Abstract: A method and system operable for: receiving a search query including search terms; using a machine learning module, selecting features of the search terms and mapping an association between the search terms and a domain object, thereby generating a domain object classification; tagging the domain object with the domain object classification; and using the domain object tagged with the domain object classification to conduct a subsequent search. Conducting the subsequent search includes: receiving a subsequent search query including subsequent search terms; tokenizing the subsequent search terms; finding permutations of the tokenized subsequent search terms; matching the subsequent search terms to the domain object tagged with the domain object classification; and displaying subsequent search results via a user interface.

Abstract: A method and system operable for: receiving a search query including search terms; using a machine learning module, selecting features of the search terms and mapping an association between the search terms and a domain object, thereby generating a domain object classification; tagging the domain object with the domain object classification; and using the domain object tagged with the domain object classification to conduct a subsequent search. Conducting the subsequent search includes: receiving a subsequent search query including subsequent search terms; tokenizing the subsequent search terms; finding permutations of the tokenized subsequent search terms; matching the subsequent search terms to the domain object tagged with the domain object classification; and displaying subsequent search results via a user interface.

Abstract: The present disclosure provides systems, methods, and metrics that filter out online visitor behavioral data that represents a potential lead with a high likelihood to convert to a vehicle sale from online visitor behavioral data that does not represent a potential lead with a low likelihood to convert to a vehicle sale, based on a mapping of sales back to observed website and vehicle configurator data. This enables more effective lead generation and the more efficient targeting of online incentive offers and sales “nudges,” for example. Further, the present disclosure enables web analytics data to be combined with sales data for sales forecasting in general.

Abstract: A method and system operable for: receiving a search query including search terms; using a machine learning module, selecting features of the search terms and mapping an association between the search terms and a domain object, thereby generating a domain object classification; tagging the domain object with the domain object classification; and using the domain object tagged with the domain object classification to conduct a subsequent search. Conducting the subsequent search includes: receiving a subsequent search query including subsequent search terms; tokenizing the subsequent search terms; finding permutations of the tokenized subsequent search terms; matching the subsequent search terms to the domain object tagged with the domain object classification; and displaying subsequent search results via a user interface.

Abstract: A system and method for the classification of geographic locations based on vehicle trip logs, including: grouping a plurality of trip records by vehicle, wherein the plurality of trip records include start times and locations and stop times and locations; sorting the plurality of trip records for a vehicle in one of descending or ascending order with respect to stop times; calculating a plurality of time differences between consecutive trips of the plurality of trip records; storing the plurality of time differences in a stop time array; for a first plurality of time differences, storing the associated stop locations in a first location array; for a second plurality of stop times, storing the associated stop locations in a second location array; and computing a time difference median for each of the first location array and the second location array and returning the time difference medians as results for each location.

Abstract: The present disclosure provides systems, methods, and metrics that filter out online visitor behavioral data that represents a potential lead with a high likelihood to convert to a vehicle sale from online visitor behavioral data that does not represent a potential lead with a low likelihood to convert to a vehicle sale, based on a mapping of sales back to observed website and vehicle configurator data. This enables more effective lead generation and the more efficient targeting of online incentive offers and sales “nudges,” for example. Further, the present disclosure enables web analytics data to be combined with sales data for sales forecasting in general.

Abstract: A system and method for the classification of geographic locations based on vehicle trip logs, including: grouping a plurality of trip records by vehicle, wherein the plurality of trip records include start times and locations and stop times and locations; sorting the plurality of trip records for a vehicle in one of descending or ascending order with respect to stop times; calculating a plurality of time differences between consecutive trips of the plurality of trip records; storing the plurality of time differences in a stop time array; for a first plurality of time differences, storing the associated stop locations in a first location array; for a second plurality of stop times, storing the associated stop locations in a second location array; and computing a time difference median for each of the first location array and the second location array and returning the time difference medians as results for each location.

Abstract: A method and system operable for: receiving a search query including search terms; using a machine learning module, selecting features of the search terms and mapping an association between the search terms and a domain object, thereby generating a domain object classification; tagging the domain object with the domain object classification; and using the domain object tagged with the domain object classification to conduct a subsequent search. Conducting the subsequent search includes: receiving a subsequent search query including subsequent search terms; tokenizing the subsequent search terms; finding permutations of the tokenized subsequent search terms; matching the subsequent search terms to the domain object tagged with the domain object classification; and displaying subsequent search results via a user interface.

Abstract: A system and method for the classification of geographic locations based on vehicle trip logs, including: grouping a plurality of trip records by vehicle, wherein the plurality of trip records include start times and locations and stop times and locations; sorting the plurality of trip records for a vehicle in one of descending or ascending order with respect to stop times; calculating a plurality of time differences between consecutive trips of the plurality of trip records; storing the plurality of time differences in a stop time array; for a first plurality of time differences, storing the associated stop locations in a first location array; for a second plurality of stop times, storing the associated stop locations in a second location array; and computing a time difference median for each of the first location array and the second location array and returning the time difference medians as results for each location.

Abstract: A system and method for the classification of geographic locations based on vehicle trip logs, including: grouping a plurality of trip records by vehicle, wherein the plurality of trip records include start times and locations and stop times and locations; sorting the plurality of trip records for a vehicle in one of descending or ascending order with respect to stop times; calculating a plurality of time differences between consecutive trips of the plurality of trip records; storing the plurality of time differences in a stop time array; for a first plurality of time differences, storing the associated stop locations in a first location array; for a second plurality of stop times, storing the associated stop locations in a second location array; and computing a time difference median for each of the first location array and the second location array and returning the time difference medians as results for each location.

