. For example, when users navigate a self-checkout system, several menus and/or inputs must be entered to properly input some items. When a user becomes frustrated that inputting a specific item is taking too long or results in multiple errors, the user may input the item as a different item in order to continue with the checkout process thus resulting in incorrect inventory numbers and potential revenue loss by the seller. As such, it may be advantageous to, among other things, rather than present a user with dozens of items for user entry, identify specific items a user typically purchases using historical user data and generating and presenting a graphical user interface depicting a list of the identified items to the user.

According to one embodiment, a personalized, point-of-sale program may capture various data items of user preferences and purchase history in order to generate a knowledge corpus of common user purchases and a frequency at which specific items are purchased. Once the personalized, point-of-sale program has satisfied a confidence threshold for various categories, the personalized, point-of-sale program may deploy the knowledge corpus for a specific user. Once deployed, the personalized, point-of-sale program may identify a user is operating a point-of-sale device, detect an item is presented by the user to the point-of-sale device, and generate a custom user interface display to the user based on the knowledge corpus.

According to at least one embodiment, the personalized, point-of-sale program150may receive historical, user-specific data that may include prior user purchases and item uses. Using the historical, user-specific data, the personalized, point-of-sale program150may generate a knowledge corpus that show user habits and routines and may be used to make predictions of future user routines. Upon generating and deploying the knowledge corpus, the personalized, point-of-sale program150, at a point-of-sale system, may detect an item presented to the point-of-sale system requiring manual user input. The personalized, point-of-sale program150may utilize the knowledge corpus to generate a graphical user interface customized depicting predicted items matching the presented item. Furthermore, notwithstanding depiction in computer101, the personalized, point-of-sale program150may be stored in and/or executed by, individually or in any combination, end user device103, remote server104, public cloud105, and private cloud106. The personalized, point-of-sale method is explained in more detail below with respect toFIGS.2and3.

Referring now toFIG.2, an operational flowchart for a personalized, point-of-sale training process200is depicted, according to at least one embodiment. At202, the personalized, point-of-sale program150captures historical, user-specific data. As a user conducts transactions, a seller may collect various transaction record details, such as electronic transaction records, voice recognition of the purchase, receipt scans, or loyalty card scans. The personalized, point-of-sale program150may capture information within a seller repository relating to a specific user, once the user opts-in as described below, in order to build a knowledge corpus of user-specific information. In another embodiment, the personalized, point-of-sale program150may capture seller information such as inventory information and optional user customizations to each item. For example, if the seller is a restaurant, the personalized, point-of-sale program150may capture a menu and available order options on the menu for each item. In one or more embodiments, the seller repository may include any repository accessible to the personalized, point-of-sale program150, such as storage124or remote database130.

In one or more embodiments, prior to beginning data collection, the personalized, point-of-sale program150may perform an opt-in procedure for each user wishing to use the personalized, point-of-sale program150. The opt-in procedure may present a prompt to the user indicating that the personalized, point-of-sale program150may gather data, the types of data the personalized, point-of-sale program150may gather, and the uses for which the personalized, point-of-sale program150may use the gathered data. For example, the personalized, point-of-sale program150may present a prompt to the user indicating that the personalized, point-of-sale program150may gather information about consumer habits in order to make a prediction as to an item presented to a point-of-sale system for manual entry.

In one or more other embodiments, the personalized, point-of-sale program150may allow a user to manually configure certain user restrictions, such as brand preferences, item preferences, food allergies, or dietary restrictions. For example, upon initial configuration, the personalized, point-of-sale program150may prompt the user with a graphical user interface requesting any food allergies and/or dietary restrictions applicable to the user, such as, but not limited to, nut allergies, gluten intolerance, vegetarianism, veganism, lactose intolerance, etc. The personalized, point-of-sale program150may include a list of choices from which the user may interact with a display screen, such as a touchscreen, to make one or more selections.

In one or more further embodiments, the personalized, point-of-sale program150may gather user-specific information from multiple sellers in order to build a more robust knowledge corpus. For example, if a user frequents two separate sellers when purchasing various items, the personalized, point-of-sale program150may, when the information is accessible, gather user-specific information from each seller, perhaps through an application programming interface (API) thus allowing the personalized, point-of-sale program150to have a more complete picture of the user's purchase habits.

