Concurrent browsing interface with user attribute-based recommendations

Abstract

This application relates to a concurrent browsing interface with recommendations based on user attributes. The provided method includes: providing a user interface displayed on a first user's first computing device, the user interface being configured to simultaneously display a first browsing screen showing the first user interacting with a site and a real-time feed of a second browsing screen presented on a second user's second computing device showing the second user interacting with the site; adding the first item to a first digital shopping cart associated with the first user in response to input instructing the first user to select a first item from the real-time feed of the second browsing screen; receiving a request to split payment for the first item in the first digital shopping cart; providing a first payment interface displayed on the first computing device for paying a first portion of the amount; and providing a second payment interface displayed on the second computing device for paying a second portion of the amount.

Description

[0001] This application is a divisional application of patent application filed on August 20, 2021, with application number 202110961805.2 and invention title "Concurrent Browsing Interface with User Attribute-Based Recommendations". Technical Field

[0002] This application specifically relates to concurrent browsing interfaces with recommendations based on user attributes. Background Technology

[0003] Traditional online marketplaces offer a personalized shopping experience by providing an interface that allows users to browse inventory with recommendations for products they wish to purchase. Recommendations are typically based on information about the user, such as their browsing activity or purchase history. However, this provides users with a singular shopping experience that doesn't allow other users to participate in real-time. Whenever a user attempts to solicit feedback from other users, they are forced to browse items (or products), identify items of interest, determine how to describe the items (e.g., via hyperlinks, screenshots, or text descriptions), and then communicate that description to others. This roundabout way of sharing items of interest with other users increases the time users spend browsing items, increases network traffic, depletes virtual resources, and reduces available internet and telephone bandwidth. Summary of the Invention

[0004] At a higher level, the aspects described in this paper involve providing a concurrent browsing interface with recommendations, annotations, and ratings for multiple users. By providing a concurrent browsing interface, each user's browsing activity is simultaneously visible to all other participating users. Similarly, the techniques described in this paper also provide recommendations to users based on the attributes of all participating users.

[0005] When a user requests a concurrent browsing session, the system receives real-time feeds of the user's browsing screen to generate a concurrent browsing interface. This interface simultaneously displays each user's browsing screen and activity to all other participating users. User attributes for each user are retrieved. These attributes are used to generate ratings for the items browsed by each participating user. Ratings can also be manually entered by the participating users. The ratings for browsed items are integrated into the concurrent browsing interface, allowing each participating user to view their ratings on the screen. Ratings are displayed near their corresponding items.

[0006] As users provide user input, the user input is processed. Comments provided as user input are merged into the same area of ​​the concurrent browsing interface along with the items they relate to. Comments are integrated into the browsing screen where they reside. Each participating user can view comments submitted by any other participating user.

[0007] In response to navigation to payment-related pages, payment information is received from participating users. This payment information is consolidated into a concurrent browsing interface to provide a shared payment view. Each participating user can view the digital shopping cart of every other participating user, including the items selected for purchase. However, sensitive financial information may be obscured or omitted for users whose information is not relevant.

[0008] Recommendations are generated based on participating users' ratings, comments, payment information, and user attributes. Using these factors, the recommendation engine generates suggestions, including but not limited to which items to purchase, which users should purchase each item, and how many items users should purchase. These recommendations are integrated into the concurrent browsing interface, positioned relatively close to the relevant items they apply to. As additional user input is received, updated concurrent browsing interfaces are generated using recommendations, comments, ratings, and payment information.

[0009] This synopsis aims to present, in a simplified form, a selection of concepts further described in the Detailed Description section of this disclosure. This synopsis is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter. Other objects, advantages, and novel features of the art will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of this disclosure or through practice of the art. Attached Figure Description

[0010] The present technology will now be described in detail with reference to the accompanying drawings, in which:

[0011] Figure 1 This is an example concurrent browsing system that can adopt the embodiments of this disclosure;

[0012] Figure 2 This is an example illustration of a graphical user interface (“GUI”) with concurrent browsing interfaces involving three users, based on the aspects described herein;

[0013] Figure 3 This is an example illustration of a concurrent browsing interface with annotations and recommendations based on the aspects described in this article;

[0014] Figure 4 This is a block diagram illustrating an example method for presenting a concurrent browsing interface with generated recommendations, based on the aspects described herein; and

[0015] Figure 5 This is a block diagram of an example computing environment applicable to implementing the aspects described in this paper. Detailed Implementation

[0016] As mentioned above, traditional online marketplaces offer interfaces tailored to individual users. While online shopping is collaborative in nature, traditional online marketplaces continue to offer interfaces tailored to individual users. This interface provides a single browsing session visible only to the user's browsing activity. Current online marketplaces also offer sponsored or recommended purchases based on individual user attributes. In this context, "user attributes" can be any information representing the user. As users increasingly seek feedback from others, they must identify any items of interest and devise alternative ways to communicate these items to other users (e.g., via hyperlinks, screenshots, text descriptions). As the scale of online marketplace inventory grows, the number of items users may want to discuss and share with other users also increases. Therefore, it becomes increasingly important to provide an interface that allows users to collaborate with other users, receive feedback from these users about browsed items, and generate recommendations that cater to the entire user group rather than individual users.

[0017] However, the problem lies in the fact that current online marketplaces use recommender systems that generate recommendations based on user attributes from individual users, even if the attributes of other users (who would otherwise be asked to provide feedback) might more accurately predict which items will ultimately be purchased. As the absolute volume of online marketplaces (and the items they sell) grows, these traditional recommender systems begin to provide users with recommendations that are only relevant to the individual user's attributes, while recommendations that are more relevant to the attributes of a group of users (who influence the user's choices) may not even be generated. To compensate, users are forced to adopt various methods to share items of interest with others in order to solicit feedback, such as sending screenshots of items, sending hyperlinks to item lists via email and SMS, and providing text descriptions of items, while communication systems are forced to store and transmit more information than is needed to achieve this sharing. This leads to many of the problems discussed earlier, such as increased network traffic, increased use of virtual resources, and reduced internet and telephone bandwidth.

