Abstract: Aspects of the disclosure provide for a method for updating business information. A business at a business location and a reference image for the business at the business location may be selected for an update. From a number of more recent images, a comparison image may be selected based on a likelihood that the comparison image captures the business location. Text and visual features reference image may be compared with text and visual features of the comparison image to determine a text similarity score and a features similarity score. A confidence level indicating whether the business in the reference image is in the comparison image may then be determined using the text similarity score and the feature similarity score. According to the confidence level, the business information of the business may be updated.

Abstract: Aspects of the disclosure provide for a method for updating business information. A business at a business location and a reference image for the business at the business location may be selected for an update. From a number of more recent images, a comparison image may be selected based on a likelihood that the comparison image captures the business location. Text and visual features reference image may be compared with text and visual features of the comparison image to determine a text similarity score and a features similarity score. A confidence level indicating whether the business in the reference image is in the comparison image may then be determined using the text similarity score and the feature similarity score. According to the confidence level, the business information of the business may be updated.

Abstract: Computer-implemented methods and systems for automatically classifying businesses from imagery can include providing one or more images of a location entity as input to a statistical model that can be applied to each image. A plurality of classification labels for the location entity in the one or more images can be generated and provided as an output of the statistical model. The plurality of classification labels can be generated by selecting from an ontology that identifies predetermined relationships between location entities and categories associated with corresponding classification labels at multiple levels of granularity. Confidence scores for the plurality of classification labels can be generated to indicate a likelihood level that each generated classification label is accurate for its corresponding location entity. Associations based on the classification labels generated for each image can be stored in a database and used to help retrieve relevant business information requested by a user.

Abstract: Aspects of the present disclosure relate to a method includes training a deep neural network using training images and data identifying one or more business storefront locations in the training images. The deep neural network outputs tight bounding boxes on each image. At the deep neural network, a first image may be received. The first image may be evaluated using the deep neural network. Bounding boxes may then be generated identifying business storefront locations in the first image.

Abstract: Aspects of the present disclosure relate to a method includes training a deep neural network using training images and data identifying one or more business storefront locations in the training images. The deep neural network outputs tight bounding boxes on each image. At the deep neural network, a first image may be received. The first image may be evaluated using the deep neural network. Bounding boxes may then be generated identifying business storefront locations in the first image.

Abstract: Computerized CAPTCHA systems using a direct connection with user computing devices are provided. An example computerized CAPTCHA system is configured to perform operations. The operations include receiving a request from a user computing device to engage in a verification process. The request is received independent of a resource provider from which the user computing device has requested a resource. The operations include providing a challenge to the user computing device at least in part in response to the request for engagement in the verification process and receiving a response to the challenge from the user computing device. The operations include determining whether the user computing device should be verified based at least in part on the response and providing a verification token to the user computing device when it is determined that the user computing device should be verified.

Abstract: A machine-implemented method for generating a social annotation is provided. The method includes receiving, at an electronic device, data from a passive sensor, wherein the data comprises an internet link associated with a remote server. The method also includes determining a geographical location of the electronic device, and transmitting the determined geographical location of the electronic device to the remote server associated with the internet link, so as to generate a social annotation for a physical object associated with the determined geographical location.

Abstract: Systems and methods for verifying a user based on reputational information are provided. In particular, a computerized CAPTCHA system consisting of one or more computers can determine a trust score based on one or more reputation signals associated with a user computing device, select a challenge to provide to the user computing device based on the trust score, and determine whether to verify the user computing device based on a received response to the challenge and/or the trust score.

Abstract: Systems and methods for updating geographic data based on a transaction are provided. In some aspects, one or more transaction records associated with a business are accessed from a memory. Each transaction record identifies a transaction time, geographic location data, and transaction information. A geocoded record of the business is selected to update, based on the geographic location data of the one or more transaction records. The selected geocoded record is updated based on at least one of the transaction time or the transaction information identified in the transaction records.

Abstract: A plurality of cells forming at least a portion of a hive of a data storage system may be capacity balanced by fragmenting a portion of at least one non-empty tile of one of the plurality of cells and moving the fragmented portion to another one of the plurality of cells. A plurality of cells forming at least a portion of a hive of a fixed content storage system may be capacity balanced by identifying at least one of the plurality of cells from which objects are to be moved, and for each of the at least one of the plurality of cells identified, determining a number of objects to be moved to another one of the plurality of cells, identifying one or more tiles that collectively have approximately the number of objects to be moved, and moving the one or more tiles to the another one of the plurality of cells.

Abstract: A plurality of cells forming at least a portion of a hive of a data storage system may be capacity balanced by fragmenting a portion of at least one non-empty tile of one of the plurality of cells and moving the fragmented portion to another one of the plurality of cells. A plurality of cells forming at least a portion of a hive of a fixed content storage system may be capacity balanced by identifying at least one of the plurality of cells from which objects are to be moved, and for each of the at least one of the plurality of cells identified, determining a number of objects to be moved to another one of the plurality of cells, identifying one or more tiles that collectively have approximately the number of objects to be moved, and moving the one or more tiles to the another one of the plurality of cells.