Next, at204, the personalized, point-of-sale program150generates a knowledge corpus based on the captured user-specific data. As previously described, the knowledge corpus may be a store of user-specific information relating to user purchase habits. From the captured user-specific information, the personalized, point-of-sale program150may categorize user purchases by type such as, but not limited to, groceries, restaurant, and entertainment. Additionally, the personalized, point-of-sale program150may parse purchase details to determine purchase and preference aspects. For example, the personalized, point-of-sale program150may note that a user prefers hamburgers and, when a hamburger is ordered, the user requests no ketchup. Furthermore, the personalized, point-of-sale program150may gather the scope of options available in a given environment to thoroughly understand the relative preference of an ordered/purchased item against all available options. For example, the personalized, point-of-sale program150may understand the options for hamburger toppings at a given restaurant are ketchup, mayonnaise, cheese, mustard, lettuce, tomato, bacon, and barbeque sauce but, when ordering a hamburger, the user typically only orders a hamburger with mustard. Since relative preference may be temporally variable, the personalized, point-of-sale program150may allow the knowledge corpus to be modified and/or expanded to include additional indicators including, but not limited to, time of day, weather, and recent activity, to reflect these preferences. For example, a specific seller may have different purchase options available during the fall months than in the spring months due to item availability from wholesalers and purchaser preferences during those times of the year.

Then, at206, the personalized, point-of-sale program150trains the knowledge corpus. The personalized, point-of-sale program150may continually train the knowledge corpus, such as through a neural network, in order to improve predictability and understanding of user preferences. For example, the personalized, point-of-sale program150may continually monitor, or after the expiration of preconfigured periods of time, the personalized, point-of-sale program150may recapture the user-specific information and reconfigure, or train, the knowledge corpus. The personalized, point-of-sale program150may perform the training through a feedback loop of recapturing historical, user-specific data and, in one or more embodiments, purge data older than a preconfigured age.

Next, at208, the personalized, point-of-sale program150determines whether the knowledge corpus has reached a threshold confidence level. The personalized, point-of-sale program150may continually train the knowledge corpus until a threshold confidence level in various environments has been reached in order to provide suggestions given the available options. In each environment, any limitations (e.g., allergy, physical, etc.) may provide the initial restriction of the menu options and the personalized, point-of-sale program150may continually train on newly captured user-specific information until enough data is obtained for the personalized, point-of-sale program150to make a prediction above a threshold confidence level.

If the personalized, point-of-sale program150determines the knowledge corpus has reached a threshold confidence level (step208, “Yes” branch), then the personalized, point-of-sale training process200may proceed to step210to deploy the knowledge corpus to a point-of-sale device. If the privacy-enhanced, biometrics-based authentication program150determines the knowledge corpus has not reached a threshold confidence level (step208, “No” branch), then the personalized, point-of-sale training process200may return to step202to capture historical, user-specific data.

Then, at210, the personalized, point-of-sale program150deploys the knowledge corpus to a point-of-sale device. The personalized, point-of-sale program150may allow for deployment to any number of point-of-sale devices. For example, the personalized, point-of-sale program150may be deployed to a point-of-sale self-checkout system in a grocery store or an ordering kiosk at a restaurant. Deployment of the knowledge corpus may include installation of the personalized, point-of-sale program150to each point-of-sale device. In one or more embodiments, deploying the knowledge corpus may include downloading the knowledge corpus to a storage unit of each point-of-sale device or connecting each point-of-sale device to the knowledge corpus through a network, such as WAN102.

Referring now toFIG.3, an operational flowchart for a personalized, point-of-sale operation process300is depicted, according to at least one embodiment. At302, the personalized, point-of-sale program150detects an item placed on a point-of-sale device. The personalized, point-of-sale program150may detect the presence of an item placed on the point-of-sale device by a user through one or more detection methods including, but not limited to, detecting a weight variance through a scale on the point-of-sale device and image recognition of an area surrounding the point-of-sale device.

In one or more embodiments, rather than detecting the presence of an item presented by the user at the point-of-sale system, the personalized, point-of-sale program150may detect a user intention to manually interact with the point-of-sale system. For example, if the point-of-sale system is an ordering kiosk at a restaurant, the personalized, point-of-sale program150may identify the user's eye gaze at the display screen of the point-of-sale system as a detection of the user's desire to interact with the point-of-sale system.

Then, at304, the personalized, point-of-sale program150generates a custom user interface display on the point-of-sale device. Once the personalized, point-of-sale program150detects the presence of an item, the personalized, point-of-sale program150may generate a custom user interface display that displays one or more items based on the knowledge base. For example, once the personalized, point-of-sale program150determines an item has been placed on a scale of the point-of-sale system, the personalized, point-of-sale program150may determine from the knowledge base that the user typically purchases bananas, apples, and oranges on a weekly basis and, due to the frequency of purchase, the personalized, point-of-sale program150may generate a graphical user interface that places bananas, apples, and oranges as the top choices for the user to select.

In one or more embodiments, the personalized, point-of-sale program150may generate a graphical user interface with several different user-specific categories based on the knowledge base. For example, the personalized, point-of-sale program150may include a category of most purchased items by the user, a category of predicted items based on user purchase frequency, a category of predicted items based on weight, and a category of predicted items based on appearance.