[0018] Embodiments of the technology disclosed herein relate to a recommendation engine and user interface engine that address these challenges by customizing recommendations for a group of users through a concurrent browsing interface. Based on user attributes of each participating user in a concurrent browsing session, the recommendation engine generates recommendations, such as items to purchase, which users should purchase items, and the quantity of items to purchase to obtain a discount. The user interface engine generates a concurrent browsing interface associated with a group of users, configured to track the browsing activity of each user and provide an interface that simultaneously displays a live feed of each participating user's browsing activity to all users. In other words, each participating user can simultaneously view the browsing screens and activities of all participating users as a live feed, along with any annotations, ratings, and recommendations.

[0019] When the user interface engine receives a request to initiate a concurrent browsing session, it receives real-time feeds of each participating user's browsing screen. The request to initiate a concurrent browsing session can specify the purpose of the session, such as purchasing clothing for an upcoming event. In response to the request, the user interface engine can provide a list of items for each user to browse inventory, such as search results for a search query (or search term). As each user browses the generated list of items, all users can simultaneously view real-time feeds of each other's browsing activity. The user interface engine can also generate a payment interface to facilitate shared payments among participating users.

[0020] As users browse the concurrent browsing interface, the recommendation engine receives user attributes for each user participating in the concurrent browsing session, such as location. The recommendation engine can also receive the purpose of the session, which is included in the request that initiates the concurrent browsing session.

[0021] As the concurrent browsing interface receives user input from each participating user, the user interface engine processes this input. In this way, the user interface engine determines how to handle different forms of user input. For example, in response to ratings and comments, the user interface engine can determine that such user input affects user attributes (and thus recommendations) and route the user input to the recommendation engine for processing. In some aspects, user requests for a shared payment interface can be routed to the recommendation engine to collect relevant payment information. This payment information can then be used to generate the payment interface using the user interface engine.

[0022] Then, when generating recommendations, the recommendation engine can consider user attributes as well as route annotations and ratings. Recommendations are based on user attributes of all participating users in the concurrent browsing session and may include ratings of browsed items by weighting user attributes by relevance. For example, if users in a group would incur the lowest shipping costs by purchasing an item based on the user's location (i.e., user attribute), the recommendation engine can generate a recommendation with text instructing the specific user to purchase the item. Recommendations are displayed near the recommended items on the concurrent browsing interface. As the user provides input and continues participating in the concurrent browsing session, an updated user interface with recommendations, annotations, and ratings is provided for display until the user chooses to end the concurrent browsing session.

[0023] For example, a user might prefer to buy outfits for an upcoming wedding with a group of friends. The user can submit a request to initiate a concurrent browsing session with a selected small group of users. As each user joins the concurrent browsing session, each user in the group can see a live feed of the browsing screen and the activity happening on other users' screens. User attributes for each user (e.g., purchase history) are retrieved and analyzed to generate recommendations, such as suggesting the user buy a floral dress because many other users in the group have purchased floral dresses. Recommendations can be overlaid on the concurrent browsing interface. As the user continues to interact with the interface and leaves comments and ratings on items, other participating users can see the feedback in real time on their own screens. The comments and ratings submitted by users are then used to generate recommendations. The user interface is updated with recommendations, comments, and ratings.

[0024] While this technique is presented within the context of a recommendation and user interface engine for generating recommendations and presenting concurrent browsing interfaces for a group of users, it will be recognized that this is merely one example use case in which the described technique can be employed. Those skilled in the art will understand that the fundamental technical methods described herein for generating recommendations for a group of users and providing concurrent browsing interfaces to view the screens and browsing activities of multiple users can be applied to many different contexts. Describing all the different contexts in which this technique can be employed is impractical. Therefore, for simplicity and consistency, the technique will continue to be described in the context of e-commerce.

[0025] Therefore, it should be clear that the technical solution of this application stems from the use of the Internet and the problems arising therefrom. Generating recommendations based on a set of users and effectively presenting them along with real-time feeds of other users' browsing screens and activities is a technical challenge and limitation of the Internet. The advantage of using the Internet lies solely in its ability to provide recommendations based on attributes associated with users and present them to multiple users in a concurrent interface.

[0026] For example, when submitting a search result for "floral dress" on an e-commerce website, users must select a hyperlink to the desired item to share it with other users. However, simply being able to share item details is insufficient for effective navigation and use of the internet. Instead, generating recommendations based on feedback solicited from a group of users, and creating a user interface to capture browsing activity and display that activity to all users, is essential for the internet's operation and users' ability to collaborate with others while shopping. Therefore, instead of providing hyperlinks to share items, users should be able to receive real-time feedback from other users that will influence their final purchase decision; otherwise, it would be impossible for users to combine feedback from multiple users regarding their selected items in real time.

[0027] The technology described in this paper provides solutions to these problems. For example, in contrast to a user interface engine that generates a user interface that only displays the browsing activity of a single user, this user interface engine can collect real-time feeds of browsing screens and present them to all users in the session by providing an interface display that allows each user to view the browsing screens and activities of every other participating user. Similarly, in contrast to a recommendation engine that generates recommendations based on the user attributes of a single user, this recommendation engine can analyze the user attributes of each participating user to generate recommendations based on groups. In this way, the user interface and recommendation engine provide users with relevant recommendations selected from the user attributes of participating users from servers with one or more different Internet connections, and can make efficient use of the Internet. Based on this, the user interface and recommendation engine provide more accurate recommendations with less user input, as it is more likely to provide users with recommendations that are relevant to them. Furthermore, due to the less data transmitted over the network, network bandwidth and overall Internet traffic are generally reduced.

[0028] Now go to Figure 1 , Figure 1 An example concurrent browsing system operating environment 100 in which embodiments of the present disclosure may be employed is shown. Specifically, Figure 1 An advanced architecture of a concurrent browsing system operating environment 100 having components according to embodiments of the present disclosure is shown. Figure 1 The components and architecture are intended as examples, as noted near the end of the detailed implementation.

[0029] Among other components or engines not shown, the concurrent browsing system operating environment 100 includes client computing devices 102 and 104. Client computing devices 102 and 104 are shown communicating with recommendation engine 114 and user interface engine 124 via network 106. Components of the concurrent browsing system operating environment 100 can communicate with each other via one or more networks (e.g., public networks or virtual private networks "VPNs"), as shown by network 106.

