Amazon Technologies, Inc.

Gustavo Eduardo Lopez

Michael Martin George

Ashraf Alkarmi

Pawan Lata Uppuluri

Aditya Balwant Pande

Wesley Shawn Davis

Andrew Russell Turner

Ruslan Anatolyevich Polyanskiy

Canku Alp Calargun

Gregory Forrest Wright

Yenchao Chua

Dana Christopher LoPiccolo-Giles

Edward Hon

Praveen Krishnan

Scott Pardy

Evan Weiss

Kristin Whitney Baker

Jason Aaron McMahon

Matthew Brett Roemer

Joel R Spiegel

David Craig Yanacek

Ian W Freed

Paul J Walsh

Steven Todd Rabuchin

AndyGibb Halim

Maciej Tadeusz Golonka

Siddharth Arora

Bharathi Masilamani

Brandon Yarbrough

Christian M Ricci

Simoina D Vasen

Mary Miller

Clifford A Cancelosi

Karthik Narayan

Valerie Grace Millar

Aditya B Pande

Bhushan H Shah

Merchant quality may be inferred through machine learning techniques. A customer satisfaction classifier may receive data associated with a customer's engagement with a merchant, and may apply a machine learning model to the received data in order to infer a satisfaction of the customer with the merchant. The inferred satisfaction may be used to determine a rating of the merchant that is imputed to the customer.

Machine learned merchant ratings

A service provider may obtain information about a plurality of items offered for acquisition by a merchant. The service provider may identify one or more items of the plurality of items that are the same as one or more respective items included in a catalog of items associated with the service provider. The items included in the catalog of items may be available for acquisition via the service provider. Additionally, the service provider may communicate with the merchant to offer services in relation to the items included in the service provider catalog of items that match the items offered by the merchant.

Merchant services for service provider catalog items

Inactive areas of interest to a merchant delivery service may be identified and customers within those areas may be utilized to recruit eligible merchants to participate with a merchant delivery service so that the merchant delivery service can deliver customer ordered products on behalf of participating merchants to the customers within the area. In some implementations, the merchant delivery service may provide incentives to customer to incent the customers to recruit eligible merchants to participate with the merchant delivery service. Likewise, incentives may be provided to merchants to incent merchants to participate in the merchant delivery service. When an eligible merchant becomes a participating merchant, the merchant delivery service provides a reward to the customers that recruited the merchant.

Customer driven activation of merchant delivery service areas

A merchant system including an order management application and method of integrating orders received from multiple ordering channels is described. An aggregated order queue is established including multiple orders from multiple different ordering channels. The orders are processed for fulfillment and a delivery channel is selected from multiple delivery channels for delivery of the one or more items of the order. The multiple orders, regardless of the ordering channel originating the order, and delivery channels may be managed via a single user interface.

Multi-channel order management

Management of persistent campaigns can be automated across multiple possible communication channels. A campaign management service monitors state information for the targeted users to ensure that the campaign content is provided to those users at appropriate times using appropriate channels. After a state change is detected for a particular user, corresponding to the desired action being taken, the campaign content will no longer provided for that user across any of those channels. The ability to manage state across all channels ensures that the providing of campaign content to any targeted users does not exceed frequency, occurrence, or other such criteria, and the types of actions can be any detectable actions performed with respect to an electronic environment.

Multi-channel persistent campaign management

This disclosure describes, in part, techniques and systems for creating personalized travel itineraries for users. The personalized itineraries may be created by analyzing prior travel preferences of a user to suggest various locations, activities, time frames, transportation, and/or lodging to the user. In some instance, the personalized itineraries may be created by comparing data associated with the user with data associated with a group of other users to determine a similarity between the user and other users within the group of other users. Where a similarity exists, an itinerary provider may create an itinerary for the user based on the travel preferences of the user and the prior behavior of the similar users.

