Abstract: Embodiments herein relate to a module which can be inserted into or removed from a computing device by a user. The module includes an input-output port which is configured for a desired specification, such as USB-A, USB-C, Thunderbolt, DisplayPort or HDMI. The port can be provided on an expansion card such as an M.2 card for communicating with a host platform. The host platform can communicate with different types of modules in a standardized way so that complexity and costs are reduced. In another aspect, with a dual port module, the host platform can concurrently send/receive power through one port and send/receive data from the other port.

Abstract: Described herein are systems and methods for designing and tailoring insurance solutions and advertising for users. The systems and methods utilize third-party websites and data retrieved therefrom, which is processed via machine learning components of the system. The systems and methods can utilize machine learning and artificial intelligence to generate content and tailor insurance solutions for users.

Abstract: A method for dependency graph-based word embeddings model generation includes the loading into memory of a computer of a corpus of text organized as a collection of sentences and the generation of a dependency tree for each word of each of the sentences. The method additionally includes the matrix factorization of each generated dependency tree so as to produce a corresponding word embedding for each word of each of the sentences without utilizing co-occurrence in order to create a word embeddings model. Finally, the method includes the storage of the model as a code book in the memory of the computer. The code book may then be used in producing a probability that a prospective term during textual analysis of a target document appears in the target document based upon a known presence of a different word in the target document and a relationship therebetween specified by the code book.

Abstract: Disclosed is a computer implemented method of bidding for advertisement opportunities based on user behavior data. The computer implemented method may include receiving a primary bid value associated with an audience list including a plurality of users. Further, each user of the plurality of users may be associated with user behavior data corresponding to a primary filtering criteria. Further, the computer implemented method may include receiving a secondary bid value associated with a sub-audience list including one or more users. Further, the one or more users may be associated with user behavior data corresponding to a secondary filtering criteria. Additionally, the computer implemented method may include transmitting a bid for an advertisement opportunity based on each of the primary bid value and the secondary bid value. The advertisement may be presentable to the one or more users.

Abstract: Disclosed is an online platform for predicting consumer interest level based on online behavior of consumers. The platform may be configured to monitor online activity of consumers across a plurality of webpages. As a result, the plurality of webpages visited by the consumer may be determined by the online platform. Further, the online platform may be configured to access and parse each webpage in the plurality of webpages in order to extract key elements. Furthermore, the platform may be configured to aggregate key elements extracted from each of the plurality of webpages and perform an analysis. Based on the analysis, the consumer may be determined to be in-market with regard to a product and/or a service. Further, based on the analysis, a confidence value representing a degree to which the consumer is in-market with respect to the product and/or the service may also be determined.

Abstract: Embodiments of the present invention provide for a method, system and computer program product for fast heterogeneous multi-data source search and analytics. In an embodiment of the invention, a method includes receiving a specification of multiple different data sources in a search and analytics engine, establishing communicative links between the engine and the data sources, and identifying a data source type and corresponding data fields storing respective data for each data source. The method further includes specifying a multi-hop graph traversal query implicating data across the different data sources, decomposing the query into constituent components and mapping each of the constituent components to each of the data sources based upon the corresponding data fields. The method even further includes formulating a specific query for each of the data sources, transmitting each query to each data source and populating in a knowledge graph each result set received for each query.

Abstract: Disclosed is a computer implemented method of bidding for advertisement opportunities based on user behavior data. The computer implemented method may include receiving a primary bid value associated with an audience list including a plurality of users. Further, each user of the plurality of users may be associated with user behavior data corresponding to a primary filtering criteria. Further, the computer implemented method may include receiving a secondary bid value associated with a sub-audience list including one or more users. Further, the one or more users may be associated with user behavior data corresponding to a secondary filtering criteria. Additionally, the computer implemented method may include transmitting a bid for an advertisement opportunity based on each of the primary bid value and the secondary bid value. The advertisement may be presentable to the one or more users.

Abstract: Disclosed is an online platform for predicting consumer interest level based on online behavior of consumers. The platform may be configured to monitor online activity of consumers across a plurality of webpages. As a result, the plurality of webpages visited by the consumer may be determined by the online platform. Further, the online platform may be configured to access and parse each webpage in the plurality of webpages in order to extract key elements. Furthermore, the platform may be configured to aggregate key elements extracted from each of the plurality of webpages and perform an analysis. Based on the analysis, the consumer may be determined to be in-market with regard to a product and/or a service. Further, based on the analysis, a confidence value representing a degree to which the consumer is in-market with respect to the product and/or the service may also be determined.