[0030] The components of the concurrent browsing system operating environment 100 can operate together to provide a user interface for generating recommendations based on the attributes of multiple users and presenting browsing screens of multiple users with merged recommendations, examples of which will be described further. The concurrent browsing system operating environment 100 supports processing user input from client computing devices 102 and 104. Specifically, client computing devices 102 and 104 can receive comments, browsing activities, and ratings, and communicate these forms of user input to the recommendation engine 114 via the user interface engine 124. Client computing devices 102 and 104 can also operate with the recommendation engine 114 and the user interface engine 124 to transmit user browsing screens and browsing activities and display or cause the display of all or part of the recommendations that may be generated in response to user input (e.g., browsing activities, comments, and ratings). The user interface engine 124 can receive user input, determine the type of received user input, and route the user input to the recommendation engine 114. The recommendation engine 114 can receive user input from the user interface engine 124, receive user attributes 110 from the database 108, process user input, generate recommendations, and send the recommendations to the user interface engine 124 for display.

[0031] Network 106 may include, but is not limited to, one or more local area networks (LANs), wide area networks (WANs), or any other communication network or method. Client computing devices 102 and 104 may be as described herein. Figure 5 The client computing device corresponding to the described computing device.

[0032] Database 108 typically stores information including data, computer instructions (e.g., software program instructions, routines, or services) or models used in embodiments of the technical solutions. Although depicted as a database component, database 108 may be embodied as one or more data storage devices or may be located in the cloud. Figure 5 The memory 512 is an example suitable for use as a database 108. Database 108 stores user attributes 110 and payment information 112, etc.

[0033] User attributes can be user-specific data used to uniquely identify a user or to represent a user in some way. This can include the user's location (which can be derived using an Internet Protocol address), purchase history, search queries, browsing activity, feedback on items or services (including ratings), a list of desired items (or other user-generated lists of items), the stated purpose for initiating a concurrent browsing session, comments, ratings, and other forms of data collected from various sources. For example, user attributes can be received from the user or client computing devices 102 and 104, such as user-provided preferences or user profile information, user-input ratings or reviews about items, or by collecting user internet cookies or client device information. Recommendation engine 114 can use user attributes to generate recommendations for users. Additionally, the terms "item" and "product" are used interchangeably.

[0034] "User" can broadly include any identifiable source of information. For example, a user can include a person using a computing device such as client computing devices 102 and 104. A user can include client computing devices 102 and 104 themselves. In another example, a user can include the network over which client computing devices 102 or 104 communicate, such as a network identified by an Internet Protocol (IP) address. Thus, for example, a user profile can be specific to a particular person, a particular computing device, a particular IP address, etc., and stored in database 108. In a specific example, a user is a potential buyer of goods or services from an e-commerce marketplace website participating in (or being invited to participate in) a concurrent browsing session.

[0035] Payment information 112 includes user-specific data relating to the user's payment methods and how related information facilitates payment. This may include sensitive financial information such as credit or debit card information, security codes, expiry dates, billing addresses, mailing addresses, and other forms of data collected from various sources. It may also include non-sensitive information such as the user's digital shopping cart, which includes items the user has selected that they may purchase but have not yet paid for. For example, payment information may be received from the user or client computing devices 102 and 104, such as when the user provides a credit card number to complete a transaction or through the collection of user internet cookies regarding payment methods. Recommendation engine 114 can use the payment information to generate shared payment mechanisms.

[0036] The user interface engine 124 is shown as including a concurrent browsing interface generator 126, a payment interface generator 128, an annotation interface generator 130, and a recommendation interface generator 132.

[0037] The user interface engine 124 can receive requests from client computing devices 102 and 104 to initiate concurrent browsing sessions among multiple users. The user interface engine 124 can also receive user input and browsing activity as users participating in the concurrent browsing sessions interact with the user interface.

[0038] For example, user input can be any action provided by the user. For example, user input can include mouse clicks or movements, pressure applied to the touchscreen interface, movement along the smart board, or any other interaction with devices or sensors used to receive and communicate user actions.

[0039] For example, browsing activity can be user input about objects on the screen. In other words, browsing activity is the tracking of user interactions with the screen. For example, as a user clicks or moves on the screen, the coordinates of the user's actions and the items affected by these actions can be recorded as browsing activity. Browsing activity can also include user input or actions on other users' browsing screens that are being displayed.

[0040] To initiate concurrent browsing sessions, the user interface engine 124 can use the concurrent browsing interface generator 126. The concurrent browsing interface generator 126 typically presents a graphical user interface (“GUI”) that includes real-time feeds of browsing screens and activities for each participating user. (See reference...) Figure 2 and Figure 3 Discuss the example GUI. See also: Figure 1 In response to a request to initiate a concurrent browsing session, the concurrent browsing interface generator 126 can receive and present real-time feeds of the browsing screens and browsing activities of each participating user. In other words, each participating user can view real-time feeds of the browsing screens and browsing activities of every other participating user. The GUI can include multi-layout screens that display the browsing screens and activities of all participating users in real time. For example, the GUI can include a split-screen format, where each portion of the screen corresponds to the content being browsed by each participating user.

[0041] A GUI can include various screens, such as a project landing page, search results populated in response to a search query, a digital shopping cart page, and a payment checkout. In some aspects, a GUI only shows the user's browsing screen because it relates to the relevant websites each user is visiting. For example, a live browsing screen would only show all the websites (or relevant web pages) that the user is currently browsing, without showing other tabs or applications that might be open on the user's device.

[0042] In some cases, users can choose to view some screens in greater detail than others. For example, in a multi-screen layout, a user can select one screen to view in more detail than others. In other words, the screens and browsing activities of participating users are visible to all users, and users can choose to view a screen in more detail than others. Similarly, when the number of participating users is high, they can choose to view a subset of the screens.

[0043] Now for reference Figure 2 , Figure 2 Example illustrations of a GUI 200 for displaying multiple browsing screens for several users are provided. GUI 200 includes three browsing screens 202, 204, and 206. Browsing screen 202 is from Amy's open browser screen. In this example, Amy is the user viewing the GUI on her device, as indicated by the "(You)" placed next to her name Amy. Browsing screen 204 for Diana and browsing screen 206 for Sarah are also shown in the multi-screen GUI layout. Each browsing screen 202, 204, and 206 simultaneously displays the movement and actions of each corresponding user. In other words, as each user interacts with the user interface and moves their cursor on their screen to click elements and navigate to different web pages, all other participating users can see that user's movement relative to their screen. For example, Amy's browsing screen 202 displays different items compared to the other two browsing screens 204 and 206. As shown, Sarah's browsing screen 206 is larger than the other two screens because Amy chooses to view it in more detail. However, Amy can choose to browse screens 202 or 204 to view these screens more closely than the rest.