Personalized travel itinerary planning

Customers may place orders for items for delivery, where merchants provide the items, and a service provider receives the orders and facilitates the delivery of the items. Upon receiving multiple orders, the orders may be prioritized based on users scores associated with users that placed the orders. An order objective for the orders may be determined and multiple fulfillment plans for each order may be generated. For a particular order, a fulfillment plan of the multiple fulfillment plans may be selected based on the order objective for that order, where the selected fulfillment plan may govern how and when the items included in the order are to be delivered to the user that placed the order. The service provider may cause the items associated with the order to be delivered to the user pursuant to the selected fulfillment plan.

System and methods for prioritization of items for delivery

A customer may specify, via a mobile device, a current location at which the customer desires to receive a delivery order for food items, as well as a preferred amount of time for the delivery. In response, food items that are available to be delivered to the current location within the preferred amount of time are identified and presented to the customer, including food items to be prepared/provided by merchants and food items current being transported by deliverers within sophisticated food item containers and/or delivery containers. Upon receiving a selection of one or more food items, a delivery of the ordered/selected food items may be facilitated. For instance, one or more instructions to prepare and/or transport the food item(s) may be transmitted to devices of merchants and/or deliverers.

Customer location-based delivery of food items

Various systems, methods and other embodiments are provided for generating offers for the purchase of products. In one embodiment, transaction data is stored in a data store. The transaction data comprises a record of a plurality of transactions for the purchase of products through a plurality of channels of commerce. Requests are received in a server from clients for an offer in association with an interaction involving a product. Each request includes a tag indicating at least one of the channels of commerce associated with the interaction. For each request, an offer is generated in response to the request, the offer being specific to the at least one of the channels of commerce.

Generating offers for the purchase of products

Implicit occasion personalization is described. A service provider may access user data associated with users corresponding to the user devices. The user data may represent user interactions with a plurality of entities that each offer one or more services. The service provider may determine a user pattern associated with a user of the plurality of users and may determine, from the user pattern, that the user is likely to request and/or receive the one or more services on a date. The service provider may generate a recommendation including at least one of a deal associated with the one or more services or a reservation on the date to the individual user on or before the date.

Implicit occasion personalization for restaurants

A food item container that contains a food item may include one or more sensors that collect sensor data indicating a characteristic (e.g., temperature, humidity, pressure, orientation, etc.) associated with the food item. A delivery container that transports the food items within the food item container may also include one or more sensors that collect the sensor data. Based on the sensor data, a component of the food item container and/or the delivery container may maintain or adjust the characteristic. The one or more sensors of the delivery container may also detect an identifier associated with the food item container, which may indicate which food item containers (and corresponding food items) are included within each delivery container.

Identification of items within delivery containers

Architectures and techniques are described related to identifying merchants associated with information obtained from a number of sources and storing portions of the information in data elements related to the merchants. The information may be provided in a structured format that enables the service provider to associate certain information with a particular merchant or in an unstructured format. The service provider may analyze the information received from the sources to determine whether the information includes any merchant identifying information. When the service provider identifies a merchant based on the merchant identifying information, the service provider may extract additional portions of the information received (e.g. merchant reviews, merchant attributes, etc.) and store those additional portions of information in the data element of the merchant. The service provider may utilize the information obtained about merchants for one or more applications, such as directory services, identifying affinities between merchants, and the like.

Automated merchant authority

A meal ordering service is configured to receive takeout and delivery orders from customers and to place the orders with selected restaurants. Past information regarding a customer is collected and analyzed to create a predictive model of the customer's meal ordering habits. On any given day, the predictive model may be used, in light of the current and scheduled status of the customer, to predict a time of day that the customer may want to receive a meal and to also determine a restaurant from which the customer may want to receive the meal. The meal ordering service may be configured to automatically order a meal for the customer from the determined restaurant so that the meal arrives at or before the predicted time of day.

Growing community of inventors

Seattle, WA, United States of America

Gustavo Eduardo Lopez