Abstract: Enriching trajectory data from a device may include determining, using a processor, enrichment data items related to points of presence within trajectory data. The enrichment data items may be retrieved from a data source. Enriching trajectory data may also include determining, using the processor, ranking metrics for the enrichment data items and ranking, using the processor, the enrichment data items according to the ranking metrics. One or more ranked enrichment data items, in association with the related point of presence of the trajectory data, may be provided to a device using the processor.

Abstract: A method, medium, and apparatus are disclosed for indexing multimedia content by a computer. The method comprises segmenting the multimedia content into a plurality of segments. For each segment, the method identifies one or more features present in the segment, wherein the features are of respective media types. The method then identifies, for each identified feature in each segment, one or more respective keywords associated the identified feature. Then, the method determines, for each identified keyword associated with an identified feature in a given segment, a respective relevance of the keyword to the given segment. The respective relevance is dependent on a weight associated with the respective media type of the identified feature.

Abstract: A method and client device is disclosed for indexing content of a multimedia file. The method comprises using a client device to segment the content of the multimedia file into a plurality of segments and to determine structure-searchable data for each segment. Determining structure searchable data for a segment comprises (1) identifying one or more features of respective multimedia types in the segment; (2) correlating each of the identified features to one or more respective keywords; and (3) calculating one or more respective relevance factors for each of the keywords, where at least one of the relevance factors is based on one or more characteristics of the client device. The method also comprises the client device transmitting the structure-searchable data (including the keywords, relevance factors, and respective media types of the identified features) to an indexing server.

Abstract: Method and a recommendation system (200) for making a media item recommendation to a user. A weighted graph is created (2:2) that comprises item nodes representing media items and edges linking the item nodes. Weights are also assigned to the edges based on weights of contributors shared by the linked item nodes. When an initial media item of interest to the user is identified (2:4), the recommendation system determines (2:5) a set of media items which are linked to the initial media item in a weighted graph. At least one of the determined set of media items is also selected based on the assigned weight of edges linking the media items to the initial media item. The recommendation system then provides (2:6) the media item recommendation with the at least one selected media item to the user.

Abstract: A method in an electronic device (100) for handling tags associated with an acquired digital image. The method includes displaying the image and the associated tags. The tags are have been assigned relative ranking values and are selected based on the relative ranking values. A selected tag is dissociated from the image by means of the electronic device detecting a user command instructing the device to dissociate a tag from the image.

Abstract: There is described a device and method for assigning a tag to a media file associated with metadata describing features of the media file. The metadata is converted into a query feature vector associated with the media file. A weighted distance is calculated between the query vector and each of a plurality of tag feature vectors, where each tag feature vector is included in a tag instance stored in a database, each tag instance further comprising a tag and a weight vector. The weighted distance is determined by comparing each feature of the query vector with the corresponding feature in the tag feature vector to obtain an initial distance value for that feature, modifying the initial distance value by a weight for that feature obtained from the weight vector in the tag instance to obtain a comparison value for that feature, and combining the comparison values of all of the features to obtain the weighted distance.

Abstract: Enriching trajectory data from a device may include determining, using a processor, enrichment data items related to points of presence within trajectory data. The enrichment data items may be retrieved from a data source. Enriching trajectory data may also include determining, using the processor, ranking metrics for the enrichment data items and ranking, using the processor, the enrichment data items according to the ranking metrics. One or more ranked enrichment data items, in association with the related point of presence of the trajectory data, may be provided to a device using the processor.

Abstract: The embodiments of the present invention relates to a method and system for classifying media. The classification is achieved by using annotation ontolgies and by associating bottom level concepts of the annotation ontology tree with explanatory representation data of a selected representation domain and then comparing the explanatory representation data with transformation of the media in the selected representation domain. In this way tags can be generated which corresponds to bottom level concepts of the ontology tree which corresponds to explanatory representation data which can be found in the transformed media.

Abstract: An apparatus 100 having a touch screen display 110 is disclosed. The apparatus 100 comprises a touch Apparatus screen display 110 configured to display a content object. The apparatus 100 also comprises a processor 120 as well as a memory 130 storing a computer program comprising computer program code. When the computer program code is run in the processor, this causes the apparatus 100 to detect a non-trigger touch interaction with the touch screen display 110, and to identify the non-trigger touch interaction as comprising a hovering interaction over a content object displayed on the touch screen display 110. In response to the identification of the non-trigger touch interaction comprising a hovering interaction, an Uniform Resource Locator, URL, address associated with the displayed content object is displayed on the touch screen display 110. Corresponding methods, computer programs and computer program products are also disclosed herein.

Abstract: A method of providing content (130) associated with a weight-value, the content (130) previously provided to a current computer associated with a current user (116) that is represented by a first node (106) in a social network (101). The method comprises the steps of: i) enabling the current computer (216) to display the content (130), in dependence of the weight-value, ii) obtaining an input of the current user (116), iii) updating the weight-value of the content (130), in dependence of the input of the current user (116), iv) determining a receiving computer associated with a second node (107) in the social network (101), and v) providing the content (130) to the receiving computer. Corresponding computers, computer program and computer readable medium are also described.