[0044] Back Figure 1 In some respects, the concurrent browsing interface generator 126 can receive and display video streams from participating users along with the displayed browsing screen. For example, the live video stream can involve augmented reality for users to check if the browsed items are suitable. In this way, users can see each other via video and decide whether to purchase an item. For example, a user browsing a dress can use augmented reality and a live video stream to "try on" the dress. When the user is displayed via a live video stream, the augmented reality function can overlay the dress on the user to show how the dress looks on the user.

[0045] The concurrent browsing interface generator 126 can be configured to generate an interactive GUI that can be manipulated in response to user input. In other words, users can interact with the GUI to change the features displayed on the multi-screen layout GUI for all users. For example, the concurrent browsing interface generator 126 can accept search queries from each user participating in the concurrent browsing session in the form of images and use live video streams to scan images, video frame images, barcodes, and / or text. In some aspects, search results generated from the live video stream can be presented in a separate GUI panel. The GUI panel can present search results, allowing users to capture live video stream frames and request search results to be generated from those frames. Similarly, the concurrent browsing interface generator 126 can be configured to allow users to navigate to other pages of the website, such as payment or checkout pages, item landing pages, and digital shopping cart pages.

[0046] The concurrent browsing interface generator 126 can also be configured to identify different types of user input and route that input to the recommendation engine 114 as needed. In other words, the concurrent browsing interface generator 126 can receive user input while the user interacts with the GUI and determine whether the user input is a comment or a rating. In both cases, the concurrent browsing interface generator 126 can route these types of user input to the recommendation engine 114 for processing. Similarly, the concurrent browsing interface generator 126 can treat search queries as user attributes and route that information to the recommendation engine 114 accordingly. Additionally, the concurrent browsing interface generator can determine whether a user or user group has chosen to end the concurrent browsing session. In some cases, when a request to end the concurrent browsing session is received, the concurrent browsing interface generator 126 prompts the user to choose whether to save the session's record or event log.

[0047] To provide annotation display during concurrent browsing sessions, the user interface engine 124 can use an annotation interface generator 130. The annotation interface generator 130 typically renders a GUI that incorporates annotations received from users onto each user's screen. The annotation interface generator 130 can accept user input, such as text, emojis, or images, regarding screen submissions from other users.

[0048] Comments are provided by users participating in concurrent browsing sessions and allow users to provide feedback on any other user's screen while browsing. Comments can include text, emojis, browsing activities, payment-related actions (such as adding items to a digital cart), and ratings. For example, if one user is viewing search results in response to the search query "dress," another user can provide additional feedback, such as a comment, on a live feed to the first user's screen. Recommendation engine 114 can use comments to generate recommendations for users based on the content of those comments. For example, recommendation engine 114 can analyze a text comment like "I like this" and increase the score of the item to which that comment belongs.

[0049] Comments can be received via user input from client computing devices 102 and 104. For example, if one user comments on another user's browsing screen, all other users participating in the concurrent browsing session will view the comment in the same location that the commenting user placed on that particular browsing screen. In some respects, the comment interface generator 130 can overlay or superimpose the received comments onto the concurrent browsing interface that displays each user's browsing activity in real time. Additionally, the comment interface generator 130 can use the coordinates of the browsing activity to record the placement of the comments to determine where to place them in a concurrent browsing session viewed by other users.

[0050] Brief Reference Figure 3 It provides a sample concurrent browsing user interface 300. Figure 3 The text shows annotations such as user ratings 308 and 314 being placed relative to their corresponding items. Similarly, the "+" annotation button 306 allows users to place annotations near the items they correspond to.

[0051] Back Figure 1 The payment interface generator 128 typically presents a GUI for users to view, share, and split payments. The payment interface generator 128 can generate a payment interface in response to at least one user navigating to a payment-related webpage. For example, a user selecting to view their digital shopping cart, adding items to their digital shopping cart, or proceeding to the checkout page can cause the payment interface generator 128 to generate a payment interface that allows the user to share the cost of the selected purchase. The payment interface generator 128 can also receive user input or comments related to the payment of an item or the selection of a payment item. For example, if a user named Amy clicks to add an item to another user's (Sarah's) digital shopping cart, which will appear on another user's (Sarah's) browsing screen, the payment interface generator 128 can generate a payment interface reflecting the item being added to Amy's digital shopping cart.

[0052] The payment interface generator 128 can receive aggregated payment information from the user and display it in the payment interface. The payment interface can be a multi-screen layout showing each user's digital shopping cart and real-time browsing of the activity. In some aspects, the payment interface can be a single screen displaying all of each user's digital shopping carts. With all selected purchases displayed, the user can choose to split the payment for selected items via a GUI. In some aspects, the user can split the cost of the entire digital shopping cart.

[0053] In some ways, aggregated payment information included in the payment interface can be pre-filtered to remove any sensitive financial information. It's also possible to display aggregated payment information so that each user only sees their own sensitive financial information. For example, the GUI could display each user's digital shopping cart to all users, including the items each user has selected and their prices, but each user can only see their own corresponding sensitive information, such as billing address and credit card information.

[0054] The recommendation interface generator 132 typically presents a GUI to users to view recommendations about browsed items or payments for those items. The recommendation interface generator 132 can receive recommendations about browsed items, wish lists, digital shopping carts, and shared payments. Each user views recommendations placed on other users' screens as well as recommendations placed on their own screen. In some respects, received recommendations are overlaid or superimposed on any combination of concurrent browsing interfaces, payment interfaces, and annotation interfaces. For example, recommendations can be displayed on concurrent browsing interfaces with overlaid annotations and ratings. Recommendations can be displayed only to the user to whom the recommendation applies, or they can be displayed to all users.

[0055] Recommendations can be placed relative to other comments and ratings existing on the GUI to ensure alignment with relevant items. In some respects, the recommendation interface generator 132 provides item display in the order of the generated ratings.

[0056] Brief Reference Figure 3 , Figure 3 Recommendation 310 is shown overlaid on the GUI, which displays annotations such as group ratings 304 and 312, and user ratings 308 and 314. Recommendation 310 suggests that users viewing the browsing screen should purchase item 320 to save Sarah $3.75 in shipping costs. Similarly, recommendation 316 is placed relative to its associated item 320 (floral dress).

