Framingham, MA, United States
Framingham, MA, United States

Staples, Inc. is a large office supply chain store, with over 2,000 stores worldwide in 26 countries. Based in Framingham, Massachusetts, United States, the company has retail stores serving customers under its original name in Australia, Austria, Brazil, China, Finland, France, Germany, India, Italy, Norway, Portugal, the United Kingdom, and the United States, while operating subsidiaries in Argentina as Officenet-Staples, in Netherlands as Staples Office Centre, in Canada as Staples Canada , and in Italy as Mondoffice. Staples also does business exclusively with enterprises in the United States and multiple European countries as Staples Advantage.Staples sells supplies, office machines, promotional products, furniture, technology, and business services both in stores and online. The company opened its first store in Brighton, Massachusetts on May 1, 1986. Wikipedia.


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Patent
Staples | Date: 2015-12-28

The disclosure describes technology for ranking search results based on customer intent. A set of matching product entries matching the one or more search keywords is determined from among product entries stored in a product database reflecting products purchasable via one or more online marketplaces. For each product entry, scores are computed using different combinations of coefficients and an amount of revenue-per-visit, a number of orders, and a ratio of page visits to product views associated with the product entry. The set of matching product entries are ranked based on a rank associated with each matching product entry in the set of matching product entries. The rank is computed based on a comparison between a plurality of scores associated with the matching product entry.


The disclosure relates in some cases to a technology for selecting one or more promotions to be presented to online customers using Bayesian bandits and affinity-based dynamic user clustering In some embodiments, a computer-implemented method determines a set of offers is determined, and computes affinity scores measuring affinities of users to items included in the offers. The method builds an affinity score distribution for the offers and identifies clusters of affinity scores for the offers using the corresponding affinity score distribution.


Patent
Staples | Date: 2016-04-13

Wipes dispensing systems are disclosed herein. The wipes dispensing system may include an inverted dockable wipes container with a wipes dispesing mechanism configured to rest upside-down on a dock. This wipes dispensing system allows excess cleaning solvent to be contained in the reservoir of the dock. The wipes dispensing system may include a wipes dispensing and cleaning system configured as a dockable wiping handle and a base for housing a stack of individual wipes. This wipes dispensing and cleaning system allows the user to selectively use a wipe without directly contacting the wipes or any solvent/cleaning agent via the users hands.


According to an example embodiment, a system is configured to determine a product group selectively grouping one or more of related products and related product classes; compute centroid values averaging customer preference values for the one or more of the related products and the related product classes of the product group; compute similarity scores between the product group and other product objects using the customer preference centroid values associated with the product group and customer preference values associated with the product objects; and select for recommendation one of the other product objects based on the similarity scores. The product objects including one or more of products and product classes from the product database.


In an example implementation, behavioral data describing past actions including products viewed and purchases made by users while using applications is compiled. The behavioral data is then segmented into clusters of behavior factors according to statistically related actions of the users. Present user data describing a current action of a user while using a merchant application is compiled. A comparative analysis that includes determining a match between the present user data and a cluster from the clusters of behavior factors is performed. A demand function is generated based on the match and the business rules associated with the merchant application. Targeted information is generated based on the comparative analysis. The targeted information includes a discount for the product in the virtual shopping cart. The targeted information including the discount for the product is provided to the user for presentation before the user leaves the merchant application.


A computer-implemented method and system are described for ordering items within a zone of a physical location. An example method may include wirelessly receiving at a server via a computer network a unique identifier associated with a physical ordering device, the physical ordering device being remotely located from the server, retrieving, at the server from an information source, item information describing an item configured to be associated with the unique identifier of the physical ordering device, retrieving, at the server from the information source, shipping data including a physical location uniquely associated with the physical ordering device, and generating at the server a purchase request for re-ordering the item. The method may further include processing at the server the purchase request, and authorizing at the server shipment of the item to the physical location associated with the physical ordering device responsive to the processing of the purchase request.


Technology for selecting promotion(s) to display in a page of an application for display to a user is described. An example method includes determining a promotion for a product; calculating for the promotion a posterior distribution of a user-action probability reflecting estimates for a user response to a display of the promotion for the product on a computing device of the user; determining the posterior distribution as collapsing beyond a certain threshold; responsive thereto, calculating an uncollapsed posterior distribution of the user-action probability reflecting modified estimates for the user response to the display of the promotion for the product on a computing device of the user; storing the uncollapsed posterior distribution of the user-action probability in a response database; and determining whether to select the promotion from the promotion database for display on a computing device of the user based on the modified estimates.


Patent
Staples | Date: 2015-01-28

A computer-implemented method and system are described for customizing content displayed to a user on a user device associated with the user. An example method may include receiving interaction data describing interactions by a user with one or more pages presented on a user device of the user, building a tag expression for the user based on the interaction data, the tag expression including a logical expression of tags and Boolean logic operators, and the tags being associated with page items. The method may also include generating a content page with a customized result customized to the user based on the tag expression.


Patent
Staples | Date: 2016-03-11

Technology for semantically processing user-submitted text and determining probabilities using computer learning model(s) is described. In an embodiment, a method, implemented using a computing device, may include receiving data including user-submitted product review(s) for a product. A product review includes review text and the method determines attributes of the product review text and feed the attributes of the product review text into a first hidden layer of an artificial neural network based on attribute type, feeding the first output of the first hidden layer of the neural network into a second hidden layer of the artificial neural network based on an association of the attributes of the product review with one or more of a story, a function, and a sentiment, and determining a predicted probability of recommendation of the review based on the second output of the second layer.


In an example embodiment, a method retrieves a customer product-class mapping that maps a customer identifier of each of a multiplicity of customers to 1) a customer tier, 2) a product class associated with an online, retail, and/or phone sales channel, and 3) a plurality of variables characterizing an interaction of the customer with the product class via the online, retail, and/or phone sales channel. The method generates a predictive score for each unique combination of the customer identifier, the customer tier, and the product class using predetermined online sales channel rules, predetermined retail sales channel rules, and/or predetermined phone sales channel rules, respectively, and the plurality of variables. The method selects a first set of customers from the multiplicity of the customers based on the predictive score and a revenue generated from the each unique combination and generates a schedule for contacting the first set of customers.

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