According to various embodiments, a logistics information managing method may include identifying at least one of information on a source fulfillment center and information on a target fulfillment center, identifying transfer target candidate order information among order information allocated to the source fulfillment center, and allocating at least a portion of the transfer target candidate order information to the target fulfillment center. Through this, order information previously allocated to a fulfillment center may be reallocated to another fulfillment center, thereby preventing a non-compliance with order processing. Other embodiments are possible as well.

A buy-online pickup-in-store (BOPIS) order at risk of abandonment is saved by converting the order to same-day delivery. A risk deadline is established using a combination of cognitive prediction, a customer-specific time frame, order constraints, and real-time data. The cognitive prediction uses a customer profile having historical data pertaining to previous BOPIS orders from this customer. If the product is not picked up by the risk deadline, a check is made to see if same-day delivery is feasible. If so, a notification is transmitted to the customer with proposed delivery details. The notification can be sent at an optimal time based on previous notifications sent to the customer and time constraints related to the order, and using an optimal communication channel based on previous engagement rates with the customer over different communication channels. Once confirmation is received from the customer, the product is shipped, saving the sale.

An approach is provided in which the approach arranges a first parameter group and a second parameter group in an initial priority order on a user interface. The first parameter group includes a selection of a first parameter option that generates a restriction on a second parameter option included in the second parameter group. In response to receiving a user input, the approach dynamically modifies the user interface by rearranging the first parameter group and the second parameter group into a different priority order and removes the restriction on the second parameter option based on the different priority order.

A system for obtaining user parameters of e-commerce users to auto complete checkout forms includes one or more processors configured to monitor user interactions of a user with a merchant website on a user device; determine an intent to purchase one or more items based of the monitoring; determine an unfilled template of a checkout form associated with the merchant website based on previously stored website information, wherein the unfilled template of the checkout form comprises a plurality of checkout form fields; determine one or more user parameters associated with the user device; apply a machine learning algorithm to predict a geolocation of the user device based on the one or more user parameters; and auto fill the at least one of the plurality of checkout form fields in the unfilled template of the checkout form based on the predicted geolocation.

The present disclosure relates to a method for supplying blockchain computing power and a system thereof. The method comprises the steps of: receiving a computing power purchase request sent by a user-side terminal; generating purchase result data according to the computing power purchase request; scheduling a first blockchain server to provide users with computing service according to the purchase result data; when the first blockchain server stops serving, starting timing to obtain the target duration; judging whether the first blockchain server restarts the service when the target duration is less than the preset duration threshold, and obtaining a preset result; when the preset result is YES, improving the computing power of the first blockchain server correspondingly, so that the total service duration of the user and the actually obtained total computing power remain unchanged; when the preset result is NO, scheduling a second blockchain server to provide users with computing service. The present disclosure enables the user to obtain stable computing power.

Systems and methods to improve an e-commerce platform through modifications to the checkout and fulfillment process aimed at reducing the incidence of returns. Modifications may include determining a likelihood of a return and, based on the likelihood of return, generating a staged fulfillment process in which an order is split in two linked orders that are processed serially and based on receipt of confirmation to proceed after delivery of a first one of the orders. Modifications may include determining during the checkout process that a custom return policy is to be used and generating the custom return policy based, at least in part, on one or more of a purchaser return history, the product item or items in the order, and product return history.

In an automated product delivery method a request for delivery of a product is received from a user device. One or more product delivery options are determined and transmitted to the user device and a selected product delivery option is received from the user device. The product is provided to the selected delivery location in advance of the selected delivery time window. A delivery processing server receives user device location information and determines an estimated arrival time of the user at the delivery location. At the estimated arrival time, the product is securely staged for pickup by the user. The delivery processing server receives user validation information, and responsive to validation of the user information, transmits to the delivery location processing device, an instruction to allow access to the product by the user, and access to the product by the user is allowed.

Systems and methods for obtaining product information via a conversational user interface. The communication channel receives communication from a user, the intent and entities of which are deduced by the NLP. These are communicated by the fulfillment API to the knowledge engine which retrieves information that fulfills the intent. The information is communicated to the fulfillment API, which converts the intent into a response, which in turn is forwarded by the NLP to the communication channel, and back to the user.

An online concierge system maintains a taxonomy associating one or more specific items offered by a warehouse with a category. When the online concierge system receives a selection of an item from a user for inclusion in an order, the online concierge system determines a category including the selected item. From prior received orders, the online concierge system 102 identifies additional categories including one or more items included in various prior received orders. Based on cooccurrences of the category and the additional categories, the online concierge system generates scores for the additional categories. An additional category is selected based on the scores and specific items from the selected additional category are displayed via an interface for selection by the user.

A system for creating, communicating and fulfilling delicatessen orders, which in preferred embodiments is provided by an internet based smart device app for customers to make orders and an interface for one or more delis to fulfill such orders.