[0057] Recommendation engine 114 is shown as including user attribute receiver 116, rating generator 118, shared payment generator 120, and recommendation generator 122. Recommendation engine 114 can operate in conjunction with user interface engine 124 to receive and process user input, such as updated browsing activity and search queries, to generate and provide recommendation displays.

[0058] User attribute receiver 116 typically receives user attributes identified by recommendation engine 114 in response to the initiation of a concurrent browsing session and in response to user input (e.g., clicking on an item or submitting a search query). To retrieve user attributes, user attribute receiver 116 can identify users currently in the concurrent browsing session and retrieve the user attributes identified by recommendation engine 114 from user attributes 110 in database 108. For example, if recommendation engine 114 requests a specific recommendation for a set of identified users, such as a recommendation to reduce shipping costs, user attribute receiver 116 can retrieve the locations (related to the recommendation) of all users in the concurrent browsing session from user attributes 110 in database 108. Recommendation engine 114 can use the retrieved locations to calculate the user with the lowest shipping cost for a specific item.

[0059] Briefly switch to Figure 3 A sample concurrent browsing interface 300 is provided. The comment button 306 provides an example of a mechanism that users can use to provide comments or feedback by clicking the "+" button on the user interface. As indicated by label "Sarah" 318, the displayed browsing screen shows browsing activity from a user named Sarah. For example, when a user who is not Sarah selects the "+" button, that user is presented with the option to enter text and / or emoji selections related to item 320 on Sarah's browsing screen 300. Similarly, selecting the "+" button can allow a user to add a manual rating to an item that another user is viewing.

[0060] Refer again Figure 1 The rating generator 118 typically generates ratings for items viewed by users in a concurrent browsing session. The rating generator 118 can calculate item ratings based on user attributes of the users participating in the concurrent browsing session and the purpose of the concurrent browsing session. These ratings can be calculated for items from various sources (browsing screens, search results, wish lists, and digital shopping carts).

[0061] The rating generator 118 can generate two types of ratings: group ratings and user ratings. Group ratings are generated by the rating generator based on user attributes of all users participating in the concurrent browsing session. For example, user attributes of participating users (e.g., purchase history) can be retrieved and used to generate a rating for the current item. If an item was previously purchased by other users and highly rated, the rating generator 118 can generate a high group rating for that item. User ratings are generated based on input from participating users and are displayed as corresponding to a specific user. During the concurrent browsing session, ratings can be entered by participating users via client computing devices 102 and 104. For example, a user can provide feedback on another user's browsing screen by providing a numerical rating for an item. Ratings can also be pre-entered by participating users before the concurrent browsing session. For example, if a user's search results include previous purchases from another user who previously provided a rating for an item as a review, the rating generator 118 can retrieve the previous rating entered by that other user.

[0062] As an example, if a concurrent browsing session is initiated for a user to “buy a dress,” the rating generator 118 can evaluate user attributes such as purchase history for the users participating in the session to generate a rating for a user’s search query “dress.” If the user attributes of most users participating in the session indicate that the user likes floral dresses, the rating generator 118 can generate a higher rating for search results that include floral dresses. Search results that are less consistent with the purpose of the concurrent browsing session may receive lower ratings. For example, if the purpose of the concurrent browsing session is to buy clothes for a party, search results that include clothing items designated as suitable for parties may receive a higher rating than search results that include clothing items designated as casual. Similarly, the purpose of the concurrent browsing session can include details about the relationships between participating users and rate the search results accordingly. For example, the purpose of the concurrent browsing session may indicate that the user is in a romantic relationship and clothing items with complementary or complementary color schemes may receive higher ratings. Likewise, search results that are not favored by the group based on user attributes (e.g., purchase history) may receive lower ratings.

[0063] Briefly switch to Figure 3 , Figure 3This is an example concurrent user interface with three participating users: Sarah, Amy, and Diana. Browsing screen 300 shows Sarah's browsing screen and activities. Group ratings 304 and 312, and user ratings 308 and 314 provide examples of ratings. Group ratings 304 and 312 are generated by the rating generator based on the user attributes of all three users participating in the concurrent browsing session. In this example, the user attributes of Amy, Diana, and Sarah indicate that all users have previously purchased a floral dress but not a striped dress. The rating generator 118 provides a higher group rating 304 for item 320, which is a floral dress, compared to group rating 312 for item 322, which is a striped dress. User ratings 308 and 314 are generated when Amy and Diana manually enter ratings for each dress.

[0064] Back Figure 1 The shared payment generator 120 typically generates a payment system that aggregates payment information from all participating users, including items in each user's digital shopping cart. To retrieve payment information, the shared payment generator 120 can identify users currently in a concurrent browsing session and retrieve and aggregate payment information identified by the recommendation engine 114 from the payment information 112 in the database 108. The aggregated payment information can be partially obfuscated or removed to avoid exposing sensitive financial information such as credit card numbers in the user interface, instead providing the total amount in the digital shopping cart. The shared payment generator 120 can also receive user input or comments (e.g., a user selecting items to add to their digital shopping cart) and update the payment information 112 in the database 108 to reflect that a particular user has added items to their digital shopping cart.

[0065] In some respects, the shared payment generator 120 may choose to provide information for display to each user. For example, for a given user, the shared payment generator 120 may collect digital shopping cart information from all other users and display it to that user along with the given user's payment method. In this way, a particular user's financial information is only visible on that user's screen and hidden from other users, but that user can still view the digital shopping carts of other users.

[0066] The shared payment generator 120 can also provide users with a mechanism for splitting payments. For example, when three users agree to split the cost of an item in one user's digital shopping cart, the shared payment generator 120 can calculate the total amount in the digital shopping cart. In some respects, the shared payment generator 120 allows users to split payments for the entire digital shopping cart. The shared payment generator 120 works in conjunction with the user interface engine 124 to generate a user interface for shared payments among participating users.

[0067] Back Figure 1The recommendation generator 122 typically generates recommendations based on user attributes of users involved in a concurrent browsing session. Recommendations may include any suggestions related to the purchase of items or services. The recommendation generator 122 may differentiate aspects of user attributes and assign weights to these aspects indicating their relevance to the generated recommendations. For example, the recommendation generator 122 may receive user attributes (e.g., a search query) of a user participating in the current concurrent browsing session and determine the historical purchases of other users in response to the search query. Those historical purchases of other users may be recommended to the user who inputs a relevant search query as a possible purchase. The search query may be input from the user or client computing devices 102 and 104 in various forms. For example, a user may upload an image (e.g., a dress that another user might be interested in) as a search query to populate the search results. Users may also scan barcodes or use frames from a live video stream as other forms of search queries.

