A method and system for matching an unknown facial image of an individual with an image of a celebrity using facial recognition techniques and human perception is disclosed herein. The invention provides a internet hosted system to find, compare, contrast and identify similar characteristics among two or more individuals using a digital camera, cellular telephone camera, wireless device for the purpose of returning information regarding similar faces to the user. The system features classification of unknown facial images from a variety of internet accessible sources, including mobile phones, wireless camera-enabled devices, images obtained from digital cameras or scanners that are uploaded from PCs, third-party applications and databases. Once classified, the matching person's name, image and associated meta-data is sent back to the user. The method and system uses human perception techniques to weight the feature vectors.

A method and system for determining access permissions to personalized clusters of multimedia content elements are provided. The method includes receiving a permission index designating at least the content description of at least one personalized cluster and an authentication factor; analyzing the content description; checking if there is at least one personalized cluster that matches the analyzed content description; and generating privacy metadata for each matching personalized cluster, wherein the privacy metadata includes at least the authentication factor associated with the respective content description matching the personalized cluster, wherein the generate privacy metadata determines access permission to the matching personalized cluster.

A method includes obtaining feature generation code from, which is configured to determine features relating to input data. The method further includes obtaining data grouping code, which is configured to generate training data by determining a plurality of data groupings for the features relating to the input data. The method further includes obtaining modeling code, which is derived at least in part by applying one or more machine learning algorithms to the training data. The method further includes applying a model wrapper code to the feature generation code, the data grouping code, and the modeling code to generate a model wrapper and deploying the model wrapper such that the model wrapper may receive a first application programming interface (API) call including an input data value, determine a score relating to the input data value, and send a second API call including the score in response to the first API call.

The present invention provides computerized systems and methods for providing electronically presented interactive area representation, such as a map, and information associated therewith. A user can select text, imagery, or other information presented on the map and associated with one or more items or locations, causing presentation of information relating to the associated one or more items or locations, such as appropriate contact information or a hyperlink to an appropriate Web site. Additionally or alternatively, a user can input or select, based on a query or otherwise, information relating to one or more items or locations associated with text, imagery, or other information presented on the map, causing presentation of an indication of one or more locations of the associated text, imagery, or other information on the map. A magnifier feature allowing internal navigation within the map can be provided. Additionally, animated images can appear to move over the map.

An e-commerce system is provided that tracks purchase transaction across multiple client devices. The e-commerce system stores hop information describing when a customer is exposed to a product of a vendor through an affiliate who advertises the vendor's products. The e-commerce system determines from the stored hop information which affiliate or affiliates to compensate for the sale of a product. This allows the e-commerce system to determine, after the sale, whether additional affiliates need to be compensated for the sale of the product based on the hop information.

A reproduction device including a reproduction unit configured to reproduce content data including at least one of audio, video, and text for which attribute data is assigned for each of a different plurality of attributes; a display unit; and a control unit configured to make the display unit display a playback screen containing at least one attribute data among the plurality of attribute data assigned to the content data when reproducing the content data and, when one attribute data among the attribute data composing the playback screen is selected, switch the display content of the content from the playback screen to the list relating to the selected attribute data.

A method includes obtaining feature generation code from, which is configured to determine features relating to input data. The method further includes obtaining data grouping code, which is configured to generate training data by determining a plurality of data groupings for the features relating to the input data. The method further includes obtaining modeling code, which is derived at least in part by applying one or more machine learning algorithms to the training data. The method further includes applying a model wrapper code to the feature generation code, the data grouping code, and the modeling code to generate a model wrapper and deploying the model wrapper such that the model wrapper may receive a first application programming interface (API) call including an input data value, determine a score relating to the input data value, and send a second API call including the score in response to the first API call.

A method includes obtaining feature generation code from, which is configured to determine features relating to input data. The method further includes obtaining data grouping code, which is configured to generate training data by determining a plurality of data groupings for the features relating to the input data. The method further includes obtaining modeling code, which is derived at least in part by applying one or more machine learning algorithms to the training data. The method further includes applying a model wrapper code to the feature generation code, the data grouping code, and the modeling code to generate a model wrapper and deploying the model wrapper such that the model wrapper may receive a first application programming interface (API) call including an input data value, determine a score relating to the input data value, and send a second API call including the score in response to the first API call.

A method for creating a multimedia data search engine platform to allow fast search of multimedia content data elements (MMDEs). The method comprises collecting MMDEs from at least an external source storing MMDEs; generating a plurality of signatures for each of the collected MMDEs; generating signature reduced clusters (SRCs) for the collected MMDEs by clustering the plurality of signatures generated for each of the collected MMDEs; and generating concept structures from the generated SRCs, wherein the concept structures generated for different SRCs are utilized to compare between different MMDEs, thereby searching for an input MMDE that matches the collected MMDEs.

A method includes obtaining feature generation code from, which is configured to determine features relating to input data. The method further includes obtaining data grouping code, which is configured to generate training data by determining a plurality of data groupings for the features relating to the input data. The method further includes obtaining modeling code, which is derived at least in part by applying one or more machine learning algorithms to the training data. The method further includes applying a model wrapper code to the feature generation code, the data grouping code, and the modeling code to generate a model wrapper and deploying the model wrapper such that the model wrapper may receive a first application programming interface (API) call including an input data value, determine a score relating to the input data value, and send a second API call including the score in response to the first API call.