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: 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 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.

Abstract: A communication apparatus for connecting to a network that requires authentication is provided. The apparatus includes a network controller for connecting to the network; a controller for controlling a connection to the network via the network controller; a sensor for obtaining biometric information of a user of the communication apparatus; and a memory for storing a subscription module applied to authentication towards the network. The subscription module includes identification information created based on biometric information of the user. In order to establish a connection to the network by use of the subscription module stored in the memory, the controller obtains biometric information of the user by use of the sensor and compares the obtained biometric information to the identification information in the subscription module.

Abstract: A method is provided for determining a correlation between a reference user and another user on the basis of two sets of ratings, where each rating is associated with a respective user. In response to a trigger, user ratings associated with the reference user and user ratings associated with the other user are collected, and all co-rated items of these two sets are correlated in the basis of an adjusted cosine correlation function which is weighted by a first and a second weighting function. The correlation is then stored and may be repeated for a plurality of users. The stored correlations may be used e.g. for ranking purposes.

Abstract: In one embodiment, a method of generating annotation tags (28) for a digital image (22) includes maintaining a library (16) of human-meaningful words or phrases organized as category entries (72) according to a number of defined image description categories (70), and receiving context metadata (20) associated with the capture of a given digital image (22). The method further includes selecting particular category entries (72-1, 72-2) as vocabulary metadata (24) for the digital image (22) by mapping the context metadata (20) into the library (16), and generating annotation tags (28) for the digital image (22) by logically combining the vocabulary metadata (24) according to a defined set of deductive logic rules (30) that are predicated on the defined image description categories (70). In another embodiment, a processing apparatus (12), such as a digital processor (18, 26) and supporting memory (14), etc., is configured to carry out the above method, or to carry out variations of the above method.

Abstract: A method of estimating a default rating of a rated dataset is provided, where the dataset comprises at least one series of ratings associated with at least one user and each series comprise ratings associated with at least two items. For a reference user and an item for which a rated value is missing the item's average rating, ir, the reference users average rating, Ru, and the datasets average rating, dr, is collected. A Poisson distribution of the reference users rating is then generated on the basis of the reference users average rating. A random Poisson rating, ur, is calculated on the basis of the Poisson distribution, and a default rating, r, is estimated by weighting the random Poisson rating on the basis of the items average rating, the users average rating and the datasets average rating.

Abstract: In one embodiment, a method of generating annotation tags (28) for a digital image (22) includes maintaining a library (16) of human-meaningful words or phrases organized as category entries (72) according to a number of defined image description categories (70), and receiving context metadata (20) associated with the capture of a given digital image (22). The method further includes selecting particular category entries (72-1, 72-2) as vocabulary metadata (24) for the digital image (22) by mapping the context metadata (20) into the library (16), and generating annotation tags (28) for the digital image (22) by logically combining the vocabulary metadata (24) according to a defined set of deductive logic rules (30) that are predicated on the defined image description categories (70). In another embodiment, a processing apparatus (12), such as a digital processor (18, 26) and supporting memory (14), etc., is configured to carry out the above method, or to carry out variations of the above method.

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.