[0068] Recommendation generator 122 can also use other user attributes such as browsing activity and payment information to provide additional recommendations, such as which user should purchase an item to minimize shipping costs or promotions that would apply if some users purchased an item. For example, recommendation generator 122 can receive user attributes (e.g., multiple users in a concurrent browsing session clicked on a wallet link and each user has a wallet in their digital shopping cart) and determine that the wallet is relevant to all participating users. Recommendation generator 122 can determine that a promotional discount exists for purchasing multiple wallets. Continuing this example, the recommendation to a user could be a promotional discount when a certain number of wallets are purchased. The recommendation could also include a user purchasing all wallets to receive a bulk purchase discount.

[0069] Recommendation generator 122 can also use additional user attributes (e.g., location and the purpose of the concurrent browsing session) to provide recommendations. For example, recommendation generator 122 receives a user's location and can recommend an item that will result in the lowest shipping cost for that user. In some respects, each user's location is derived from their IP address. Recommendation generator 122 can also receive a user's location and the purpose of the concurrent browsing session to recommend which user to buy the item. For example, if the purpose of the concurrent browsing session is to buy a dress for an upcoming wedding in Los Angeles, recommendation generator 122 can recommend the user with the lowest shipping cost to buy the item that can then be distributed during the event, thus reducing shipping costs for users in locations that result in higher shipping costs. Recommendation generator 122 can also use a user's location to recommend items based on specific characteristics of that location. For example, if users are browsing winter jackets, and one user lives in a warm climate while other users live in a colder climate, recommendation generator 122 can recommend that the user in the warm climate buy a lighter winter jacket, while the other users buy a heavier winter coat. Similarly, if the purpose of a concurrent browsing session indicates that a user in a warmer climate is planning to visit a user in a colder climate during the winter, then the recommendation generator 122 can recommend that the user buy a heavier winter jacket instead of a lighter one.

[0070] Briefly switch to Figure 3 Recommendation 310 provides examples of recommending items for users to purchase to save on shipping costs. For example... Figure 3 As shown, Recommendation 310 states that users viewing this item should purchase it to save Sarah $3.75 in shipping costs. In this example, Recommendation 310 is generated based on each user's location and associated shipping costs.

[0071] The recommendation generator 122 can also use other user attributes such as purchase history and search terms to provide additional recommendations. For example, the recommendation generator 122 can receive user attributes (e.g., search queries from users and each user's purchase history) and determine items to recommend to the user based on the list of purchase history. For example, the recommendation generator 122 can receive a search query for "floral dress" from a specific user and any associated search results. The recommendation generator 122 can compare the list of search results with purchase history from other users to find items that are associated with both the search results and purchase history for recommendation. The recommendation generator 122 can also use the first user's search query to search each user's purchase history to determine other potentially associated purchase history. For any item associated with purchase history and search query, the recommendation generator 122 can generate a recommendation for the user to purchase that item.

[0072] In some respects, recommendation generator 122 can compare search results from two different users within a group to identify common items. Based on the identified items, recommendation generator 122 can further determine whether a promotion applies to the item and recommend that the user take advantage of the promotion. For example, if one user searches for “red wallet” and another user searches for “large wallet,” recommendation generator 122 can identify search results present in both groups and identify that a promotion currently applies to purchasing two or more of the same item. Continuing this example, recommendation generator 122 can generate recommendations for participating users to purchase the item to receive a bulk discount.

[0073] Briefly switch to Figure 3 Recommendation 316 provides examples of recommending users to purchase multiple items to obtain discounts. For example... Figure 3 As shown, Recommendation 316 states that if you buy two items of clothing, the group will save $5. In this example, Recommendation 310 is generated based on search results from multiple users' browsing screens, including items with applicable discounts.

[0074] Recommendation generator 122 can also use user attributes such as ratings to generate recommendations. Recommendation generator 122 can retrieve ratings from rating generator 118 to generate recommendations. For example, recommendation generator 122 can receive the rating for each search result presented in response to a search query. Using the ratings of each received item, recommendation generator 122 can rank items based on the ratings as recommendations of which items to buy. Item ranking based on ratings can be performed in multiple areas: search results, digital shopping cart, wish list, etc.

[0075] Briefly switch to Figure 3 The "Sort by Rating" icon 302 provides an example of sorting the displayed items. For example... Figure 3 As shown, each item has group ratings (e.g., group ratings 304 and 312) and user ratings (e.g., user ratings 308 and 314). The "Sort by Rating" icon 302 allows users to sort the displayed items by both group and user ratings. The sorting can be in ascending or descending order.

[0076] The recommendations generated by the recommendation generator 122 can take many forms: text, emojis, audio, and other types of media. Once generated, the recommendations are provided to the user for display via the user interface engine 124.

[0077] Back Figure 1 The recommendation generator 122 works in conjunction with the user interface engine 124 to provide recommendation displays to client computing devices 102 and 104.

[0078] It should be understood that the components of recommendation engine 114 and user interface engine 124 can be discrete algorithms or models. However, as mentioned above, the components of recommendation engine 114 and user interface engine 124 are described as discrete components to aid in describing the technique. Although in other embodiments intended by this disclosure, the functionality of these components may overlap or be further divided. Therefore, it should also be understood that other embodiments of the technique include combined implementations of these components. For example, the recommendation interface generator may be a modification of the concurrent browsing interface generator (modified concurrent browsing interface generator) that incorporates recommendations into the concurrent browsing interface. In this case, the modified concurrent browsing interface generator can generate and provide the display of a user interface with a multi-screen layout that incorporates recommendations. Although these (and other) components are described separately in this disclosure, it is intended to also include modified or combined implementations of these components. That is, for example, where the functions are described separately as “generating a concurrent browsing interface” and “generating a recommendation interface”, it is intended that the steps of these separately disclosed steps also include a single action of generating a concurrent browsing interface with recommendations incorporated by the modified concurrent browsing interface generator.

[0079] The concurrent browsing system described herein can be used to execute the method. In an embodiment, one or more computer storage media having computer-executable instructions thereon cause the one or more processors to execute the method in the search system when executed by the processors.