The category of most purchased items by the user may be based on identification of items most often purchased by the user at the specific seller according to information within the knowledge base. For example, if the user typically purchases apples from one specific seller and oranges from another seller, the personalized, point-of-sale program150may generate a category subheading or section on the graphical user interface that includes only apples and omits oranges since the personalized, point-of-sale program150may determine the user is presently at the specific seller and the personalized, point-of-sale program150has determined, based on the knowledge base, that the user typically only purchases apples at this seller.

The category of predicted items based on user purchase frequency may be a category of predictions by the personalized, point-of-sale program150based on the purchase frequency data within and derived from the knowledge base. For example, the personalized, point-of-sale program150may determine that a user purchases lettuce once a week and typically on a Sunday, which may indicate the user consumes lettuce with lunches on weekdays. Therefore, if the user is utilizing the point-of-sale device on a Sunday, the personalized, point-of-sale program150may predict that a category for predicted items based on user purchase frequency displayed on a point-of-sale display screen should include lettuce when the user presents an item for manual entry.

The category of predicted items based on weight may be a prediction by the personalized, point-of-sale program150of items typically purchased by the user based on specific weight of the item presented on a scale of the point-of-sale device. For example, if the user typically purchases four pounds of apples at a time, the personalized, point-of-sale program150may include apples in a category of predicted items based on weight on a display screen of the point-of-sale device when the scale registers the weight of an item for manual entry at 4.15 pounds.

The category of predicted items based on appearance may be a predictions of items presented by the user for purchase at a point-of-sale device based on characteristics of the item as detected by an image capture device. For example, the personalized, point-of-sale program150may determine that a user frequently purchases bananas but never purchases lemons based on the knowledge base. Therefore, if an image capture device associated with the point-of-sale device identifies the item presented for manual entry at the point-of-sale device is yellow in color, the personalized, point-of-sale program150may present bananas as an option for user selection from a category of predicted items based on appearance on the graphical user interface but omit lemons from the same category. In one or more other embodiments, the personalized, point-of-sale program150may utilize various other item characteristics identifiable by an image capture device include, but not limited to, smoothness, hardness, item count, and item shine.

In another embodiment, the personalized, point-of-sale program150may generate the graphical user interface based on user preferences within the knowledge corpus. As previously described, the personalized, point-of-sale program150may allow a user to manually enter brand preferences, item preferences, dietary restrictions, and food allergies. For example, the personalized, point-of-sale program150may provide a point-of-sale device at a grocery store checkout with preferred or recent purchases of a user and, on a top row of a graphical user interface, display specific varieties of produce (e.g., gala apple or yellow onion) when an item for manual entry is placed on a scale of the point-of-sale device. Furthermore, the personalized, point-of-sale program150may also automatically detect such preferences based on historical user purchase information within the knowledge corpus. For example, the personalized, point-of-sale program150may determine that a user frequently purchases items marked as gluten-free and located in a specialty section of a grocery store. Therefore, the personalized, point-of-sale program150may determine the user prefers items marked as gluten-free.

It may be appreciated thatFIGS.2and3provide only an illustration of one implementation and do not imply any limitations with regard to how different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements. For example, in a restaurant setting where a user interacts with an ordering kiosk, the personalized, point-of-sale program150may allow a suggested order to be defined or user-specific defaults to be added as the user goes through screen prompts to complete the ordering process. For instance, if a user orders a quarter pound hamburger with cheese, the personalized, point-of-sale program150may, through the knowledge base, determine the user never orders ketchup despite ketchup being a default condiment for the restaurant. As such, the personalized, point-of-sale program150may automatically deselect ketchup or mark a “no ketchup” option on the graphical user interface without requiring the user's manual selection of such an option.

In one or more additional embodiments, the personalized, point-of-sale program150may track other users frequenting establishments with the user and user preferences associated with the other users. Therefore, the personalized, point-of-sale program150may determine specific users with whom a user typically frequents an establishment. For example, if a user frequently visits a restaurant with another individual, the personalized, point-of-sale program150may identify this attendance and, when a user visits that establishment, assume the other user is in attendance and, when an item associated with the other user is ordered on an ordering kiosk, customize the order according to the other user's preferences as stored in the knowledge base.

In one or more embodiments, the personalized, point-of-sale program150may present a notification to the user through the point-of-sale system display screen that an item ordered or presented for purchase may be or includes an ingredient subject to an allergy or food restriction for the user. For example, if the personalized, point-of-sale program150understands the user has a nut allergy and the user has presented an item with nuts as an ingredient, the personalized, point-of-sale program150may present a notification to the user that the item contains an ingredient of which the user is allergic.