Abstract: A method and system for determining the time of delivery of an order is performed by a service system. The system receives the order from a customer device, and maps the customer telephone number to a routing telephone number. The system then sends the order with the routing telephone number to a provider, such as a restaurant. The provider prepares the order. A deliveryman calls the routing telephone number when he delivers the order to the customer. The system sets the reception time of the call as the time of delivery of the order. The system then forwards the call to the customer.

Abstract: Technologies for computing, within a real-time time interval, of a digital plan for a vehicle in response to receipt by an online system of a volume of short-route pickup-and-delivery requests that increases over an operational period, include: receiving, by a hybrid optimization model, a set of digital inputs; where the set of digital inputs includes (i) pairs of geo-location data associated with pickup-and-delivery requests and (ii) geo-location data associated with the vehicle and (iii) precedence data that indicate an order that geo-locations in a pair of geo-location data are to be visited; using the hybrid optimization model to generate, in an execution time that does not exceed the real-time time interval, a pickup and delivery plan that conforms to the precedence data as digital output in response to the set of digital inputs; where the hybrid optimization model includes a constraint programming-based solver that operates in coordination with at least one other solver; creating, digitally storing, tra

Abstract: A computer-implemented method comprises receiving training data corresponding to a plurality of trips, the training data including at least a value for a set of attributes for each of the plurality of trips, the set of attributes including an indication of when in a week a trip is taken or in which of a plurality of meal-based time zones in a day the trip is taken, the training data including an actual travel time for each of the plurality of trips. The method further comprises creating and storing, in computer memory, a digital model that is configured to predict a travel time for a future trip based on the training data, the digital model including a set of parameters corresponding to the set of attributes, the digital model including a plurality of sets of values for the set of parameters.

Abstract: The disclosed technologies can compute predictive reorder scores and measures of the quality of food products and food providers using objectively verifiable data, including reorder data, and use these predictive quality measures to create new functionality in an information search and retrieval system and/or a product ordering system.

Abstract: A restaurant service system for assessing the accuracy of estimated delivery time provided by a restaurant includes an order server, a restaurant server, a service server, and an assessment server. Each of the servers includes a server software application. The order server software application collects a set of orders from a set of diner devices. The restaurant server software application retrieves an estimated delivery time for each order in the set. The service server software application determines an order actual delivery time for at least one order in the set. The assessment server software application determines an accuracy measure of estimated delivery time for the restaurant.

Abstract: The disclosed technologies can compute inferred taste preferences relating to food products and food providers using objectively verifiable data, including order data, and use these inferred taste preferences to create new functionality in an information search and retrieval system and/or a product ordering system.

Abstract: A restaurant service system includes a restaurant server adapted to communicate with diner communication devices and restaurant communication devices. The restaurant server builds a food taxonomy including dishes, dish attributes and dish ingredients. The dishes are mapped from menu items. The server also trains dish classifiers and applies the dish classifiers to map menu items to dishes. Dish attributes are derived from search logs and dish ingredients are extracted from menu item descriptions. The server determines the search intent of a diner based on her search terms. Food search is conducted based on the taxonomy and the search intent with consideration of restaurant similarity and dish popularity. The server also provides dish recommendations based on taste similarity between diners.

Abstract: Systems and methods for using machine learning to estimate courier arrival times are described herein. According to an embodiment, a server computer receives a request to deliver to a requester prepared items associated with a preparation entity. The server additionally receives courier data identifying available couriers within the geographic region of the preparation entity and current request data identifying current requests to deliver prepared items from a plurality of entities in the geographic region. The server uses a trained machine learning system to compute an estimated pickup time, using at least the courier data and courier request data as inputs. The server computer then routes a preparation request to the preparation entity along with the estimated pickup time.

Abstract: The disclosed technologies can compute predictive reorder scores and measures of the quality of food products and food providers using objectively verifiable data, including reorder data, and use these predictive quality measures to create new functionality in an information search and retrieval system and/or a product ordering system.