[0080] Figure 4 Method 400 illustrates an example for generating a user interface with recommendations, annotations, and ratings. In box 402, a request to initiate a concurrent browsing session is received by the user interface engine and routed to the recommendation engine. This request is received from the client computing device. The client computing device may be located remotely from both the user interface and the recommendation engine, or it may host all or part of the engine. In either case, the user interface engine receives the request from the client computing device. In some aspects, the request may include a purpose or reason for generating the concurrent browsing session for recommendations. For example, the specified purpose could be “shopping for a wedding in Los Angeles in Spring 2020.” In box 404, a real-time feed of user browsing screens reflecting the browsing activity of each participating user is received. In box 406, the concurrent browsing interface is generated and its display is provided. A concurrent browsing interface generator can be used to generate the concurrent browsing interface. Figure 1 The concurrent browsing interface generator 126 is an example concurrent browsing interface generator that can be used by this method.

[0081] In box 408, receive user attributes. You can use the user attribute receiver to receive user attributes from participating users. Figure 1 User attribute receiver 116 is an example user attribute receiver that can be used by this method. By receiving user attributes, Figure 1 The recommendation engine 114 can determine the recommendations. In box 410, any received user input is processed. Figure 1 The concurrent browsing interface generator 126 is an example user input processor that can be used by this method. The concurrent browsing interface can determine the type of submitted user input and route the input accordingly. For example, user input of an emoji can be identified as a comment and routed to other components of the recommendation engine 114 and the user interface engine 124. Components of the recommendation engine 114 (e.g., rating generator 118 and shared payment generator 120) can process the received user input. For example, if the user input has been identified as a comment, rating, or browsing activity, the rating generator 118 can retrieve user attributes, receive the user input, and process these values ​​to generate a rating. Similarly, if the user input involves requesting a shared payment interface or navigating to payment-related content, the shared payment generator 120 can retrieve payment information 112 and user attributes 110, as well as obfuscated sensitive financial information, for display via the payment interface, as generated by the user interface engine 124.

[0082] In box 412, generate recommendations. A recommendation generator can be used to generate recommendations. Figure 1 Recommender generator 122 is an example recommendation generator that can be used by this method. It receives user attributes, comments, ratings, and browsing activity. Figure 1 The recommendation engine 114 can generate recommendations. These recommendations can range from promotional suggestions to which items to buy, and even which users should purchase specific items.

[0083] Box 414 provides the updated concurrent browsing interface display. Figure 1 The payment interface generator 128, annotation interface generator 130, and recommendation interface generator 132 are example interface generators suitable for use by this method. Payment interface generator 128 generates shared payment interfaces, such as multi-screen layouts of digital shopping carts, aggregated digital shopping carts, and mechanisms for splitting payments. Annotation interface generator 130 generates an annotation interface that includes annotations submitted by users participating in concurrent browsing sessions, allowing each user to view annotations related to the items placed next to them. Recommendation interface generator 132 generates a recommendation interface, allowing each user to view recommendations in addition to viewing a live feed of each user's browsing screen and activities.

[0084] As shown in box 416, an evaluation is performed regarding whether to request the termination of the concurrent browsing session. If a request to terminate the concurrent browsing session is received, box 418 is executed. If no request to terminate the concurrent browsing session is received, box 404 is executed.

[0085] In box 418, end the concurrent browsing session. The concurrent browsing interface generator can prompt the user to choose whether to save the session's record or event log.

[0086] Having described an overview of embodiments of the present technology, the following description provides an example operating environment in which embodiments of the present technology may be implemented to provide a general context for various aspects. First, refer to... Figure 5 Specifically, an example operating environment for implementing embodiments of the present technology is shown, and it is generally designated as computing device 500. Computing device 500 is merely an example of a suitable computing environment and is not intended to impose any limitation on the scope or functionality of the present technology. Nor should computing device 500 be construed as having any dependency or requirement on any of the components or combinations of components shown.

[0087] The techniques disclosed herein can be described in the general context of computer code or machine-usable instructions (including computer-executable instructions such as program modules) that are executed by a computer or other machine (e.g., a personal data assistant or other handheld device). Generally, a program module, including routines, programs, objects, components, data structures, etc., refers to code that performs a specific task or implements a specific abstract data type. This technology can be implemented in various system configurations, including handheld devices, consumer electronics, general-purpose computers, and more specialized computing devices. This technology can also be implemented in distributed computing environments, where tasks are performed by remote processing devices linked via a communication network.

[0088] refer to Figure 5 The computing device 500 includes a bus 510 that directly or indirectly couples to the following devices: memory 512, one or more processors 514, one or more presentation components 516, input / output ports 518, input / output components 520, and an illustrative power supply 522. The bus 510 may represent one or more buses (e.g., an address bus, a data bus, or a combination thereof). Although lines are shown for clarity... Figure 5 The various boxes are depicted, but in reality, the individual components are not so clearly defined, and metaphorically speaking, these lines are more accurately described as gray and blurry. For example, one might consider presentation components such as display devices as I / O components. Furthermore, the processor has memory. We recognize this as a characteristic of the art and reiterate... Figure 5 The figures only illustrate example computing devices that can be used in conjunction with one or more embodiments of this technology. No distinction is made between categories such as "workstation," "server," "laptop," "handheld device," etc., because all of these are considered within the scope of this technology. Figure 5 Within the scope of [the term] and the term "computing device".

[0089] Computing device 500 typically includes a variety of computer-readable media. Computer-readable media can be any available media accessible by computing device 500 and includes volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable media can include computer storage media and communication media.

[0090] Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic tape cassettes, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible by a computing device 500. Computer storage media itself does not include signals.

[0091] Communication media typically embody computer-readable instructions, data structures, program modules, or other data in the form of modulated data signals, such as carrier waves or other transmission mechanisms, and include any information delivery medium. The term "modulated data signal" refers to a signal whose characteristics are set or altered in a manner that encodes information within the signal. By way of example and not limitation, communication media include wired media (e.g., wired networks or direct wired connections) and wireless media (e.g., acoustic, RF, infrared, and other wireless media). Any combination of the above types should also be included within the scope of computer-readable media.

[0092] Memory 512 includes computer storage media in the form of volatile or non-volatile memory. Memory can be removable, non-removable, or a combination thereof. Example hardware devices include solid-state memory, hard disk drives, optical disk drives, etc. Computing device 500 includes one or more processors that read data from various entities such as memory 512 or I / O components 520. Presentation component 516 presents data indications to a user or other device. Examples of presentation components include display devices, speakers, printing components, vibration components, etc.