Abstract: A method and system for determining the time of delivery of an order is performed by a service system. The system receives the order from a customer device, and maps the customer telephone number to a routing telephone number. The system then sends the order with the routing telephone number to a provider, such as a restaurant. The provider prepares the order. A deliveryman calls the routing telephone number when he delivers the order to the customer. The system sets the reception time of the call as the time of delivery of the order. The system then forwards the call to the customer.

Abstract: The disclosed technologies can compute inferred taste preferences relating to food products and food providers using objectively verifiable data, including order data, and use these inferred taste preferences to create new functionality in an information search and retrieval system and/or a product ordering system.

Abstract: A restaurant service system for assessing the accuracy of estimated delivery time provided by a restaurant includes an order server, a restaurant server, a service server and an assessment server. Each of the servers includes a server software application. The order server software application collects a set of orders from a set of diner devices. The restaurant server software application retrieves an estimated delivery time for each order in the set. The service server software application determines an order actual delivery time for at least one order in the set. The assessment server software application determines an accuracy measure of estimated delivery time for the restaurant.

Abstract: Techniques related to real-time distribution and adjustment of content placement are disclosed. A database stores score values respectively corresponding to content items. Computing device(s) are coupled to the database and to digital data storage device(s) storing instructions that cause the computing device(s) to perform a method for adjusting, at runtime, visibility of a particular content item. The method comprises computing a random value for the particular content item based on an identifier for a communication session between the computing device(s) and a client device. The method further comprises generating an adjusted score value for the particular content item based on combining the random value with a particular score value corresponding to the particular content item. Still further, the method comprises sending, to the client device via the communication session, the content items including the particular content item, which is sorted among the content items according to the adjusted score value.

Abstract: A restaurant service system includes a restaurant server adapted to communicate with diner communication devices and restaurant communication devices. The restaurant server builds a food taxonomy including dishes, dish attributes and dish ingredients. The dishes are mapped from menu items. The server also trains dish classifiers and applies the dish classifiers to map menu items to dishes. Dish attributes are derived from search logs and dish ingredients are extracted from menu item descriptions. The server determines the search intent of a diner based on her search terms. Food search is conducted based on the taxonomy and the search intent with consideration of restaurant similarity and dish popularity. The server also provides dish recommendations based on taste similarity between diners.

Abstract: The disclosed technologies can compute predictive reorder scores and measures of the quality of food products and food providers using objectively verifiable data, including reorder data, and use these predictive quality measures to create new functionality in an information search and retrieval system and/or a product ordering system.

Abstract: A utility for determining the competitors of a subject restaurant accesses a database that contains information about other, nearby restaurants. The utility assigns a competitor score to each restaurant within the maximum radius based on the distance from the subject restaurant, as well as the cuisine types, menus, and order histories of the subject and competitor restaurants.

Abstract: A restaurant service system includes a restaurant server adapted to communicate with diner communication devices and restaurant communication devices. The restaurant server builds a food taxonomy including dishes, dish attributes and dish ingredients. The dishes are mapped from menu items. The server also trains dish classifiers and applies the dish classifiers to map menu items to dishes. Dish attributes are derived from search logs and dish ingredients are extracted from menu item descriptions. The server determines the search intent of a diner based on her search terms. Food search is conducted based on the taxonomy and the search intent with consideration of restaurant similarity and dish popularity. The server also provides dish recommendations based on taste similarity between diners.

Abstract: A third-party server exposes a first set of data from a content based website. Access to a second set of data requires that a token be assigned to a specific web browser and corresponding to a specific subset of protected data, the token containing no information that can be used to gain access to other protected data.

Abstract: A customer service system operated by a restaurant service provides a restaurant service server. From a database, the restaurant service server retrieves a ticket that is generated by a restaurant service server application. The restaurant service server applies a diner credit policy to the ticket, and issues a diner credit for the diner associated with the ticket.