[0093] I / O port 518 allows computing device 500 to be logically coupled to other devices including I / O components 520, some of which may be built-in. Illustrative components include microphones, joysticks, game controllers, disc-shaped satellite antennas, scanners, printers, wireless devices, etc.

[0094] The embodiments described above can be combined with one or more of the specifically described alternatives. Specifically, the claimed embodiments may include references to several other embodiments in the alternatives. The claimed embodiments may specify further limitations on the claimed subject matter.

[0095] The subject matter of this technology has been specifically described herein to satisfy legal requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventors have anticipated that the claimed or disclosed subject matter may also be embodied in other ways, including different steps or combinations of steps similar to those described herein, in combination with other existing or future technologies. Furthermore, although the terms “step” or “box” may be used herein to refer to different elements of the method employed, these terms should not be construed as implying any particular order between the various steps disclosed herein, unless and only when the order of the various steps is explicitly stated.

[0096] For the purposes of this disclosure, the word "comprising" has the same broad meaning as the word "including," and the word "access" includes "receiving," "referring to," or "retrieval." Furthermore, the word "communication" has the same broad meaning as the words "receiving" or "transmitting," which is supported by a software- or hardware-based bus, receiver, or transmitter using the communication medium described herein. Additionally, the word "initiate" has the same broad meaning as the words "execute" or "instruct," wherein a corresponding action can be completed or interrupted based on the occurrence of another action.

[0097] Furthermore, unless otherwise indicated, words such as “one” and “a” include both plural and singular forms. Thus, for example, the constraint of “feature” is satisfied when one or more features are present. Additionally, the term “or” includes conjunctions, antonymous conjunctions, and both (therefore, a or b includes both a and b).

[0098] The distributed computing environment described herein is merely an example. Components can be configured to perform novel aspects of this technology, where the term "configured to" can mean "programmed to" use code to perform a specific task or implement a specific abstract data type. Furthermore, while embodiments of this technology can generally be referenced to the distributed data object management system and schematic diagrams described herein, it should be understood that the described technology can be extended to other implementation environments.

[0099] As can be seen from the foregoing, this technology is well-suited to achieving all the aforementioned intentions and objectives, including other advantages that are obvious or inherent to this structure. It should be understood that certain features and sub-combinations are useful and can be used without reference to other features and sub-combinations. This is contemplated by and within the scope of the claims. Since many possible embodiments of the described technology can be implemented without departing from the scope, it should be understood that everything described herein or shown in the accompanying drawings should be interpreted as illustrative rather than restrictive.

Claims

1. A computer-implemented method, comprising: A user interface is provided for display on a first user's first computing device, the user interface being configured to simultaneously display a first browsing screen showing the first user interacting with the site and a real-time feed of a second browsing screen presented on a second user's second computing device showing the second user interacting with the site; In response to input instructing the first user to select a first item from the live feed of the second browsing screen, the first item is added to a first digital shopping cart associated with the first user; Receive a request to split the payment for the first item in the first digital shopping cart; A first payment interface is provided for display on the first computing device for paying a first portion of the payment; as well as A second payment interface is provided for display on the second computing device for paying the second portion of the payment.

2. The method according to claim 1, wherein, The first digital shopping cart includes a shared digital shopping cart that can be displayed on the first computing device and the second computing device.

3. The method according to claim 2, wherein, The first digital shopping cart includes a second item added to the first digital shopping cart by the second user.

4. The method according to claim 1, wherein, The request to split the payment for the first item in the first digital shopping cart includes: a request to split the payment for all items in the first digital shopping cart.

5. The method according to claim 1, wherein, The method further includes: Provide another user interface for simultaneously presenting a first digital shopping cart associated with the first user and a second digital shopping cart associated with the second user, wherein the first digital shopping cart includes one or more items added by the first user and the second digital shopping cart includes one or more items added by the second user.

6. One or more computer storage media containing computer-executable instructions, which, when executed by one or more processors, cause the one or more processors to perform operations including: A user interface is provided for display on a first user's first computing device, the user interface being configured to simultaneously display a first browsing screen showing the first user interacting with the site and a real-time feed of a second browsing screen presented on a second user's second computing device showing the second user interacting with the site; In response to input instructing the first user to select a first item from the live feed of the second browsing screen, the first item is added to a first digital shopping cart associated with the first user; Receive a request to split the payment for the first item in the first digital shopping cart; A first payment interface is provided for display on the first computing device for paying a first portion of the payment; as well as A second payment interface is provided for display on the second computing device for paying the second portion of the payment.

7. The medium according to claim 6, wherein, The first digital shopping cart includes a shared digital shopping cart that can be displayed on the first computing device and the second computing device.

8. The medium according to claim 7, wherein, The first digital shopping cart includes a second item added to the first digital shopping cart by the second user.

9. The medium according to claim 6, wherein, The request to split the payment for the first item in the first digital shopping cart includes: a request to split the payment for all items in the first digital shopping cart.

10. The medium according to claim 6, wherein, The operation also includes: Provide another user interface for simultaneously presenting a first digital shopping cart associated with the first user and a second digital shopping cart associated with the second user, wherein the first digital shopping cart includes one or more items added by the first user and the second digital shopping cart includes one or more items added by the second user.

11. A system comprising: One or more processors; as well as One or more computer storage media storing computer-usable instructions, which, when used by the one or more processors, cause the one or more processors to perform operations including the following: A user interface is provided for display on a first user's first computing device, the user interface being configured to simultaneously display a first browsing screen showing the first user interacting with the site and a real-time feed of a second browsing screen presented on a second user's second computing device showing the second user interacting with the site; In response to input instructing the first user to select a first item from the live feed of the second browsing screen, the first item is added to a first digital shopping cart associated with the first user; Receive a request to split the payment for the first item in the first digital shopping cart; A first payment interface is provided for display on the first computing device for paying a first portion of the payment; as well as A second payment interface is provided for display on the second computing device for paying the second portion of the payment.

12. The system according to claim 11, wherein, The first digital shopping cart includes a shared digital shopping cart that can be displayed on the first computing device and the second computing device.

13. The system according to claim 12, wherein, The first digital shopping cart includes a second item added to the first digital shopping cart by the second user.

14. The system according to claim 11, wherein, The request to split the payment for the first item in the first digital shopping cart includes: a request to split the payment for all items in the first digital shopping cart.

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